Pub Date : 2026-03-01Epub Date: 2026-01-21DOI: 10.1016/j.iref.2026.104951
Deni Wahidin , Alexandr Akimov , Eduardo Roca , Nicholas Rohde
We study the nexus between institutional quality and domestic bond market development. Using World Bank data, we undertake a comprehensive search for associations between various institutional quality indicators and domestic bond market development. We find that markers of rule of law, regulatory quality, control of corruption, government effectiveness, political stability and absence of violence/terrorism, and voice and accountability are all predictive of bond market growth in the longer term. Although the direction of association between institutional quality indicators and bond market development remains broadly consistent across models, the magnitude and statistical significance vary across dimensions. In particular, rule of law and political stability consistently show stronger and statistically significant effects in fixed-effects and five-year lagged models. Our econometric set-up ensures that endogeneity owing to reverse causality is avoided, enhancing the likelihood that these associations are causal. We provide further insights as to whether the impact of institutional quality is direct or indirect via mediating analysis. The indirect impacts of institutional quality through broader economic variables are stronger than the direct impacts.
{"title":"Does institutional quality matter for domestic bond market development?","authors":"Deni Wahidin , Alexandr Akimov , Eduardo Roca , Nicholas Rohde","doi":"10.1016/j.iref.2026.104951","DOIUrl":"10.1016/j.iref.2026.104951","url":null,"abstract":"<div><div>We study the nexus between institutional quality and domestic bond market development. Using World Bank data, we undertake a comprehensive search for associations between various institutional quality indicators and domestic bond market development. We find that markers of rule of law, regulatory quality, control of corruption, government effectiveness, political stability and absence of violence/terrorism, and voice and accountability are all predictive of bond market growth in the longer term. Although the direction of association between institutional quality indicators and bond market development remains broadly consistent across models, the magnitude and statistical significance vary across dimensions. In particular, rule of law and political stability consistently show stronger and statistically significant effects in fixed-effects and five-year lagged models. Our econometric set-up ensures that endogeneity owing to reverse causality is avoided, enhancing the likelihood that these associations are causal. We provide further insights as to whether the impact of institutional quality is direct or indirect via mediating analysis. The indirect impacts of institutional quality through broader economic variables are stronger than the direct impacts.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104951"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-12DOI: 10.1016/j.iref.2026.104912
Hugo Benedetti , Ehsan Nikbakht , Boris Pastén-Henríquez
This paper examines the directional connectedness between the returns of Bitcoin and Ethereum and the supply of stablecoins across different market conditions. Using a Quantile Vector Autoregression (QVAR) model, we analyze daily log-returns of major cryptocurrencies and changes in stablecoin supply from January 2021 to November 2024. Our findings show that the Total Connectedness Index (TCI) nearly triples under extreme conditions, with Bitcoin and Ethereum transitioning from passive roles in normal periods to dominant transmitters of influence during downturns.
Stablecoins behave heterogeneously across regimes, with roles varying significantly even within the same subclass. These patterns suggest that, under certain conditions, major cryptocurrencies can influence stablecoin issuance in distinct ways, leading to asymmetric adjustments in supply across individual stablecoins and shaping liquidity dynamics throughout the ecosystem.
The findings motivate the development of regime-sensitive monitoring tools and support ongoing policy discussions around stablecoin design, issuance frameworks, and market transparency.
