We develop a simple model where the final output is produced using two technologies—one with diminishing returns and another with constant returns—and labor as the sole input. We show that the rate of decline in the share of agricultural employment is a sufficient statistic for the onset of economic transition from stagnation to sustained growth. Our quantitative results are consistent with the implications for the evolution of per capita income for economies in various stages of development and structural transformation.

This study examines the determinants of food security in a panel of eleven Arab countries over the period 2000–2023 using the cross-sectionally augmented autoregressive distributed lag (CS-ARDL) approach. The aim is to explore both short-run dynamics and long-run equilibrium relationships affecting food self-sufficiency. The results indicate that per capita income exerts a negative effect in the short run, reflecting changing consumption patterns and greater reliance on food imports, while agricultural production emerges as the most consistent positive driver of food self-sufficiency. Short-run dynamics also confirm a rapid adjustment process, with the error correction term (ECT = –1.3569, p < 0.01) indicating strong and more-than-complete convergence toward equilibrium. In the long run, agricultural value added is the only variable with a robust and significant influence, underscoring the strategic importance of the agricultural sector for sustainable food security. These findings highlight the structural vulnerability of food systems reliant on imports and call for policies aimed at enhancing agricultural productivity, promoting self-reliance, and reducing exposure to volatile international food markets.

The disparities in accessibility of public charging stations (PCSs) have emerged as a critical bottleneck constraining urban social equity and sustainable development. This study focuses on PCS accessibility within the central urban area of Tianjin. Employing an enhanced Gaussian-based two-step floating catchment area method, spatial autocorrelation analysis, and multi-scale geographically weighted regression, the research investigates spatial configuration, spatial dependence, and influencing factors from three analytical dimensions. The study elaborates on the disparities in PCS accessibility among different income groups and examines the determinants contributing to these variations. Research findings indicate that: (1) PCS accessibility1 in the central urban area exhibits a core-periphery spatial structure, with notable heterogeneity in the spatial distribution patterns of PCS accessibility across different income groups. (2) The MGWR model demonstrates superior performance compared to conventional models, with an increase in R2 explanatory power of at least 23.7% and a reduction in AICc values ranging from 3.94% to 47.45%. (3) Housing prices are positively correlated with PCS accessibility across different income groups, while per capita income and education and cultural services differentiated associations with PCS accessibility. From the perspective of different income groups, this study systematically analyzes the disparities in PCS accessibility and their associated factors, providing a theoretical foundation for sustainable urban planning and development.

Malta's economy has grown rapidly over the past decade, with per capita income nearly doubling since 2013. Growth averaged close to 7 percent annually, led by labor-intensive tourism, online gaming, and professional services, and was supported by inflows of foreign workers. Labor-driven growth supported by immigration, is expected to continue in the medium term but slow in the longer term as Malta, an island economy with the highest population density in the EU, cannot sustain further substantial population and labor force growth through immigration. Without adequate policy and structural reform measures, the inevitable deceleration in labor force growth would weigh on potential output and limit actual growth.

This study to analyze the effect of Non Performing Loan (NPL), Capital Adequacy Ratio (CAR), and Net Interest Margin (NIM) on Return on Assets (ROA) with Operating Expenses to Operating Income (BOPO) as an intervening variable at PT Bank Rakyat Indonesia (Perser) Tbk. This research employs a quantitative approach using secondary data obtained from the annual financial statements of Bank BRI for the period 2015-2024. The data analysis method used in this study is Structural Equation Modeling (SEM) based on Partial Least Squares (PLS), which allows the examination of both direct and indirect relationship among variables in the research model. The independent variables consist of NPL, CAR, and NIM, the intervening variable is BOPO and the dependent variable is ROA. The results indicate that NPL has a positive effect on BOPO, suggesting that higher credit risk leads to increased operational costs. CAR and NIM have a negative effect on BOPO, indicating that adequate capital and effective interest income management improve operational efficiency contributes to increased bank profitability. The findings also confirm that BOPO mediates the relationship between NPL, CAR, and NIM on ROA. This study is expected to contribute to the academic literature on banking financial management and provide practical insights for bank management in enhancing operational efficiency and sustainable profitability.

