Search Results (33)

FinTech-based lending has rapidly expanded in emerging economies, offering convenience and inclusion but also raising concerns about over-indebtedness. In Indonesia, the surge of digital loans has been accompanied by growing signs of risky borrowing behavior, including late payments, high debt-to-income ratios, and poor credit discipline. This study investigates the determinants of individuals’ propensity to indebtedness in FinTech-based loans, focusing on the influence of financial behavior biases, emotions, culture, and materialism, as well as the moderating effects of financial literacy, job security, and religiosity. Data were collected from 400 Indonesian civil servants and private/self-employed workers through an online questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results show that all proposed determinants significantly increase indebtedness, with financial behavior biases having the strongest impact. Financial literacy and job security amplify these effects, while religiosity weakens the influence of emotions and materialism. These findings contribute to behavioral finance theory and underscore the importance of promoting financial literacy, strengthening job stability, and integrating responsible lending policies to mitigate debt risks in emerging economies. Full article

In the context of increasing digitization, integrating ICT technologies, artificial intelligence, and remote working is altering residential mobility patterns and housing preferences. This study examines the housing market’s impact, focusing on how residential affordability affects residential choices, using a case study of the Metropolitan City of Florence. The analysis employs a methodology centered on the Debt-to-Income Ratio (DTI), which cross-references real estate market values (source: Agenzia delle Entrate and leading real estate portals) with household income brackets to identify affordable areas. The results reveal a clear divide: households with incomes below EUR 26,000 per year (representing about 69% of the population) are excluded from the central urban property market. This evidence confirms regional and national trends, emphasizing a growing mismatch between housing costs and disposable incomes. The study concludes that affordability is a technical–financial parameter and a valuable tool for supporting inclusive urban planning. Its application facilitates the orientation of effective public policies and the identification of socially sustainable housing solutions. Full article

Hyperparameter optimization (HPO) is critical for enhancing the predictive performance of machine learning models in credit risk assessment for peer-to-peer (P2P) lending. This study evaluates four HPO methods, Grid Search, Random Search, Hyperopt, and Optuna, across four models, Logistic Regression, Random Forest, XGBoost, and LightGBM, using three real-world datasets (Lending Club, Australia, Taiwan). We assess predictive accuracy (AUC, Sensitivity, Specificity, G-Mean), computational efficiency, robustness, and interpretability. LightGBM achieves the highest AUC (e.g., $70.77\%$ on Lending Club, $93.25\%$ on Australia, $77.85\%$ on Taiwan), with XGBoost performing comparably. Bayesian methods (Hyperopt, Optuna) match or approach Grid Search’s accuracy while reducing runtime by up to $75.7$-fold (e.g., $3.19$ vs. $241.47$ min for LightGBM on Lending Club). A sensitivity analysis confirms robust hyperparameter configurations, with AUC variations typically below $0.4\%$ under $\pm 10\%$ perturbations. A feature importance analysis, using gain and SHAP metrics, identifies debt-to-income ratio and employment title as key default predictors, with stable rankings (Spearman correlation $> 0.95, p<0.01$) across tuning methods, enhancing model interpretability. Operational impact depends on data quality, scalable infrastructure, fairness audits for features like employment title, and stakeholder collaboration to ensure compliance with regulations like the EU AI Act and U.S. Equal Credit Opportunity Act. These findings advocate Bayesian HPO and ensemble models in P2P lending, offering scalable, transparent, and fair solutions for default prediction, with future research suggested to explore advanced resampling, cost-sensitive metrics, and feature interactions. Full article

This study examines the relationship between student loan debt and civic engagement among college graduates, with particular attention to differential effects by field of study. Drawing on data from the College and Beyond II dataset, this research analyzes how varying levels of debt burden impact political and community participation among 1673 graduates, including 1059 liberal arts majors and 614 graduates from professional, STEM, and other fields. Employing OLS regression models with multiple measures of debt burden and distinct dimensions of civic engagement, this study finds that both medium and high levels of student loan debt (USD 201–500 monthly and >USD 500 monthly) are associated with significantly higher political engagement—a pattern that aligns with relative deprivation theory’s proposition that financial strain may motivate political action aimed at systemic change. This relationship is particularly pronounced among liberal arts graduates, who demonstrate stronger positive associations between debt and civic participation than their peers from other fields. Debt-to-income ratio analysis reveals a potential “sweet spot” at 10–15% of income, where debt appears to optimize civic engagement without overwhelming resources. These findings suggest that liberal arts education may fundamentally alter how graduates respond to financial constraints, potentially by providing analytical frameworks for understanding debt as a systemic issue and civic skills that facilitate participation despite economic pressures. The results challenge assumptions about debt’s uniformly negative civic consequences and highlight the importance of educational context in mediating economic effects on democratic participation. Full article

