Understanding the dynamics of oil price volatility is a growing concern for oil-importing economies like India. Literature has examined the relationship between oil price shocks and economic growth with linear and symmetric assumptions. These assumptions mask the real effects on economic indicators with structural rigidity. This study examines whether oil price shocks cause asymmetric effects on inflation in India, and if so, how these effects differ in the short and long run. The study employed the non-linear autoregressive distributed lag model using monthly data from April 1997 to March 2025 of Brent Crude oil and the Wholesale Price Index of India. The results confirm long-run asymmetry, and positive oil price changes have a stronger and persistent effect on inflation. There is no significant short-run asymmetry observed. The error correction term indicates that 42% of long-run disequilibrium is adjusted within a month. Diagnostics and robustness tests confirmed model stability and prediction accuracy, and the result is not influenced by any structural break shocks. The results emphasize the need for an inflation-forecasting model to integrate the asymmetric transmission and to adjust its trading strategies and the fiscal policy.

Funding shortfalls persist for practices in the most deprived areas, despite capitation formula adjustments. Did deprivation scores predict practice payment trends between 2018-19 and 2023-24? Multivariable analysis of English general practices (2018-19 to 2023-24), excluding practices with <750 patients or average payments >£500/patient, using published data. We fitted a quadratic mixed effects model, using cluster-robust standard errors. The outcome was log-transformed average NHS practice payments per patient (net of deductions/reimbursements). The fixed effects were time (categorical), the Index of Multiple Deprivation (IMD) score (higher score = greater deprivation), and seven covariates (geographical, population or organisational). The random effect was practices' random intercepts. Among 5,726 included practices (85.2% of England's total), median payments increased in nominal terms [8.6%] but decreased in real-terms [-12.6% (consumer price index), -9.0% (health inflation)]. The IMD-payment trend relationship was curvilinear, peaking at IMD 49.8 (1.4% above mean deprivation, IMD 23.2), declining to 0.6% higher at IMD 70. More positive payment trends were associated with the non-London regions, rurality, greater long-term conditions (LTCs) prevalence, and higher baseline payments; less positive trends with more under 16s, larger lists and Personal Medical Services contracts. In interaction models, rurality increased whilst higher LTCs decreased IMD's impact. Deprivation had a positive but diminishing association with payment trends as deprivation increased, moderated by geography and morbidity. Payment uplifts must match inflation. Funding formulas must better compensate for deprivation/morbidity, address the attenuated positive effect of deprivation in high-LTC practices and minimise geographical inequalities.

Amid escalating global food security challenges, global food trade has become increasingly pivotal in securing national food supplies and stabilizing nutritional availability. However, the identification of food export drivers remains hindered by fragmented indicator systems and the limitations of linear causality methods in capturing complex, nonlinear dynamics. In this study, we construct a comprehensive, multidimensional indicator framework encompassing economic, agricultural, and environmental factors, and apply the nonlinear Convergent Cross Mapping (CCM) method to identify robust causal relationships between these variables and global food exports. The results show that economic factors — particularly GDP, GDP per capita, and the Consumer Price Index (CPI) — are the most influential drivers of food exports, while renewable water resources show similarly strong effects and agricultural indicators such as arable land and food production exert moderate effects; in contrast, precipitation shows a weak causal signal. Furthermore, developed countries tend to rely more on economic efficiency and technological advancement, whereas developing countries are more dependent on agricultural production capacity and labor inputs, reflecting significant heterogeneity in export drivers across development levels. This research expands the methodological toolkit for international trade analysis, offers new insights into the causal mechanisms underlying food exports, and provides empirical guidance for tailoring food trade and agricultural policies to development contexts.

