This study, based on a theoretical model, evaluates the optimality of green finance policies for banks, considering three options: (i) mandatory green priority sector lending—the social planner mandates all banks to allocate a fixed minimum portion of total credits to the green sector, (ii) voluntary green priority sector lending—the social planner advises banks to allocate certain portion of total credits to the green sector voluntarily, but imposes a compensatory green tax on those banks which do not invest in green sector to adjust the green credit loss and invests entire proceeds in the green sector, and (iii) green funding—banks raise funds specifically for green credits. The optimal choice depends on the risk-return profile, the cost of raising funds, and the value banks place on green projects. The model suggests that if the cost of green finance is high and banks place a high value on green projects, voluntary green priority sector lending is preferable over mandatory one if the compensatory green tax rate is below a threshold level. Conversely, if banks do not highly value green projects, raising green funds and allocating the proceeds to green projects is optimal.
This study investigated the nonlinear corruption-growth relationship using panel data for the period 2012–2017. Economic performance is measured by GDP per capita in the long-run and GDP growth in short-run, while corruption is proxied by Corruption Perceptions Index, Control of Corruption, and the International Country Risk Guide. We used quadratic functional form specification of the corruption proxy and the fixed effected estimation method. Separate model for each proxy are estimated using country fixed effect, region fixed effect and income group fixed effect. Empirical results indicate that in the long-run, corruption is associated with the low income per capita – sand the wheel hypothesis. However, in the short-run the relationship is more nuanced: when the governance is weak, the average growth rate is relatively higher, consistent with grease the wheel hypothesis, but this comes with the substantially greater volatility and risk of negative growth. As the governance improves, the average growth decline from approximately 5.8
This study explores the energy-environment-growth nexus in BRICS nations, focusing on renewable energy consumption, CO₂ emissions, green technology, and economic growth. Using Panel ARDL, Panel NARDL, and the Dumitrescu-Hurlin panel causality test, the study analyzes the symmetric and asymmetric effects of renewable energy and CO₂ emissions on economic growth, highlighting their distinct short- and long-term impacts. The results indicate that renewable energy fosters long-term growth but poses short-term challenges due to transition costs, whereas CO₂ emissions drive short-term growth while undermining long-term sustainability. The study also highlights the role of green technology and capital formation in promoting sustainable growth. These findings underscore the need for targeted renewable energy policies and carbon reduction strategies that balance economic growth with environmental sustainability. By examining BRICS economies and uncovering asymmetric relationships, this research contributes to the literature by offering deeper insights into the energy-environment-growth nexus in rapidly developing economies.
The study investigates the impact of credit market factors on financial development in 114 economies from 2001 to 2021. We applied Pooled Mean Group estimation to examine the association between credit market factors and financial development. Instrumental Variable regression is used to analyse the impact of credit market factors on financial development. We also employed panel threshold regression to examine the heterogeneity between different groups of countries regarding credit market factors and financial development. Using Pooled Mean Group analysis, we found that credit market factors are associated with domestic credit in the long run, whereas they are found to be insignificant in the short run. Furthermore, the instrumental variable regression reveals that credit market factors, including bank liquid reserves, net interest margin, bank overhead costs, and liquid assets, have a significant influence on domestic credit. Panel threshold regression findings highlight that credit market factors interact with credit allocated to the private sector differently depending on the level of financial development in the economy. This study contributes by helping policymakers design and implement policies that support financial development, including financial incentives, credit risk models, regulatory and supervisory measures, and reforms for financial institutions. Financially developed countries tend to perform better in terms of investment, capital flow, and economic development. The study emphasizes the significance of financial development and its relationship with credit market factors.
Recent literature establishes signs of improvement in the anchoring of households’ inflation expectations in India after the implementation of flexible inflation targeting. This paper attempts to study the anchoring of expectations across geographical regions and compares the results between the pre-Covid and post-Covid periods. It tests the sensitivity of expectations towards inflation and estimates the time-varying trend in the expectations. It also tests the anchoring in expectations with respect to food and fuel shocks. The paper finds that the sensitivity of expectations to inflation, is significant in the northern and southern regions. Disagreements amongst the households in all the regions, except western region, increased. Inflation expectations in all the regions became less anchored after the pandemic. Given disperse findings across regions, a focused approach is required to contain inflation to anchor expectations.
Kerala has long been distinguished among Indian states for its robust healthcare infrastructure, underpinned by strong local governance mechanisms and institutional frameworks such as Self-Help Groups (SHGs). Against the backdrop of the COVID-19 pandemic, which revealed vulnerabilities in the state’s health system, this study investigates the association among local government expenditure patterns, district-level economic performance, and key health indicators in the 14 districts of Kerala. The health indicators used are the Infant Mortality Rate (IMR), the Maternal Mortality Ratio (MMR), the Death Rate (DR), and the Stillbirth Rate (SBR). Employing descriptive statistics, trend analysis, and an unbalanced panel data regression framework, the analysis draws on data from all 1200 urban and rural Local Government Institutions (LGIs) in the state, ensuring both comprehensive coverage and empirical rigour. The findings suggest that in a relatively high-performing state like Kerala, differences in LGI expenditure and district economic performance are not significantly associated with changes in key health indicators during the period of study. Due to the unavailability of consolidated district-level data before 2017–18, the temporal scope of the analysis is limited to recent years. This study addresses a critical gap in the literature as it examines the interlinkage between decentralised governance and public health outcomes and contributes to a nuanced understanding of the limits of decentralisation in improving health performance in a developing country’s context.