{"title":"Quantile-based connectedness in the crypto-stablecoin network across market conditions","authors":"Hugo Benedetti , Ehsan Nikbakht , Boris Pastén-Henríquez","doi":"10.1016/j.iref.2026.104912","DOIUrl":"10.1016/j.iref.2026.104912","url":null,"abstract":"<div><div>This paper examines the directional connectedness between the returns of Bitcoin and Ethereum and the supply of stablecoins across different market conditions. Using a Quantile Vector Autoregression (QVAR) model, we analyze daily log-returns of major cryptocurrencies and changes in stablecoin supply from January 2021 to November 2024. Our findings show that the Total Connectedness Index (TCI) nearly triples under extreme conditions, with Bitcoin and Ethereum transitioning from passive roles in normal periods to dominant transmitters of influence during downturns.</div><div>Stablecoins behave heterogeneously across regimes, with roles varying significantly even within the same subclass. These patterns suggest that, under certain conditions, major cryptocurrencies can influence stablecoin issuance in distinct ways, leading to asymmetric adjustments in supply across individual stablecoins and shaping liquidity dynamics throughout the ecosystem.</div><div>The findings motivate the development of regime-sensitive monitoring tools and support ongoing policy discussions around stablecoin design, issuance frameworks, and market transparency.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104912"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-20DOI: 10.1016/j.iref.2026.104926
Florinda Silva, Sónia Silva
This study analyzes the relationship between working capital management and the profitability of EU-based SMEs over the period 2012–2022. We find an inverted U-shaped relationship, indicating an optimal level of working capital investment that maximizes corporate profitability. Results show a positive effect of low levels of investment in working capital on profitability, which turns negative when such investment surpasses its optimal point. The optimal net trade cycle is lower for more financially constrained firms. Any deviation from the optimal investment point in working capital negatively impacts firms’ performance, particularly for more financially constrained firms. These findings remain robust to several additional tests.
{"title":"Working capital management and corporate performance: The role of financial constraints","authors":"Florinda Silva, Sónia Silva","doi":"10.1016/j.iref.2026.104926","DOIUrl":"10.1016/j.iref.2026.104926","url":null,"abstract":"<div><div>This study analyzes the relationship between working capital management and the profitability of EU-based SMEs over the period 2012–2022. We find an inverted U-shaped relationship, indicating an optimal level of working capital investment that maximizes corporate profitability. Results show a positive effect of low levels of investment in working capital on profitability, which turns negative when such investment surpasses its optimal point. The optimal net trade cycle is lower for more financially constrained firms. Any deviation from the optimal investment point in working capital negatively impacts firms’ performance, particularly for more financially constrained firms. These findings remain robust to several additional tests.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104926"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-20DOI: 10.1016/j.iref.2026.104935
Sihan Liu , An Huang , Nan Lin , Zhenfu Han
This study examines how digital infrastructure affects firm productivity and highlights platform integration as a key enabling mechanism. Using a panel dataset of Chinese listed companies from 2014 to 2023, we employ a fixed-effects regression approach to assess the extent to which improvements in digital infrastructure enhance total factor productivity (TFP) at the firm level. In addition, the study investigates the moderating role of platform integration, positing that firms more deeply embedded in digital platform ecosystems are better positioned to capitalize on digital infrastructure to improve productivity outcomes. The empirical results demonstrate that digital infrastructure exerts a significantly positive effect on firm productivity. More importantly, the analysis shows that platform integration markedly strengthens this positive relationship, highlighting its essential function in converting digital infrastructure investments into measurable productivity gains. These findings highlight the importance of complementing tangible infrastructure investments with measures that foster platform ecosystem development and enterprise integration to fully unlock productivity gains, particularly in regions with developing digital foundations.
{"title":"Does platform integration unlock the value of digital infrastructure? Evidence from Chinese listed firms","authors":"Sihan Liu , An Huang , Nan Lin , Zhenfu Han","doi":"10.1016/j.iref.2026.104935","DOIUrl":"10.1016/j.iref.2026.104935","url":null,"abstract":"<div><div>This study examines how digital infrastructure affects firm productivity and highlights platform integration as a key enabling mechanism. Using a panel dataset of Chinese listed companies from 2014 to 2023, we employ a fixed-effects regression approach to assess the extent to which improvements in digital infrastructure enhance total factor productivity (TFP) at the firm level. In addition, the study investigates the moderating role of platform integration, positing that firms more deeply embedded in digital platform ecosystems are better positioned to capitalize on digital infrastructure to improve productivity outcomes. The empirical results demonstrate that digital infrastructure exerts a significantly positive effect on firm productivity. More importantly, the analysis shows that platform integration markedly strengthens this positive relationship, highlighting its essential function in converting digital infrastructure investments into measurable productivity gains. These findings highlight the importance of complementing tangible infrastructure investments with measures that foster platform ecosystem development and enterprise integration to fully unlock productivity gains, particularly in regions with developing digital foundations.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104935"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-27DOI: 10.1016/j.iref.2026.104932
Xingyu Li , Hang Hong , Biao Liu
This study takes Chinese A-share listed companies from 2013 to 2023 as the research sample to empirically examine the relationship between green innovation and corporate tax reduction, as well as the regulatory role of venture capital in this mechanism. The results indicate that: First, corporate green innovation input exerts a significant positive effect on tax reduction. Second, the tax reduction effect of green innovation shows obvious heterogeneity depending on the corporate tax burden level. Third, in terms of external organizational characteristics, the tax reduction effect of green innovation is more significant in enterprises with high industry competition intensity and high social responsibility performance. Fourth, venture capital participation plays a positive moderating role in the process of green innovation promoting corporate tax reduction; specifically, the involvement of venture capital strengthens the tax reduction effect brought by green innovation. Fifth, the moderating effect of venture capital is heterogeneous in enterprises with different financialization degrees, and it is more significant in highly financialized enterprises, while it has no obvious regulatory impact in low-financialized enterprises.