We examine the influence of economic policy uncertainty (EPU) on the degree of exchange rate pass-through (ERPT) to domestic prices in India. By analysing data on the nominal effective exchange rate (NEER) from 2003 to 2024, we estimate ERPT to both the consumer price index and the wholesale price index. Employing the autoregressive distributed lag (ARDL) model, while we find that an appreciation of NEER leads to reduction in prices, but on the contrary, with the increase in EPU, it leads to an increase in prices. The non-linear-ARDL estimation reveals an asymmetric ERPT effect with NEER appreciation showing relatively a stronger ERPT effect than depreciation, while both large and small exchange rate changes exhibiting similar pass-through effect on prices. Additionally, employing time-varying parameter vector autoregressions (TVP-VAR) technique, we find that except for four-quarter horizon, the impulse responses for one-, eight- and twelve-quarter horizons of prices to exchange rate shocks remain negative all throughout the period. Moreover, we find that ERPT to domestic prices is similar across optimistic, pessimistic and normal economic outlooks. Our findings emphasise the significance of EPU in influencing ERPT to domestic prices in India, highlighting the imperative for monetary authorities in emerging market economies to consider the role of EPU in the transmission mechanism of exchange rate movements to domestic prices. JEL Codes: E31, F31, C22

Garlic (Allium sativum L.) is a commercially grown spice that plays a significant role in rural livelihoods, providing opportunities for revenue diversification and value-added processing. The present study utilised secondary time-series data to identify growth trends in area, production, and productivity of garlic in India and Himachal Pradesh. A decomposition analysis revealed that the interaction between area and productivity contributed the most towards the change in production in India. In contrast, the area effect was highest in Himachal Pradesh. A seasonality analysis of garlic prices and arrivals in Solan, Kullu, and Ludhiana markets was also conducted, revealing that November, December, and January had the highest seasonal indices for prices, while April, May, and June had the highest seasonal indices for arrivals. India is the second-largest garlic producer globally after China; however, its productivity is comparatively low. Enhancing productivity, therefore, requires improved modern agricultural practices and adequate post-harvest infrastructure.

Urethroplasty is the gold standard for recurrent urethral stricture disease per the 2016 American Urological Association (AUA) guideline. Its sustainability depends on economic viability; however, recent payment cuts and inflationary pressures threaten reimbursement. This study analyzed: (1) trends in Medicare providers performing urethroplasty, (2) urethroplasty procedure volumes, and (3) nominal and inflation-adjusted Medicare reimbursement from 2013-2023, contextualized against comparator urologic procedures. This was a retrospective, cross-sectional study using the Centers for Medicare & Medicaid Services (CMS) Physician Fee Schedule and Medicare Part B Physician and Other Practitioners datasets from 2013-2023. Urethroplasty procedures were identified using CPT codes 53,400, 53,405, 53,410, 53,415, and 53,430. Comparator procedures included Cystourethroscopy with Direct Vision Internal Urethrotomy (DVIU, CPT 52276) and Endoscopic/Robotic-Assisted Radical Prostatectomy (CPT 55866). Reimbursement values were adjusted to August 2025 U.S. dollars using the Consumer Price Index. Urethroplasty provider numbers and procedure volumes increased 33-45% and 36-65%, respectively. Nominal urethroplasty reimbursement remained stable (-2.5% to + 22.8%), while inflation-adjusted reimbursement declined 18-25% across all types. By contrast, DVIU demonstrated declining provider participation (-33.9%) and procedure volumes (-46.5%), with inflation-adjusted reimbursement declining 27.4%. Endoscopic prostatectomy showed growing provider participation (+ 33.4%) and volumes (+ 27.3%), but experienced the most severe nominal (-26.5%) and inflation-adjusted (-43.5%) reimbursement decline of all procedures examined. Urethroplasty faces a distinctive economic burden: rising clinical demand paired with stagnant nominal and substantially declining real reimbursement. While reimbursement erosion affects all urologic procedures studied, the combination of increasing utilization and inadequate payment adjustment is uniquely pronounced for reconstructive urology. Long-term sustainability will require reimbursement models that align payment with evidence-based, high-value surgical care.