Zakāt has a worship dimension (a pillar of Islam) and a rights dimension (a right of the poor ordained by Allah). We focus on the economic dimension only by asking: how is zakāt different from both the capital income tax and the wealth tax? Economists discuss whether a capital income tax is optimal for welfare or a wealth tax is better. Traditional literature argued that the two are equivalent if the tax on wealth is set equal to the capitalised value of the tax on capital income. However, Guvenen et al. (2019, 2023) have forcefully shown that this equivalence holds only if the rate of return on capital is equal across individuals. When rates of return on investment are heterogeneous across individuals, the two tax systems have opposite implications for efficiency and inequality. A capital income tax burdens the more productive capital, whereas a wealth tax—by taxing all wealth holders—not only increases the tax base but reduces the wealth of low-productivity individuals. If the productivity differences are persistent, then the wealth of low-productivity individuals will be gradually pruned. In this sense, a wealth tax creates a use-it-or-leave-it effect. How is zakāt different from both taxes and what are its efficiency and distributional implications? We bring in some key macro-features of zakāt which is a tax on idle wealth, not on deployed (or employed) wealth. Aimed at promoting wealth circulation and helping the poor, zakāt helps address the wealth gap and income inequality in a different way than what is possible by either the capital income tax or the wealth tax. It has a much stronger “use-it-or-lose-it” effect than a wealth tax.

This research examines how tax planning, profitability, and capital structure influence corporate income tax, with operating costs playing a moderating role. The analysis centers on publicly traded manufacturing firms listed on the Indonesia Stock Exchange (IDX) from 2020 to 2023. Employing secondary data from audited statements and purposive sampling, the research analyzes 284 firm-year observations from 71 companies. The key variables are operationalized as follows: ETR for tax planning, ROA for profitability, DER for capital structure, and total SG&A expenses for operating costs. Analysis using a panel data Fixed Effects Model (FEM) with Moderated Regression Analysis (MRA) in EViews 13 reveals a positive and significant impact of profitability on tax obligations, with no significant effects found for tax planning or capital structure. Furthermore, operating costs strengthen the positive relationship between profitability and tax. Conversely, operating costs negatively and significantly moderate the effects of both tax planning and capital structure on corporate income tax. These findings highlight the critical role of operating cost efficiency in shaping how financial factors influence tax obligations. The study contributes to academic taxation literature and offers practical insights for firms in developing compliant tax strategies.

This study investigates the multifaceted impacts of two global megatrends—globalization and digitalization—on public welfare in the ASEAN and BRICS nations from 2007 to 2024. Amid the prevailing narrative that links both phenomena to economic progress, this thesis critically analyses the influence of proxy variables for globalization (trade openness and foreign capital inflows) and digitalization (internet penetration and social media usage), alongside crucial domestic factors such as corruption and entrepreneurship, on three primary welfare indicators: per capita income, unemployment rate, and the poverty level. The principal objective of this research is to deliver nuanced empirical evidence on how the interplay of external forces and internal conditions shapes the welfare landscape in two of the world's most dynamic economic blocs. Employing a quantitative approach through panel data regression analysis, the findings reveal complex and non- uniform relationships. The analysis indicates that trade openness and internet penetration significantly contribute to a reduction in the unemployment rate. Conversely, social media usage demonstrates a negative correlation with per capita income and a positive association with rising poverty, suggesting potential counter-productive impacts and the promotion of consumptive behaviours. Furthermore, domestic governance factors, particularly corruption, are proven to have a significant influence on impeding income growth and exacerbating employment conditions. These findings affirm that the benefits of globalization and digitalization are not automatic but are heavily contingent upon the quality of domestic institutions and policies capable of optimizing opportunities and mitigating emergent risks.

Abstract Nature loss poses a growing risk to the global economy, prompting calls for enhanced business accountability. To support the urgent business transformations required to achieve Nature Positive goals, decision‐makers, investors, lenders and other stakeholders need consistent, comparable and decision‐useful information on the state of nature and the benefits it provides. This paper presents a conceptual framework for natural capital accounting (NCA), adapting principles from financial accounting to support transparent reporting on business interactions with nature. We propose a set of structured statements— Natural Capital Balance Sheets , Natural Capital Income Statements, and Ecosystem Change Statements— that report on stocks of natural capital and associated ecosystem service flows. This approach is novel in its application of double‐entry financial accounting principles to ecological assets, liabilities and equity, offering a structured method for tracking organisational stewardship of nature. The framework builds on established financial reporting practices and aligns with global statistical standards such as the United Nations System of Environmental‐Economic Accounting (SEEA). A hypothetical example demonstrates how the framework can be applied to track changes in ecosystem condition and assess progress towards Nature Positive commitments. This work provides a foundation for mainstreaming NCA as a tool for ecological accountability. Further research is needed to operationalise the framework, refine ecological metrics and integrate NCA into governance and reporting systems. As global sustainability standards evolve, this framework offers a foundation for embedding nature into business reporting and decision‐making and aligning corporate performance with ecological sustainability.