{"title":"Venture capital, green innovation, and corporate tax reduction","authors":"Xingyu Li , Hang Hong , Biao Liu","doi":"10.1016/j.iref.2026.104932","DOIUrl":"10.1016/j.iref.2026.104932","url":null,"abstract":"<div><div>This study takes Chinese A-share listed companies from 2013 to 2023 as the research sample to empirically examine the relationship between green innovation and corporate tax reduction, as well as the regulatory role of venture capital in this mechanism. The results indicate that: First, corporate green innovation input exerts a significant positive effect on tax reduction. Second, the tax reduction effect of green innovation shows obvious heterogeneity depending on the corporate tax burden level. Third, in terms of external organizational characteristics, the tax reduction effect of green innovation is more significant in enterprises with high industry competition intensity and high social responsibility performance. Fourth, venture capital participation plays a positive moderating role in the process of green innovation promoting corporate tax reduction; specifically, the involvement of venture capital strengthens the tax reduction effect brought by green innovation. Fifth, the moderating effect of venture capital is heterogeneous in enterprises with different financialization degrees, and it is more significant in highly financialized enterprises, while it has no obvious regulatory impact in low-financialized enterprises.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104932"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2025-12-16DOI: 10.1016/j.iref.2025.104840
Luoxi Lin , Menjuan Luo
We explore the impact of policy uncertainty in the field of environment and technology (E&T) on risk assets and safe-haven assets (SSEC, gold and 10-year bonds), using a sample period of 2003–2023. We focus on four types of policy uncertainty indicators, namely, artificial intelligence (AI), high technology (HighT), carbon emissions (CarbonE), and green energy (GreenE). The study reveals that the impact of E&T policy uncertainty on financial assets has significant quantile heterogeneity and time-varying characteristics. At the quantile level, the stock market is significantly affected by CarbonE over the short term when it is extremely volatile; gold is sensitive to the medium- and short-term policy uncertainty of HighT and CarbonE at a medium level of volatility; and the bond market has the most complex response to policy uncertainty, covering multiple quantiles and lag orders. At the time level, the policy impact undergoes dynamic changes, such as the phased impacts of AI and HighT on SSEC, gold, and bond and the long-term significant impacts of CarbonE and GreenE on SSEC. This research provides investors and policy-makers undergoing the energy transition period with an understanding of asset allocation and policy optimization on the basis of policy uncertainty.
{"title":"Policy uncertainty in environment & technology: Impacts on risky and safe-haven assets in China","authors":"Luoxi Lin , Menjuan Luo","doi":"10.1016/j.iref.2025.104840","DOIUrl":"10.1016/j.iref.2025.104840","url":null,"abstract":"<div><div>We explore the impact of policy uncertainty in the field of environment and technology (E&T) on risk assets and safe-haven assets (SSEC, gold and 10-year bonds), using a sample period of 2003–2023. We focus on four types of policy uncertainty indicators, namely, artificial intelligence (AI), high technology (HighT), carbon emissions (CarbonE), and green energy (GreenE). The study reveals that the impact of E&T policy uncertainty on financial assets has significant quantile heterogeneity and time-varying characteristics. At the quantile level, the stock market is significantly affected by CarbonE over the short term when it is extremely volatile; gold is sensitive to the medium- and short-term policy uncertainty of HighT and CarbonE at a medium level of volatility; and the bond market has the most complex response to policy uncertainty, covering multiple quantiles and lag orders. At the time level, the policy impact undergoes dynamic changes, such as the phased impacts of AI and HighT on SSEC, gold, and bond and the long-term significant impacts of CarbonE and GreenE on SSEC. This research provides investors and policy-makers undergoing the energy transition period with an understanding of asset allocation and policy optimization on the basis of policy uncertainty.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104840"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146035968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-16DOI: 10.1016/j.iref.2026.104916
B.S. Prashanth , Manoj Kumar , Ariful Hoque , Nasser Al Muraqab , Immanuel Azaad Moonesar , Udo Christian Braendle , Ananth Rao
The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal.