Climate policies, energy shocks and spillovers between green and brown stock price indices

The housing price index is one of the key economic indicators reflecting changes in housing prices in a country or region. This index is of critical importance, particularly for investors and financial institutions, in making short- and long-term investment decisions. Various macroeconomic variables affect the housing price index. Therefore, examining the effects of macroeconomic factors on the housing price index is of great importance. In this study, using data from the period 2020:4–2024:12, the effects of macroeconomic variables such as the consumer price index (CPI) and the industrial production index (IPI) on the TR3 region housing price index (HPI3) were analyzed using the ARDL bounds test method. According to the long-term analysis results, the CPI variable has a negative effect on HPI3 at a 10% significance level, and a 1% increase in CPI reduces HPI3 by 0.49%. The IPI was found to have a positive effect on the HPI3 at the 5% significance level, with a 1% increase in the IPI increasing the HPI3 by 7.98%. The error correction coefficient was calculated as -0.17, indicating that short-term imbalances will return to long-term equilibrium within approximately 6 months. In the short-term analysis, the current, one-period, and three-period lagged values of CPI were found to have positive and significant effects on HPI3. These findings reveal that inflation may have a positive effect on housing prices in the short term.

The objective of this research is to analyze the impact of consumer price index, exchange rate, broad money supply, and export on tax revenue in Cambodia. The short-run effects from the ARDL cointegration model suggest that inflation negatively influences tax revenue. Changes in the exchange rate also had a negative effect on tax revenue, although the power of this influence was large. On the other hand, an increase in money supply was realized to improve tax revenue performance as revealed by a positive and statistically significant coefficient of broad money. The empirical results also indicate that the impact of exports on tax revenue was insignificant. The error correction term indicates that the flow adjustment was fast, in that about 83.76% of short-run deviations were corrected in one period, thus enabling the quick restoration to the long-run equilibrium after transient disturbances. In the long-run, only exchange rate and broad money were found to be statistically significant determinants of tax revenue. The relationship was found to be negative for exchange, but positive for broad money.

Global food price shocks (volatility) is an important issue of national and international concern that is attracting debates among development economist; yet is understudied. This study analyzed the effects of global food price shocks on the supply response of Nigerian maize, rice, and wheat. Monthly time-series data from 1981–2023 were collected and analyzed for the study. A weighted FAO-based global food price index was constructed, while Johansen cointegration, Vector Error Correction Model (VECM), and Autoregressive Distributed Lag (ARDL) techniques were employed. The results showed that all variables were integrated of order one and cointegrated, confirming stable long-run relationships. In the short run, global food price shocks significantly increased farmers’ supply response (β = 0.165, p &lt; 0.01), while farm size also had a positive effect (β = 0.119, p &lt; 0.01); the error correction term (−0.487, p &lt; 0.01) indicated that about 49% of disequilibrium was corrected within one period. In the long run, global food price shocks positively affected output of maize (β = 0.165), rice (β = 0.129), and wheat (β = 0.077), whereas rainfall and temperature exerted negative and significant effects across all crops. The study recommended strengthening climate-resilient agricultural investments to complement price-based incentives and enhance staple food production in Nigeria.

India has experienced persistent food price inflation over the past three decades, posing significant challenges for policymakers and consumers alike. This study examines the influence of key determinants, including temperature, rainfall, food subsidies, crude oil prices, the global food price index and money supply, on food price inflation in India. Using monthly data from December 1991 to December 2021, the analysis employs the autoregressive distributed lag (ARDL) co-integration framework and dynamic ARDL simulation technique to assess both short-run and long-run effects. The results reveal that crude oil prices, temperature, rainfall and money supply exert a significant positive influence on food price inflation in the long run, while food subsidies are weakly associated with a reduction in inflation. In the short run, only crude oil prices and rainfall show a significant impact. These findings highlight the complex drivers of food inflation in India and suggest that targeted interventions, such as curbing oil dependency, promoting climate-resilient agriculture and refining food subsidy mechanisms can help stabilise domestic food prices. JEL Codes: B22, C22, E31

