Search Results (47)

Misclassification of binary outcomes in network settings may bias the estimates of causal effects, including spillover effects that arise from social interactions, and may generate spurious causal effects. To address this issue, we develop a parametric framework that jointly estimates misclassification probabilities and causal effect parameters within a binary choice model with neighborhood exposure mappings. Monte Carlo simulations show that ignoring outcome misclassification or network-related variables leads to substantial bias, whereas the proposed method achieves a smaller bias and RMSE. By applying the method to microfinance and social network data from Karnataka, we find that under binary exposure, ignoring outcome misclassification yields statistically significant spillover and overall effects, whereas these effects become statistically insignificant once outcome misclassification is corrected for. Furthermore, omitting network-related variables overstates the direct effect. These results underscore the importance of jointly correcting for outcome misclassification and accounting for network-related variables to obtain credible causal inference. Full article

This study investigates directional causality between Bitcoin and gold across different market conditions. Rather than relying on mean-based dependence, we examine how causal effects vary across return quantiles, investment horizons, and market regimes. To address this question, we apply a Causal–Frequency–Quantile–Regime (CFQR) framework. The approach combines frequency-domain Granger causality, quantile-based non-causality tests, and endogenous regime classification within a unified setting. Macroeconomic controls are included to reduce omitted variable bias. Statistical inference relies on bootstrap procedures with false discovery rate correction to account for multiple testing. Using daily data from 2013 to 2025, we find that the full-sample directional dominance between Bitcoin and gold is generally weak after multiple testing adjustments. However, under stress regimes, the causal relationship of gold to Bitcoin becomes more pronounced at longer investment horizons. Under normal conditions, causal effects remain unstable and fragmented. Economic effects are modest. Variance-based hedging gains are limited, while downside risk measures show moderate improvement during stress periods. Overall, the evidence suggests that gold does not serve as a universal hedge for Bitcoin, but may exert conditional informational influence during high-uncertainty states. The CFQR framework provides a structured way to identify such state-dependent causal patterns. Full article

The transition from Sanger to next-generation sequencing (NGS) for HIV-1 drug resistance testing offers enhanced sensitivity but also introduces bioinformatic variability. This study evaluated four strategies: the commercial Exatype platform, the academic Stanford HIVdb-NGS, the open-source Quasitools (HyDRA) suite, and a custom de novo assembly workflow, iLunaR. Using 85 clinical HIV-1 pol MiSeq sequencing datasets, concordance was assessed at a 2% mutation detection threshold. A majority consensus standard defined true presence if a mutation was detected by at least three pipelines and supported by Sanger sequencing. While the datasets were successfully processed by all pipelines, discordances emerged in detecting low-abundance mutations and a specific case of structural mutation. iLunaR achieved perfect agreement (Cohen’s kappa = 1.000; 95% CI: 1.000–1.000). Quasitools demonstrated the lowest agreement (Cohen’s kappa = 0.901; 95% CI: 0.807–0.995) due to consistent reporting of mutations at lower abundance levels and aligner-induced reference bias misclassifying a deletion as a point mutation. Exatype (Cohen’s kappa = 0.951; 95% CI: 0.884–1.000) and Stanford (Cohen’s kappa = 0.926; 95% CI: 0.846–1.000) exhibited specific failures, including an omitted integrase mutation and codon translation errors, respectively. These findings confirm that bioinformatic algorithm choice remains a critical clinical variable despite NGS advancements in HIV-1 drug resistance testing. Full article

Pedestrian safety remains a pressing challenge in low- and middle-income countries (LMICs), where global predictive models often misrepresent local realities. This study tests the hypothesis that global predictive models, such as the International Road Assessment Programme (iRAP), overestimate countermeasure effectiveness in LMICs because key contextual factors are omitted. The two-phase research combined a PRISMA-based systematic literature review (SLR) with a quantitative iRAP performance gap analysis of the countermeasures implemented in the candidate studies of the SLR. The review systematically evaluated the effectiveness of pedestrian safety countermeasures, with an emphasis on their application in LMIC contexts. Following PRISMA 2020 guidelines, 14 longitudinal before–after studies were selected from 1911 records and screened with EPPI-Reviewer 4 software. The analysis identified 33 contextual factors shaping countermeasure performance across both high- and low-income settings; of these, 23 were specific to LMICs, and 13 are not accounted for in the iRAP model. The findings show that iRAP systematically overestimates countermeasure effectiveness in LMICs due to weak enforcement, poor maintenance, and informal road use. Transverse rumble strips were the only intervention consistently effective across diverse LMIC settings. A novel performance gap analysis of five LMIC case studies revealed an average discrepancy of 30.9% (SD = 29.7%) between predicted and observed outcomes. A risk of bias assessment showed that most LMIC studies were of moderate to serious risk, reflecting systemic data limitations and a frequent reliance on proxy outcomes. These findings highlight the urgent need for recalibrated, context-sensitive frameworks that incorporate enforcement, maintenance, and socio-economic variables. Policy implications include prioritising affordable and scalable countermeasures, pairing infrastructure with enforcement and education, and strengthening crash data systems to support more realistic, evidence-based road safety planning. Full article