We utilize a supervised learning pipeline that incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives.
Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.
{"title":"Prediction of bank transaction fraud using TabNet—an adaptive deep learning architecture","authors":"B.S. Prashanth , Manoj Kumar , Ariful Hoque , Nasser Al Muraqab , Immanuel Azaad Moonesar , Udo Christian Braendle , Ananth Rao","doi":"10.1016/j.iref.2026.104916","DOIUrl":"10.1016/j.iref.2026.104916","url":null,"abstract":"<div><div>The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal.</div><div>We utilize a supervised learning pipeline that incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives.</div><div>Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104916"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146036060","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-02-03DOI: 10.1016/j.iref.2026.104953
Francisco-Xavier Lores
This paper quantitatively analyzes six major recessions in the Spanish economy between 1850 and 2023 using an accounting framework that decomposes deviations from trend into structural components. The results identify two types of recessions: those driven by labour-aumenting efficiency—such as the fin de siècle Depression and the Great Depression—and those primarily shaped by the household labour wedge—namely, the Great Stagflation, European Recession, Great Recession, and the covid-19 crisis. Tax dynamics played a key role in the Great Stagflation, but not in the more recent crises. Openness and employment composition are informative about labour-aumenting efficiency trend, while institutional labour market features — such as temporary contracts and unemployment benefits — are closely linked to the household labour wedge. The analysis confirms the growing relevance of the household labour wedge in explaining macroeconomic fluctuations in Spain, even when accounting for a stochastic downward trend in hours worked.
{"title":"Spanish recessions 1850–2023: A business cycle accounting analysis","authors":"Francisco-Xavier Lores","doi":"10.1016/j.iref.2026.104953","DOIUrl":"10.1016/j.iref.2026.104953","url":null,"abstract":"<div><div>This paper quantitatively analyzes six major recessions in the Spanish economy between 1850 and 2023 using an accounting framework that decomposes deviations from trend into structural components. The results identify two types of recessions: those driven by labour-aumenting efficiency—such as the <em>fin de siècle Depression</em> and the <em>Great Depression</em>—and those primarily shaped by the household labour wedge—namely, the <em>Great Stagflation</em>, <em>European Recession</em>, <em>Great Recession</em>, and the <span>covid-19</span> crisis. Tax dynamics played a key role in the <em>Great Stagflation</em>, but not in the more recent crises. Openness and employment composition are informative about labour-aumenting efficiency trend, while institutional labour market features — such as temporary contracts and unemployment benefits — are closely linked to the household labour wedge. The analysis confirms the growing relevance of the household labour wedge in explaining macroeconomic fluctuations in Spain, even when accounting for a stochastic downward trend in hours worked.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104953"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-24DOI: 10.1016/j.iref.2026.104936
Nimrod Segev
This paper studies the effect of bank competition on the optimal use of monetary and macroprudential policies. To this end, I develop a New Keynesian DSGE model with collateral constraints and an imperfectly competitive banking sector. The results from the model demonstrate that the degree of competition in the banking sector has a sizable impact on the optimal mix of monetary and macroprudential policies. Specifically, the gains from a leaning-against-the-wind monetary policy are substantially smaller when the banking sector is less competitive. Results suggest that, from a policy perspective, monitoring the level of bank competition is crucial when the objective is to promote financial and economic stability.