This study investigates the efficacy of Dynamic Price Monitoring (DPM) as a policy tool for improving household affordability of critical goods and fostering economic equity in Lusaka, Zambia. The research fills an important gap in the literature: while other studies have shown descriptive connections between price monitoring and welfare outcomes, there is a lack of empirical information on the distributional impacts of DPM across different income groups. An integrated theoretical framework that combines signalling theory and income inequality theory guided this research. The study utilised a quantitative research design, incorporating cross-sectional household survey data from 384 respondents situated in three income-diverse residential neighbourhoods (Woodlands, Chalala, and Zingalume), alongside thirty quarters of secondary price data spanning from Q3 2017 to Q4 2024. The analytical framework integrated treatment-effects estimation via Least Absolute Shrinkage and Selection Operator (LASSO) regression, Difference-in-Differences (DiD) analysis categorised by income cluster, and methodologies for inequality decomposition. We measured the dependent variable, Affordability of Essential Commodities (AEC), by looking at the percentage of household income spent on basic goods. We measured the independent variable, DPM exposure, by using a simulated treatment based on Dynamic Price Index (DPI) volatility thresholds. The Economic Equality Index (EEI), based on Gini coefficients of AEC distributions, was the main measure of inequality. The results show that exposure to DPM leads to statistically significant increases in affordability, with an average treatment effect of 0.1872 (p &lt; 0.01). Distributional analysis shows that the effects were progressive: low-income households had a 3.8 percentage point drop in their essential expenditure burden (from 68.0% to 64.2%), while high-income households saw no meaningful changes. The DiD estimates reveal varied treatment effects, with low-income clusters showing the highest and most significant improvements (coefficient = -0.254, p &lt; 0.001). A coping strategy study shows that low-income households have made big changes to their negative coping behaviours. For example, they skip meals less often (31% less often) and rely less on high-interest informal debt (22% less often). An inequality study shows that the EEI went up by 0.05, which means that spending-based inequality went down by 12.2%. The research finds that DPM is a policy tool that promotes equality and has clear progressive distributional impacts. Suggestions include making the Lusaka Dynamic Price Index a permanent part of the Zambia Statistics Agency; adding DPM triggers to social protection programmes that can change over time; making market regulation stronger by making prices more clear; starting public information campaigns on multiple channels; and setting up a DPM governance framework for ongoing policy learning. These findings enhance theoretical comprehension of information-driven welfare interventions and offer empirical assistance for urban policy formulation in environments marked by price instability and systemic inequality.

The increasing house prices in the UK are affected by factors such as the Pandemic, Brexit, inflation, and monetary policy, among other structural economic shifts. This societal phenomenon has led to a pressing housing affordability crisis due to a conflict between the residents' average income and mortgage affordability, which sequentially affects the decisions of UK residents and real estate investors. A widely recognized indicator of house prices is the HPI (House Price Index), which measures the change in prices of residential properties. This research aims to forecast the Housing Price Index in the UK by leveraging the ETS model and the ARIMA model. The first step in this process is to conduct a detailed analysis of the HPI dataset retrieved from the UK Office for National Statistics and HM Land Registry, spanning from 1968 to 2025. As a result, the forecast showcases that the SARIMA(1,1,3)(1,0,0) model with drift is the best for forecasting the UK HPI from 2010 to 2025. This study has provided an excellent statistical forecast model in order to gain a nuanced understanding of future house price index fluctuation within the United Kingdom and hoped that this can provide valuable insights for both local residents and financial institutions, as well as real estate agencies, with regard to risk assessment and investment planning against the structural economic shift.