Financial accounting manipulation (FAM) remains a persistent concern in emerging Asian markets, yet existing studies typically assess firm resilience, market valuation, and institutional governance separately. This study addresses this gap by examining how the Resilience Factor (RF), Market Valuation (VAL), and Country Governance Index (CGI), along with their interaction effects, shape FAM. Using a panel dataset of 4303 non-financial firms across 17 Asian countries from 2012 to 2023 (51,636 observations), the analysis employs an Instrumental Variable–Two-Stage Least-Squares (IV-2SLS) approach to address endogeneity related to simultaneity and omitted variable bias. The results show that financially resilient firms are more prone to manipulation, market valuation reduces manipulation incentives, and stronger country governance constrains manipulation. Moreover, valuation moderates the governance–manipulation relationship, suggesting complementary monitoring roles between markets and institutions. Robustness checks across regions, industries, and the COVID-19 period confirm the findings. The study contributes to agency and institutional theory by highlighting how firm-level and country-level mechanisms jointly influence manipulation, offering policy implications for regulators and investors in Asian capital markets. Full article

Feldstein and Horioka in 1980 estimated (I/GDP)_i = α + β(S/GDP)_i where “i” is for a given country over time, “I” is domestic investment, and “S” is domestic savings. Feldstein and Horioka found βs that were insignificantly different from one and significantly different from zero. According to Feldstein and Horioka, these results conflict with an assumption of perfect capital mobility because, if capital were perfectly mobile, then β should be zero. We estimated (I/GDP)_it = α + β(S/GDP)_it using data from 22 countries from 1970 to 2023, where i denotes the country and t denotes the year. We found βs to be significantly less than 1 but greater than 0. We then used Reiterative Truncated Projected Least Squares, which was designed to solve the omitted variable problem (and helps a researcher visualize the effects of heteroscedasticity), to estimate a β_it for every observation. We find that β_it decreases for countries that export capital and increases for countries that import capital. We argue that the Feldstein-Horioka “puzzle” is based on a confusion—when the effect of both exporting and importing capital is considered, β should equal approximately one. Feldstein and Horioka focus on single countries, but when pairs of savings exporters and importers are considered, their “puzzle” disappears. However, the fact that β_it is now much less than 1 and falling over time suggests that a global glut of savings is worsening. Full article

Background: Nursing is an essential pillar in health services provision; however, its social value is often underestimated. The public image of, and society’s attitude toward, the profession is commonly influenced by stereotypes and biases. Objective: This study aimed to analyze the predictive influence of empathy, professional values and communication skills on the social image and attitude towards nursing. Methods: A cross-sectional, correlational study was conducted in the Valencian Community, Spain. Snowball sampling was used for data collection from 300 participants (81% female; mean age 35.85 years, SD = 14.99). Empathy, professional values and communication skills were measured, and a structural equation model was proposed to assess their influence. Results: Professional values were significant predictors of both social image (β = 0.41) and attitude toward nursing (β = 0.34). Similarly, communication skills predicted social image (β = 0.31) and attitude (β = 0.37). Empathy also emerged as a significant, though minor, predictor. Collectively, these three factors explained 30% of the variance in social image and 39% in attitude toward the profession. The main limitations arise from the severe demographic bias of the snowball sample (skewed toward women, young, and highly educated individuals) and the modest explanatory power (R² of 30–39%). This limits the generalizability of the findings and suggests the need for future research on omitted variables, such as working conditions and organizational culture. Conclusions: Empathy, professional values and communication skills are key competencies contributing to a more positive social image of and attitude toward nursing. Investing in the development of these competencies can significantly enhance the recognition and appreciation of nursing within the healthcare system. Full article