We build on the literature of consumer debt–income inequality nexus, by developing a post-Keynesian model of growth and income distribution that incorporates both consumer and corporate borrowing. Specifically, we examine a non-linear dynamic system of three differential equations of workers’ debt-to-capital ratio, corporate debt-to-capital ratio, and the accumulation rate. We conduct simulations to solve the system for the long-run equilibrium points and examine local stability using the Routh–Hurwitz conditions. Additionally, we conduct a comparative statics analysis and investigate the stability of the model using a measure of the maximum distance among the three equilibrium points. Our key findings suggest that although our model shares several quantitative and qualitative aspects with the 2 × 2 models, the inclusion of corporate debt alters the impact of parameter changes on macroeconomic stability. This incorporation increases the number of parameters that have differing effects on stability across various scenarios. The ratio of external-to-internal borrowing, along with the interest rate, is among the few parameters that consistently undermines macroeconomic stability across all scenarios. Full article

This article examines the relationship between environmental income dependence and household vulnerability in rural settings. Using household-level livelihood data from the Poverty Environment Network (PEN) dataset of Nepal, we construct a household vulnerability index and analyze its relationship with environmental dependence, measured as the share of environmental income in total income, while controlling for other variables. The findings reveal that higher environmental dependence significantly increases household vulnerability. In contrast, household debt helps mitigate vulnerability by providing financial support and enabling productive investments. However, high dependency ratios and exposure to shocks exacerbate vulnerability by limiting income generation and destabilizing livelihoods. Policy measures such as promoting economic diversification and social safety net programs could reduce environmental dependence and mitigate household vulnerability in rural Nepal. Furthermore, providing timely access to credit during hardships and addressing unforeseen shocks could enhance household resilience. Full article

Education is considered an investment in human capital that is gained at the cost of knowledge acquisition. This cost is borne by the beneficiary along with subsidy provided by the government, if any, that is mainly collected through tax revenues. This article aims to systematically review the utility derived and the burden experienced with educational debt borrowers across the globe as per the three types of educational loan schemes present across the globe. This study follows the PRISMA guidelines for review selection, and 47 articles published between 1994 and 2024 were included for the final review. The study results reveal that education improves the quality of life; an educational debt servicing to income ratio above 8% is considered as a financial burden. Also, the results reveal that material benefits are high after education along with an increase in the psychological burden because of repayment concerns. This study highlights the need to move towards designing a flexible repayment system in the education loan scheme based on the income contingent schemes adopted in many countries. Income contingent schemes reduce the repayment burden of the borrowers but the return to the lender is limited to the income of the borrower, and mortgage-based schemes are associated with high repayment burden. Therefore, a dynamic scheme will fix the problems associated with the repayment burden by creating a dynamic link between the benefits received and the contributions made by the borrower. Full article

This study used the panel data from 15 emerging markets to examine the impact of restrictive macroprudential policies on income inequality from 2000–2019 using Bayesian panel vector autoregression and Bayesian panel dynamics generalised method of moments models. The chosen models are suitable for addressing multiple entity dynamics, accommodating a wide range of variables, handling dense parameterisation, and optimising formativeness and heterogeneous individual-specific factors. The empirical analysis utilised various macroprudential policy proxies and income inequality measures. The results show that when the central banks tighten systems using macroprudential policy instruments to sticker debt-to-income and financial instruments for lower-income borrowers (the bottom 40% of the income distribution), they promote income inequality in these countries while reducing income inequality for high-income borrowers (the high 1 percent of the income distribution). The impact of loan-to-value ratios was found to be insignificant in these countries. Fiscal policy through government expenditure and economic development reduces income inequality, while money supply and oil-price shocks exacerbate it. The study suggests implementing a progressive debt-to-income (DTI) ratio system in emerging markets to address income inequality among lower-income borrowers. This would adjust DTI thresholds based on income brackets, allowing lenient credit access for lower-income borrowers while maintaining stricter limits for higher-income borrowers. This would improve financial stability and reduce income disparities. Additionally, targeted financial literacy programs and a petroleum-linked basic income program could be implemented to distribute oil revenue to lower-income households. A monetary supply stabilisation fund could also be established to maintain financial stability and prevent excessive inflation. Full article

Maximising financial performance while maintaining adequate liquidity is a crucial and ongoing challenge for bank management, particularly in emerging markets. This study focuses on the relationship between capital structure and financial performance in Jordanian banks, with the mediating role of liquidity risk. Using panel data from 13 central Jordanian banks over the 2015–2022 period, we employ structural equation modelling (SEM) to analyse how capital structure ratios (equity-to-asset, debt-to-loan, and deposit-to-asset) influence financial performance metrics (return on assets and net income-to-expenditure ratio). Our findings reveal a significant positive association between capital structure and financial performance. However, liquidity risk fully mediates this effect. Capital structure primarily impacts performance by influencing a bank’s liquidity risk profile. Furthermore, the strength of this mediating effect is noteworthy—capital structure exhibits a statistically more robust association with liquidity risk than its direct impact on performance. This highlights the crucial role of managing liquidity risk within the complex dynamics of bank operations. This research makes a significant contribution to the existing literature by demonstrating the positive impact of capital structure on performance using the underlying mechanism through which this effect occurs. The insights of this research provide several implications for practice in the context of banking industries. Full article