High-resolution income data is crucial for informing policy decisions as it allows policymakers to better understand the distribution of wealth and poverty. However, obtaining this information is often cost-prohibitive, especially in developing countries. We evaluate the potential of using high-resolution satellite imagery and machine learning techniques to create income maps with a high level of geographic detail. We train a neural network with satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census data to estimate per capita income at a 50x50 meter resolution for 2013, 2018 and 2022. The model, based on the EfficientNetV2 architecture, demonstrates strong predictive accuracy for household incomes (R2 = 0.878), achieving a spatial resolution over 20 times finer than existing methods in the literature. The model also allows estimating income maps for arbitrary images, and can therefore be applied at any point in time. Our approach opens up new possibilities for generating highly detailed data, which can be used to assess public policies at a local level, target social programs more effectively, and address information gaps in areas where traditional data collection methods are lacking.

High-resolution income data is crucial for informing policy decisions as it allows policymakers to better understand the distribution of wealth and poverty. However, obtaining this information is often cost-prohibitive, especially in developing countries. We evaluate the potential of using high-resolution satellite imagery and machine learning techniques to create income maps with a high level of geographic detail. We train a neural network with satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census data to estimate per capita income at a 50x50 meter resolution for 2013, 2018 and 2022. The model, based on the EfficientNetV2 architecture, demonstrates strong predictive accuracy for household incomes (R2 = 0.878), achieving a spatial resolution over 20 times finer than existing methods in the literature. The model also allows estimating income maps for arbitrary images, and can therefore be applied at any point in time. Our approach opens up new possibilities for generating highly detailed data, which can be used to assess public policies at a local level, target social programs more effectively, and address information gaps in areas where traditional data collection methods are lacking.

Four decades of global cereal lead contamination patterns.

ABSTRACT While the substantial growth of e‐commerce has transformed air cargo from a peripheral service into a major revenue source in air transportation, airports, key nodes in this network, have increasingly benefited from this shift. The emergence of new players such as Amazon Air has significantly influenced the revenue growth trajectories of many airports. Despite this trend, the determinants guiding Amazon's airport selection strategy remain poorly understood. By analyzing data from 62 U.S. airports between 2010 and 2023, this study employs an Instrumental Variables Probit model to shed light on the key factors that influence Amazon's airport choices, thereby providing insights to help airports better align with the operational needs of major e‐commerce carriers. The results reveal that Amazon prefers to choose airports that are non‐hub, have good freight infrastructure, are far from urban centers, have low ground delays, and have a large scale of regional employment, while the per capita income has no significant level. These findings indicate the core logic of the “decentralized” layout of e‐commerce logistics: by avoiding the high cost and congestion of traditional hubs, relying on professional facilities and flexible scheduling to achieve efficient performance, which confirms its high sensitivity to operational efficiency and scheduling freedom.

Tanzania’s moderate GDP growth over the past decade has not effectively translated into sustained improvements in per capita income and productive employment, challenging the achievement of Sustainable Development Goal (SDG) 8. This study aims to evaluate Tanzania’s progress toward SDG 8 by examining the influence of GDP growth rate and employment-to-population ratio on sustained per capita GDP growth from 2014 to 2023. Using a quantitative time-series design, secondary macroeconomic data from reputable sources such as the World Bank, WDI, UNDP, and Tanzania National Bureau of Statistics were analyzed to ensure data validity and comprehensiveness. Econometric methods including Augmented Dickey-Fuller tests for stationarity, Johansen cointegration for long-run relationships, Vector Error Correction Models for dynamic analysis, and Ordinary Least Squares regression for impact assessment were applied to capture both short- and long-term effects. Results show an average GDP growth rate of 5.69%, employment ratio of 82.38%, and per capita GDP growth of 1.95%. A strong positive correlation between GDP growth and per capita income (r = 0.98, p < 0.01) was observed, while employment ratio showed a positive but statistically insignificant effect on GDP growth (β = 0.12, p = 0.165). The study concludes that improving per capita income is essential for sustained economic growth. It recommends prioritizing labor market formalization, investing in human capital, promoting economic diversification, supporting structural reforms and innovation, and strengthening data collection and monitoring systems to accelerate Tanzania’s progress toward SDG 8.1.