This study investigates the interconnections among sustainability reporting, social performance, and firm value across the BRICS nations (Brazil, Russia, India, China, and South Africa). Employing a quantitative research design, the study utilizes firm-level data from the Refinitiv database, covering 862 firms operating in the BRICS countries from 2017 to 2022. The analysis begins with Ordinary Least Squares (OLS) regression and extends to models incorporating year-fixed effects and firm-fixed effects to account for heterogeneity and omitted variable bias. Robustness checks are conducted using OLS regression with robust standard errors, fixed effects regression with Driscoll–Kraay standard errors, and an instrumental variable approach to address potential endogeneity concerns. To examine the moderating role of sustainability reporting, interaction terms are incorporated into the regression models and margin plots are used for visualization. The findings reveal that social performance positively impacts firm value, underscoring the role of social responsibility in driving financial performance. Furthermore, sustainability reporting strengthens this relationship, indicating that firms with well-established reporting frameworks can effectively leverage social initiatives to enhance market valuation. Therefore, this study contributes to the literature by providing empirical evidence on the moderating effect of sustainability reporting in emerging markets. The findings offer valuable implications for policymakers, investors, and corporate leaders seeking to optimize CSR strategies and enhance firm value in dynamic economic environments. Full article

Urban growth models often prioritize environmental and accessibility factors while underestimating behavioral and functional dynamics. This study develops a POI-enhanced Cellular Automata (CA) framework to simulate urban expansion by incorporating three semantic indicators derived from Point-of-Interest (POI) data—density (PD), diversity (PDI), and functional centrality (FC). Taking Yan’an, China, as a case, the model integrates these indicators with terrain and infrastructure variables via logistic regression to estimate land-use transition probabilities. To ensure robustness, spatial block cross-validation was adopted to reduce spatial autocorrelation bias. Results show that the POI-based model outperforms the baseline in both Kappa and Figure of Merit metrics. High-density and mixed-function POI zones correspond with compact infill growth, while high-centrality zones predict decentralized expansion beyond administrative cores. These findings highlight how functional semantics sharpen spatial prediction and uncover latent behavioral demand. Policy implications include using POI-informed maps for adaptive zoning, ecological buffer protection, and growth hotspot management. The study contributes a transferable workflow for embedding behavioral logic into spatial simulation. However, limitations remain: the model relies on static POI data, omits vertical (3D) development, and lacks direct comparison with alternative models like Random Forest or SVM. Future research could explore dynamic POI trajectories, integrate 3D building forms, or adopt agent-based modeling for richer institutional representation. Overall, the approach enhances both the accuracy and interpretability of urban growth modeling, providing a flexible tool for planning in functionally evolving and ecologically constrained cities. Full article

The incorporation of environmental, social, and governance (ESG) concerns into corporate finance has accelerated globally; nevertheless, empirical data about its effects in the U.S. context is still scarce. This research examines the impact of sustainable financing on the financial performance of non-financial enterprises listed on the New York Stock Exchange (NYSE) from 2008 to 2024. This study used the stakeholder theory and other theories to analyze four aspects of sustainable finance: green financing efforts, emission reduction strategies, sustainable product initiatives, and environmental investment initiatives. The study implemented a dynamic panel regression model with the two-step Generalized Method of Moments (GMM) to mitigate endogeneity and omit variable bias. The findings indicate that green finance, emission reduction strategies, and sustainable product efforts have a positive and significant impact on Return on Assets (ROA) and Return on Net Operating Assets (RNOA), demonstrating their effectiveness in enhancing financial performance. Conversely, environmental investment programs exhibited a strong and negative correlation with financial success, indicating immediate cost implications. These findings emphasize the significance of strategic planning in sustainability investments and reinforce the necessity for legislative incentives to assist enterprises throughout the transition. This study enhances the literature by providing U.S.-specific, component-level insights into the financial implications of sustainable financing, therefore offering pragmatic counsel for managers, investors, and regulators. Full article

This study provides the first empirical evidence on the impact of innovation and firm growth on performance across G7 economies, using a unique panel dataset of 252 firms from 2020 to 2024. This study examines two core dimensions of firm performance—labor productivity and asset turnover—and employs multiple innovation proxies, including R&D Intensity, R&D-to-Assets, and R&D Growth Rate. To address potential endogeneity arising from reverse causality and omitted variable bias, the author implements the heteroskedasticity-based instrumental variable estimator, which constructs internal instruments from the model’s error structure. The study’s results reveal a consistent and significant positive causal effect of innovation on labor productivity, confirming its role as a driver of firm-level efficiency. However, innovation exhibits a negative and significant association with asset turnover, highlighting short-term trade-offs in operational efficiency, particularly in firms with aggressive R&D strategies. This study further finds that these effects are moderated by firm profitability and industry conditions, suggesting the importance of strategic and contextual alignment in innovation outcomes. Taken together, the findings offer new insights into the dual nature of innovation, enhancing productivity while imposing transitional efficiency costs and carrying significant implications for corporate innovation strategy and public policy in advanced economies. Full article