The aim of this study is to analyze which factors affect the profitability of banks in the eurozone and to make recommendations for supporting them to achieve higher levels of profitability in particular eurozone countries. The banks operating in the eurozone are specific that they are under one monetary policy. The main purpose of the banks’ profitability analysis is to identify main bank-specific and macroeconomic determinants and help bank management to more fully comprehend their importance of bank-specific determinants and macroeconomic determinants’ influence when measuring and evaluating bank profitability. For the purpose of this research, we analyze the impact of bank-specific determinants (NPL, CIR, NIM, NIF and NIT) and macroeconomic determinants (GDP, INF, UNM and DEBT) on bank profitability in the eurozone for the period of 2015–2020 using a random effects model, fixed effects model, and the general method of moments (GMM). This empirical research analyzed quarterly data series from Eurostat for eighteen countries in the eurozone. We came to the results that on the eurozone-level NPL, the cost-to-income ratio has a negative impact on the banks’ profitability, while the net interest income to the operating income, the net income for trading assets to the operating income and the net fee and commission income to the operating income have a positive impact on the banks’ profitability. Considering the macroeconomic variables, we found a positive impact only in the case of GDP, while the inflation rate, unemployment rate and gross government debt have shown a negative impact on the banks’ profitability. The main contribution of this study implies different panel techniques with two uncommonly used macroeconomic variables such as the unemployment rate and debt ratio. The results on the country level differ from country to country and these findings can give a lead to policy makers on the national level on how to enhance the banks’ profitability levels. Full article

This paper analyses the relationship between household debt and income inequality in South Africa for the period 1980–2021. We use two measures of inequality and estimate a vector error correction model (VECM) which includes household debt, inequality, and other macroeconomic variables. To test the robustness of our results, single equation models are used, which estimate household debt as a function of inequality and macroeconomic factors. We employ two measures of inequality, namely Gini coefficient and ratio of top and bottom income earners’ proportion of income. Furthermore, we use both household debt as a percentage of disposable income and household debt service costs as dependent variables in single equation regressions. The study finds a negative and significant relationship between household debt and income inequality in the long run, which contradicts the Rajan hypothesis in the South African case. Rather, we find that inequality in South Africa creates a bias in debt allocation towards high-income earners, whose incomes can easily absorb the extra debt (reduced ratio of debt to disposable income). There are therefore no socio-equity considerations in South African credit markets. We find growth in gross domestic product (GDP) per capita also has a moderating effect on the relationship between household debt and income inequality. High GDP per capita growth in the presence of high inequality reduces the impact of inequality on household debt and vice-versa. All other control variables take expected signs. These results are robust to changes in the inequality or household debt measures. Full article

Although low-income homeownership programs serving vulnerable families at the lower end of the income distribution have been the focus of housing policy in many countries over the past 50 years, little is known about the post-origination experiences of immigrant families participating in these programs. Notably absent from the extant literature are studies examining the sustainability of homeownership among immigrant homebuyers and their susceptibility to falling behind on payments and experiencing mortgage defaults, evictions, or short sales. Utilizing data from 8263 families participating in Norway’s Starter Mortgage Program (Startlån) during the first three calendar years after mortgage origination, we examine the extent to which serious mortgage arrears varies by immigrant background. Two primary questions shape our research: (1) What is the incidence of serious mortgage arrears among Western, Eastern European, and non-Western immigrant homeowners relative to ethnic Norwegians participating in a public low-income homeownership program? and (2) What pre- and post-origination characteristics of applicants and households, mortgage terms at the time of origination, and experiences of household financial vulnerability or economic shocks predict heterogeneity in serious mortgage arrears by immigrant backgrounds? We found that 6.1% of ethnic Norwegian, 6.2% of Western, 4.9% of non-Western, and 3.2% of Eastern European immigrant homeowners participating in the Starter Mortgage Program were in serious mortgage arrears at least once during the first three calendar years after mortgage origination. Results from our negative binomial regression analyses suggest that program participants who were sole owners, with larger families, and higher debt were more likely to experience serious mortgage arrears; these effects were accentuated for ethnic Norwegians. Additionally, mortgage terms at the time of origination produced differential effects by immigrant background. Compared to Western and Eastern European immigrant homeowners, ethnic Norwegians were more likely to have experienced serious mortgage arrears if they purchased a single-family home, had larger LTV and DTI ratios, or if the Startlån share of their mortgages was higher. Non-Western immigrant mortgagors were more likely to make late mortgage payments if they had larger LTV ratios, interest-only mortgage servicing, or if they were more reliant on Startlån funds to finance their mortgages; however, this risk was reduced if they had fixed-rate mortgages. Financial vulnerability in terms of higher debt or fewer assets also increased the risk of serious mortgage arrears for ethnic Norwegians and non-Western immigrant homeowners, while increases in real wealth reduced that risk for all immigrant mortgagor groups. Full article