This paper examines the relationship between the Consumer Price Index (CPI) and the Producer Price Index (PPI) in France over the period 1970–2022 using wavelet coherence and partial wavelet coherence (PWC). PWC is employed as a de-trending device in the time–frequency domain: by conditioning the CPI–PPI relationship on M3, it isolates direct producer-to-consumer transmission from co-movement driven by a common monetary trend. The results show that the CPI–PPI relationship is time-varying and frequency-dependent, with PPI leading CPI at medium frequencies and during crisis episodes, consistent with a cost-push mechanism. Occasional reversals around the subprime crisis suggest that transmission is state-dependent rather than structural. Conditioning on M3 substantially reduces low-frequency coherence, indicating that part of the long-run co-movement reflects a common monetary trend rather than direct pass-through, a result not previously documented in the wavelet literature. Headline CPI exhibits stronger coherence with PPI than core CPI, confirming the amplifying role of food and energy prices.
This paper explores the relationships between economic complexity, mobile phone subscriptions, and energy usage on carbon dioxide emissions, in the top 20 CO2 emitting countries. Using the Method of Moments Quantile Regression (MMQR), we find that these relationships differ depending on the level of CO2 emissions. Higher levels of Economic Complexity were associated with lower carbon dioxide emissions (-0.032
This paper employs machine learning to investigate the predictive power of educational attainment on new firm creation across U.S. counties. By using various supervised learning models trained on historical data (2018, 2019, and 2022) to predict 2023 establishment births, we find that the share of college-educated residents is consistently the most important predictor across all model types. In contrast, the share of high school dropouts is relatively unimportant and demonstrates inconsistent predictive power across different model specifications. These findings highlight that higher educational attainment serves as a robust predictive factor for establishment births at the local level, suggesting that counties with a high share of college-educated residents experience significantly higher rates of new firm creation.
This paper investigates the impact of strengthening laws regarding crime against women in India during 2005–18 on the reporting of crime. The study is based on data from the National Crime Records Bureau from 1995 to 2021 across 28 states in India regarding both internal (domestic) and external (workplace) crimes against women. First, using the method of (endogenously determined) multiple structural breaks, it is examined whether the sharp upturns in crime reporting appear to correspond closely to the enactment of stronger laws regarding crime against women. Then the panel autoregressive distributive lag model is applied to examine the impact of some critical socioeconomic factors on crime reporting and to confirm whether,—controlling for these factors—the strengthening of laws that facilitated and expedited prosecution did lead to a significant increase in reporting of such crimes. Finally, it is noted that changes in crime reporting does not necessarily represent corresponding changes in actual crime so that the impact of changes in laws on the actual incidence of crime also need to be studied.
This study investigates the role of institutions in explaining the association between finance and economic growth in 22 emerging economies from 2000 to 2022. A multidimensional analysis of financial development considering financial depth, financial access and financial efficiency across two primary categories, namely, financial institutions and financial markets is employed to examine in-depth the relationship between financial development and economic growth. In addition, the study utilizes Rodrick’s (2005) recommended four-way classification of institutions to represent institutional quality. The evidence estimated by employing a System Generalized Method of Moments (SGMM) suggested that both overall financial development and its varied dimensions contribute to enhancing economic growth in emerging economies. Importantly, developed institutional structure not only promote economic progression but also mitigates the adverse impact of financial development on economic growth. Overall, the empirical results propose policy recommendations to boost the level of growth by using financial development and institutions as economic tools.
This study examines the impact of different examination methods, specifically open-book exams (OBEs) and closed-book exams (CBE), on student mental well-being and learning outcomes. CBEs have traditionally dominated educational assessments, emphasizing memorization but often inducing stress and anxiety (Putwain, 2008). In contrast, OBEs are gaining attention for promoting deeper engagement with course material and reducing exam-related anxiety (Sotiriadou et al., 2020). However, the long-term retention benefits of OBEs remain uncertain, with mixed evidence on their effectiveness compared to closed-book formats (Senkova et al., 2018). This research conducted a between-Group evaluation in a private school in Haryana, India, examining students’ mental health and academic performance under both exam formats. Our findings reveal that students in the OBE group performed better in recall-based tests. However, the two groups observed no significant differences in critical thinking test scores. Regarding mental well-being, the overall effect of OBE was not significantly different from traditional exam formats. Yet, the top 40
Understanding the co-movements and dependence between financial assets is crucial for investment decision-making and better economic policies. In this paper, I will analyze the multivariate dependence structure between commodity and equity markets. The ARMA-GARCH R-vine copula model, a flexible approach to model high-dimensional data, was employed to examine linear and tail dependencies among three principal stock markets: the SP500, MSCI China, and MSCI India, alongside four commodity indices (SP GSCI precious metals, SP GSCI industrial metals, SP GSCI energy, and SPGSCI agriculture). The findings suggest that the financialization of commodities had an impact on the increase of the correlation between commodity and stock markets. The role of agriculture and precious metals as a safe haven has been highlighted, while energy and industrial metals have a significant profit potential but are more risky. Thus, commodities cannot be viewed as a single homogeneous class, and their behavior differs depending on the sector. Finally, the efficiency of the vine copula approach has been confirmed using a risk management analysis.