This study investigates the impact of environmental, social, and governance (ESG) disclosure on value creation in a balanced panel of 100 non-financial Sharia-compliant firms listed on the Saudi Stock Exchange over the period 2014–2023. The analysis employs a combination of econometric techniques, including fixed effects models with Driscoll–Kraay standard errors, Pooled Ordinary Least Squares (POLS) with Driscoll–Kraay standard errors and industry and year dummies, and two-step system generalized method of moments (GMM) estimation to address potential endogeneity and omitted variable bias. Value creation is measured using Tobin’s Q (TBQ), Return on Assets (ROA), and Return on Equity (ROE). The models also control for firm-specific variables such as firm size, leverage, asset tangibility, firm age, growth opportunities, and market capitalization. The findings reveal that ESG disclosure has a positive and statistically significant effect on firm value across all three performance measures. Furthermore, firm size significantly moderates this relationship, with larger Sharia-compliant firms experiencing greater value gains from ESG practices. These results align with agency, stakeholder, and signaling theories, emphasizing the role of ESG in enhancing transparency, reducing information asymmetry, and strengthening stakeholder trust. The study provides empirical evidence relevant to policymakers, investors, and firms striving to achieve Saudi Arabia’s Vision 2030 sustainability goals. Full article

This research studies the impact of macroeconomic announcement surprises on daily U.S. Treasury excess returns during the heart of Alan Greenspan’s tenure as Federal Reserve Chair, addressing the possible limitations of standard static regression (SSR) models, which may suffer from omitted variable bias, parameter instability, and poor mis-specification diagnostics. To complement the SSR framework, an automated general-to-specific (Gets) modeling approach, enhanced with modern indicator saturation methods for robustness, is applied to improve empirical model discovery and mitigate potential biases. By progressively reducing an initially broad set of candidate variables, the Gets methodology steers the model toward congruence, dispenses unstable parameters, and seeks to limit information loss while seeking model congruence and precision. The findings, herein, suggest that U.S. Treasury market responses to macroeconomic news shocks exhibited stability for a core set of announcements that reliably influenced excess returns. In contrast to computationally costless standard static models, the automated Gets-based approach enhances parameter precision and provides a more adaptive structure for identifying relevant predictors. These results demonstrate the potential value of incorporating interpretable automated model selection techniques alongside traditional SSR and Markov switching approaches to improve empirical insights into macroeconomic announcement effects on financial markets. Full article

Promoting water conservation is crucial for building a modern ecological civilization. The water resource tax helps enforce water-saving policies and strict usage controls. The difference-in-differences (DID) method avoids endogeneity and omitted variable bias, making it ideal for policy evaluation. Using the 2017 pilot water tax expansion as a quasi-natural experiment, this study applies DID to assess the reform’s impact on total factor productivity (TFP) in water-intensive industries. The results indicate that the TFP of water-intensive enterprises in pilot regions increased by an average of 2.5% and that the reform has a positive and significant effect on TFP, with notable improvements in management efficiency and resource allocation. The findings further imply that the reform encourages better management practices, such as optimized water use and cost-effective resource allocation, rather than technological innovation as the main driver of improved productivity. This underscores tax reforms’ dual role in enhancing operational efficiency and environmental sustainability. The findings demonstrate water resource tax reforms’ potential to foster a more sustainable industrial sector, especially in water-stressed regions. Full article

This opinion piece postulates that quantitative environmental research and public health spatial analysts unknowingly tolerate certain spatial statistical model specification errors, whose remedies constitute some of the urgent emerging trends and issues in this subfield (e.g., forecasting disease spreading). Within this context, this paper addresses misspecifications affiliated with omitted variable bias complications arising from ignoring, and hence abandoning, negative spatial autocorrelation latent in georeferenced disease data, and/or being ill-informed about reigning teledependencies (i.e., long-distance spatial correlations). As imperative academic challenges, it advances elegant and convincing arguments to do otherwise. Its two particular themes are positive–negative spatial autocorrelation mixtures, and hierarchical autocorrelation generated by hegemonic urban systems. Comprehensive interpretations and implementations of these two conjectures constitute future research directions. Important conceptualizations for treatments reported in this paper include confounding variables and Moran eigenvector spatial filtering. This paper’s fundamental implication is an advocacy for a prodigious paradigm shift, a marked change in the collective mindsets and applications of spatial epidemiologists when specifying spatial regression equations to describe either environmental health data, or a publicly transparent geographic diffusion of diseases. Full article