{"title":"Macroprudential and monetary policies with an imperfectly competitive banking sector","authors":"Nimrod Segev","doi":"10.1016/j.iref.2026.104936","DOIUrl":"10.1016/j.iref.2026.104936","url":null,"abstract":"<div><div>This paper studies the effect of bank competition on the optimal use of monetary and macroprudential policies. To this end, I develop a New Keynesian DSGE model with collateral constraints and an imperfectly competitive banking sector. The results from the model demonstrate that the degree of competition in the banking sector has a sizable impact on the optimal mix of monetary and macroprudential policies. Specifically, the gains from a leaning-against-the-wind monetary policy are substantially smaller when the banking sector is less competitive. Results suggest that, from a policy perspective, monitoring the level of bank competition is crucial when the objective is to promote financial and economic stability.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104936"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146189343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-03-01Epub Date: 2026-01-27DOI: 10.1016/j.iref.2026.104970
Paula Castro, Borja Amor-Tapia, María T. Tascón
We investigate how a firm's environmental management efficiency affects spare debt capacity. We compute the firm-specific expected and unexpected carbon emissions, and two measures of debt capacity for 1307 European firms in 20 countries during the 2002–2022 period. We define spare debt capacity as firm-specific debt flexibility and analyze it through three transition-risk mechanisms: information asymmetry reduction derived from the mandatory carbon disclosures; information asymmetry reduction derived from information stability; and bankruptcy risk increase derived from unexpected carbon emissions. With respect to the first mechanism, we find no direct link between total emissions and spare debt capacity, highlighting the need to decompose emissions. For the second mechanism, expected emissions show a positive and significant association with spare debt capacity, suggesting that stable and predictable environmental information helps reduce information asymmetry. For the third mechanism, unexpected emissions are negatively related to spare debt capacity, consistent with their role as signals of higher environmental and compliance risk, increasing perceived bankruptcy risk. Further analysis shows that the results hold in the presence of liquidity reduction, additional capital investment, and assurance practices, as well as for firms operating under different environmental uncertainty scenarios (firm-level, industry-level, and macroeconomic), and those facing business uncertainty. Our study introduces a novel method for separating emissions into expected and unexpected components, enabling tests of pecking-order and trade-off theories mechanisms related to debt capacity. These insights can help lenders integrate environmental factors into credit assessments and support firms' managers in designing financing strategies for the transition to cleaner production.
{"title":"Enhancing spare debt capacity via efficient carbon management","authors":"Paula Castro, Borja Amor-Tapia, María T. Tascón","doi":"10.1016/j.iref.2026.104970","DOIUrl":"10.1016/j.iref.2026.104970","url":null,"abstract":"<div><div>We investigate how a firm's environmental management efficiency affects spare debt capacity. We compute the firm-specific expected and unexpected carbon emissions, and two measures of debt capacity for 1307 European firms in 20 countries during the 2002–2022 period. We define spare debt capacity as firm-specific debt flexibility and analyze it through three transition-risk mechanisms: information asymmetry reduction derived from the mandatory carbon disclosures; information asymmetry reduction derived from information stability; and bankruptcy risk increase derived from unexpected carbon emissions. With respect to the first mechanism, we find no direct link between total emissions and spare debt capacity, highlighting the need to decompose emissions. For the second mechanism, expected emissions show a positive and significant association with spare debt capacity, suggesting that stable and predictable environmental information helps reduce information asymmetry. For the third mechanism, unexpected emissions are negatively related to spare debt capacity, consistent with their role as signals of higher environmental and compliance risk, increasing perceived bankruptcy risk. Further analysis shows that the results hold in the presence of liquidity reduction, additional capital investment, and assurance practices, as well as for firms operating under different environmental uncertainty scenarios (firm-level, industry-level, and macroeconomic), and those facing business uncertainty. Our study introduces a novel method for separating emissions into expected and unexpected components, enabling tests of pecking-order and trade-off theories mechanisms related to debt capacity. These insights can help lenders integrate environmental factors into credit assessments and support firms' managers in designing financing strategies for the transition to cleaner production.</div></div>","PeriodicalId":14444,"journal":{"name":"International Review of Economics & Finance","volume":"106 ","pages":"Article 104970"},"PeriodicalIF":5.6,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146074735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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