ABSTRACT Building on a theoretical framework, we examine how fertility behavior affects household risky financial asset allocation using microdata from the 2016, 2018, and 2020 waves of the China Family Panel Studies (CFPS). Fertility behavior is characterized along two sequential dimensions: the decision to have children and the decision to continue childbearing. We find that: (1) Choosing to have children significantly increases households’ participation in risky financial markets, whereas continuing to have children significantly reduces both the probability of participation and the share of risky financial assets held. These findings remain robust across a series of robustness checks. (2) The effects exhibit substantial heterogeneity across urban-rural areas, intergenerational support arrangements, housing liabilities, and household characteristics, including education, asset holdings, and child gender composition. (3) Human capital investment and the income penalty serve as key mediating mechanisms. Specifically, choosing to have children increases household investment in education and healthcare and encourages risk-taking, while continuing childbearing reduces per capita income and constrains participation in risky financial assets. (4) Birth spacing emerges as an important temporal factor: longer intervals between successive births alleviate the financial pressures associated with childbearing and promote greater risk-taking in household portfolios.

This study reexamines Indonesia’s development paradigm with a focus on achieving sustainable welfare. Employing a dynamic panel data approach, the analysis investigates the effects of social, economic, and environmental dimensions on sustainable welfare proxied by the Gini ratio across 34 provinces over the 2019–2024 period. To address endogeneity concerns and capture dynamic relationships, the Generalized Method of Moments (GMM) is applied. The estimation results from the FDGMM, SYSGMM, and DPGMM models satisfy the criteria of validity, consistency, and unbiasedness. Overall, the findings indicate that the explanatory variables in all three models exert a strong, negative, and statistically significant effect on inequality reduction, thereby enhancing sustainable welfare. However, per capita income exhibits a positive yet statistically insignificant impact on welfare in both the short and long run. Based on these findings, the study recommends a reformulation of Indonesia’s development paradigm toward sustainable welfare by positioning the Human Development Index (HDI) as the core foundation of development and structural reform. The results underscore the need for policy innovation that prioritizes HDI-based development, accelerates structural transformation, and promotes green job creation. Ultimately, progressive, inclusive, and consistent policy design is essential for achieving sustainable wellbeing, with a strong emphasis on equity and long-term sustainability

P1006 Health Economics Analysis of IBD at a tertiary centre in India shows larger proportion of non-urban patients and significant discrepancy in affordability of advanced therapies

ABSTRACT Ghana faces a persistent infrastructure financing gap, prompting the exploration of alternative funding models such as Public–Private Partnerships (PPPs), green bonds, and blended finance instruments. This study uses macroeconomic and sectoral indicators as proxies to evaluate the long‐term impact of these models on economic performance in Ghana. Using quarterly time‐series data from 2003 to 2023, the paper applies a Dynamic Ordinary Least Squares (DOLS) model to estimate the long‐run relationship between real GDP and selected infrastructure‐related indicators. The results reveal that per capita income, employment, and road infrastructure have a significant and positive impact on economic performance, while educational attainment and access to electricity exhibit unexpected negative effects. These results highlight that it is critical to prioritize high‐impact sectors that are often addressed with alternative infrastructure funding models and to streamline delivery, especially in social infrastructure. This study contributes to the growing discourse on infrastructure finance in Sub‐Saharan Africa and provides implicit policy lessons that can be applied to the strategic application of sustainable non‐traditional funding mechanisms in Ghana.

Abstract Following Entman’s observation that policy frames define social problems, diagnose causes and suggest remedies, we examined the strategies that 12 U.S. governors (from states matched according to population size and density, demographic composition, per capita incomes, geographic proximity, and COVID-19 incidence) used to frame COVID-19 policy agendas. After scraping the governors’ statements about COVID-19 from press releases issued from January 2020 to May 2023 (N = 14,629), we leveraged ChatGPT (GPT) to identify and assess the intensity of public health, economic stability, and civic vitality frames. Subsequent analysis explored differences in the framing strategies according to the governors’ political party and gender. In the process, this study underscores the importance of AI prompt engineering to realize GPT’s transformative potential to facilitate communication research by efficiently identifying and assessing the content of policy frames.

This study investigates the relationship between key economic sectors, capital formation, and per capita income in Nepal. Focusing on the period from 1975 to 2023, it has two central objectives: (ii) to show the empirical relationship between gross capital formation, primary and tertiary sectors on real per capita output, and (iii) to explore the short-run and long-run relationship between these variables. Using time-series data and employing the Johansen cointegration test and the Vector Error Correction Model (VECM), the analysis confirms a significant long-run relationship. The results reveal statistically significant impacts of the primary sector, the tertiary sector, and gross capital formation on real per capita income in both the short and long run. The Error Correction Term was -0.28 and significant, indicating a 28% annual adjustment towards long-run equilibrium. The study concludes that primary and tertiary sector outputs, along with capital formation, are fundamental to Nepal's per capita income, with significant implications for economic policy.