This paper investigates the role of human capital in fostering financial sector development in developing economies, an area that has received limited attention despite the extensive literature on the relationship between human capital and economic growth. The study empirically analyzes data from 28 developing countries over the period 1990–2019, employing FGLS, DOLS, FMOLS, and Driscoll-Kraay estimation techniques for robust results. Causality is assessed using the Dumitrescu-Hurlin bootstrap approach. The findings demonstrate that human capital significantly contributes to long-term financial sector development. Additionally, economic growth, trade openness, and remittances are positively associated with financial sector expansion. The causality analysis reveals bidirectional causality between human capital and financial sector development, as well as between other explanatory variables and financial development. The results suggest that policymakers should prioritize human capital development, alongside strategies to stimulate economic growth, trade openness, and remittance flows, to support financial sector advancement.
This paper investigates the long and short-term impacts of trade openness, energy use, urbanization, and GDP per capita growth on CO2 emissions in the five SAARC countries: Bangladesh, India, Pakistan, Nepal, and Sri Lanka; between 1991 and 2023. The findings obtained with the help of the Panel ARDL-PMG estimator partially confirm the Environment Kuznets Curve (EKC) hypothesis, indicating the region is still in the upward and pollution-intensive stage. The long-term forecasts indicate that the growth in CO2 emissions is 0.011
This study examines the effects of oil shocks—including oil supply, oil demand, and aggregate demand shocks—on dirty energy stock markets at a global level, employing a Quantile-on-Quantile Regression (QQR) framework to capture heterogeneous and distribution-dependent responses. By exploring the asymmetric relationships across multiple quantiles, this research provides a nuanced understanding of how different levels of oil shocks impact various segments of the dirty energy sector, including crude oil, gasoline, heating oil, and natural gas stock returns. The findings indicate that oil shocks exert a significant influence on dirty energy stock markets, with variations in impact dependent on the quantile being examined. Notably, positive and negative relationships were observed between oil shocks and dirty energy stock returns across different quantiles, highlighting the complexities and dynamics of the dirty energy market. These insights are especially relevant for investors, policymakers, and stakeholders as they navigate the changing landscape of dirty energy markets in response to fluctuations in oil prices. By identifying the nature of these relationships, this study contributes valuable knowledge to the ongoing discourse on energy sustainability and market integration.
This paper aims to study individual preferences towards ambient air quality improvements in India, through the willingness to pay (WTP) measure. Contingent valuation method is employed to elicit individual WTP for air quality improvements via closed-end double bound questioning technique. Bivariate probit model is estimated based on the data coming from 539 in-person interviews to find key determinants of WTP. Estimation results suggest that place of residence, education, consciousness regarding air pollution, and household income are the key determinants of individual WTP for air quality improvements. Random probit model estimated based on the same data finds the presence of shifting and anchoring anomalies, leading towards bias in the mean WTP estimation from the Bivariate probit model. After correcting those anomalies, the estimated mean WTP is ₹255.69 (or 3.09) per month. This is the first study estimating the bias-corrected WTP for air quality enhancements, covering a vast region of India.
Rapidly industrializing BRICS nations face the dual challenge of development and climate change, requiring effective environmental policies to guide clean energy transitions. This study investigates the long-term, potentially non-linear relationship between renewable energy (RE) and nuclear energy (NE) consumption, economic growth, and environmental health in BRICS from 1993 to 2020. Environmental sustainability is measured using the Load Capacity Factor (LCF), which captures the balance between ecological supply and human demand. Unlike conventional approaches assuming symmetrical effects, we apply an asymmetric model to reveal more realistic dynamics. Standard models found no enduring relationship between clean energy and LCF; however, incorporating asymmetry uncovered significant long-run connections. Declines in both RE and NE consumption significantly harmed environmental health. While increases in RE had no notable effect on improving LCF, this highlights the importance of avoiding setbacks in RE deployment. In contrast, positive changes in NE consumption showed potential to enhance environmental sustainability, underlining its complementary role in the clean energy mix. Economic growth consistently exerted negative pressure on environmental sustainability, emphasizing the persistent challenge of decoupling growth from environmental degradation. These findings suggest that steady RE deployment and the integration of NE are critical for long-term sustainability in developing countries. By uncovering asymmetric effects, this study offers nuanced insights for crafting more effective environmental policies tailored to emerging economies. It emphasizes the importance of policy designs that consider the uneven impacts of energy transitions on environmental outcomes while supporting sustainable development goals.