Agri-food companies play an economic, social, and environmental role. In Italy, the typical products protected by the European Union with the PDO and PGI marks are spread throughout the national territory, even in disadvantaged ones, and generate turnover, employment, and positive effects in related industries. These companies are often of medium or small size (SMEs) and are financially constrained. The research applies the assessment of financial sustainability to the balance sheet statement (BSS), financial ratios (FRs), interest coverage ratios (ICRs), duration of the cash conversion cycle (CCC), and net working capital (NWC) data. It shows that companies in the sector have high capital intensity in both the fixed asset (FA) and NWC cycles. Profitability is largely eroded by the cost of debt (COD). Financial sustainability is calculated through the following: (1) the duration of the cash conversion cycle (CCC), broken down into the cycle lengths of inventory, receivables, and payables; (2) the calculation of the income and financial margins generated by the management and their correlation; (3) the calculation of financial ratios (FRs) and the verification of financial leverage (ROA > ROD); (4) the calculation of income and financial interest coverage ratios (ICRs) and tests on the significant differences between them. The application of ICRs with the financial methodology applied in the research makes it possible to (1) improve the assessment of financial sustainability and reduce the related risk and (2) reduce the information asymmetry between the company and the bank, facilitating access to credit. The duration of the CCC is negatively correlated to profitability (ROE), while size and economic performance are positively correlated. The ICRs (interest coverage ratio) calculated with the financial approach are statistically different from the ones calculated with the economic one. The application of the result is relevant to industry operators and for future research. The research is replicable; the results can be tested on other sectors of the agri-food sector and disseminated to operators. Full article

Household leverage ratio is an important factor affecting family stability. Digital finance has changed the means of payment and consumption frequency, but the relationship between digital finance and household leverage ratio is still unclear. The existence of household debt is defined as the existence of leverage. The higher the household debt, the greater the household leverage. Based on the matching data of the China Household Finance Survey (CHFS) 2019 and the China Digital Inclusive Finance Index, this paper studies the impact of digital finance on household leverage ratio and explores its mechanism theoretically and empirically. This research finds that digital finance can significantly promote the household leverage ratio and this conclusion is still valid after instrumental variable method and robustness test. The mechanism analysis shows that digital finance can promote household over-consumption and further expand household leverage ratio. Digital finance can also reduce household leverage ratio by increasing household income. The heterogeneity analysis suggests that the role of digital finance in expanding leverage ratio is stronger for urban areas and households with low educational level. For households with higher assets, digital finance helps to reduce leverage ratio. Therefore, the government should guide residents to consume rationally and give full play to the entrepreneurship-facilitating and income-increasing effect of digital finance. Meanwhile, the residents themselves should speed up the cultivation of digital financial literacy, which is of vital significance for lowering household leverage ratio and systemic financial risks. China’s development level of digital finance ranks among the top in the world. Studying the role of digital finance in China is helpful to provide experience reference for countries around the world. Full article

This paper aimed to develop a useful Machine Learning (ML) model for detecting companies with lasting competitive advantages (companies’ moats) according to their financial ratios in order to improve the performance of investment portfolios. First, we computed the financial ratios of companies belonging to the S&P 500. Subsequently, we assessed the stocks’ moats according to an evaluation defined between 0 and 5 for each financial ratio. The sum of all the ratios provided a score between 0 and 100 to classify the companies as wide, narrow or null moats. Finally, several ML models were applied for classification to obtain an efficient, faster and less expensive method to select companies with lasting competitive advantages. The main findings are: (1) the model with the highest precision is the Random Forest; and (2) the most important financial ratios for detecting competitive advantages are a long-term debt-to-net income, Depreciation and Amortization (D&A)-to-gross profit, interest expense-to-Earnings Before Interest and Taxes (EBIT), and Earnings Per Share (EPS) trend. This research provides a new combination of ML tools and information that can improve the performance of investment portfolios; to the authors’ knowledge, this has not been done before. The algorithm developed in this paper has a limitation in the calculation of the stocks’ moats since it does not consider its cost, price-to-earnings ratio (PE), or valuation. Due to this limitation, this algorithm does not represent a strategy for short-term or intraday trading. Full article