The paper investigates the correlation of consumer price index with labour force participation and economic growth in India between 2017 to 2024. The analysis involves using secondary data that is sourced from the Ministry of Labour and Employment, the World Bank, the Ministry of Education and Macro trends to explore the effect of the rural and urban labour force participation rates between males and females, their enrolment in secondary schooling, total and planned spending on education and the yearly increase in the GDP on consumer price movements. Since the consumer price index data is non-negative and skewed and the time-series sample used is rather small, Poisson regression is the model used as the major econometric tool. The findings show that the result is high heterogeneity of labour market segments. Male labour force participation in the rural areas has a positive and significant relationship with the consumer price index and a negative significant relationship with female labour force participation. The variables of urban labour force participation are statistically insignificant and there are weaker or less predictable relationships with the price movements. The increase in the gross domestic product is observed to significantly affect the consumer price index negatively, which implies a higher rate of economic growth could alleviate the inflation pressure. The results reveal the significance of the demographic and sectoral differences in the study of the dynamics of inflation and offer policy-related details on labour participation and growth in the determination of price stability.

The article conducts a correlation-regression analysis of the relationship between the index of the physical volume of gross domestic product in actual prices and factor variables, such as the agricultural output index, the food production index, and the consumer price index for food products. The calculation of the correlation coefficient showed the closest positive relationship between the index of the physical volume of gross domestic product in actual prices and the agricultural output index (0.81) and the food production index (0.86). The analysis of the econometric dependence and the consequences of the correlation between the index of the physical volume of gross domestic product in actual prices, the agricultural output index, and the food production index allowed us to confirm that the closest relationship exists between the net income received by agricultural enterprises and the indicators of the index of the physical volume of gross domestic product in actual prices and the agricultural output index. The conducted correlation-regression analysis also allowed to confirm the rather high influence of indicators of activity of agricultural business entities on the level of achievement of sustainable development goals. Based on the calculations, a model was generalized, which demonstrates the place of agricultural enterprises in the system of analysis of sustainable development. The proposed model summarizes the components of sustainable development in three main areas: economic, social and environmental, and demonstrates the interdependent relationship between the results of the activities of agricultural business entities and the level of achievement of strategic goals, objectives and indicators of sustainable development. The effectiveness of the implementation of the regional model of agricultural sector management largely depends on the effectiveness of interaction between state authorities, local governments, business entities and civil society. In addition, the model demonstrates the need for constant monitoring of the level of achievement of sustainable development goals at both the state and regional levels.

The distributional impacts of earmarked taxes and co‐payments in healthcare

This research aims to analyze the capital market’s reaction to the announcement of the Sharia Business Unit (UUS) spin-off policy at PT Bank CIMB Niaga Tbk (BNGA) and PT Bank Tabungan Negara (Persero) Tbk (BBTN), as well as to compare the market reaction patterns between the two issuers. This study employs a quantitative approach using the event study method. The market reaction is measured using abnormal returns calculated by the market model, with the Composite Stock Price Index (JCI) serving as a proxy for market returns. The observation period includes five days before and five days after the announcement date. The data used are in the form of daily closing stock prices, which are analyzed to obtain abnormal returns, average abnormal returns (AAR), and cumulative abnormal returns (CAR). The results of the study show that the spin-off announcements trigger different market reactions for each issuer. Shares of PT Bank CIMB Niaga Tbk (BNGA) exhibited a positive and statistically significant market reaction, while shares of PT Bank Tabungan Negara (Persero) Tbk (BBTN) exhibited a negative but not statistically significant reaction. These findings indicate that the capital market does not respond to spin-off policies uniformly, and that investor reactions are strongly influenced by the company’s internal preparedness as well as the clarity of managerial signals.

This study explores the moderating effect of government spending and oil price changes on inflation in Nigeria from 1986 to 2024, using annual data. Inflation is measured by the Consumer Price Index, while the main explanatory variables include the official exchange rate, broad money supply, oil price, real interest rate, and an interaction term between government expenditure and oil price. An ARDL estimation technique was used and the ADF Unit root tests confirm that all variables are non-stationary at levels but stationary at first difference, suggesting they are integrated of order one. The bounds test reveals a long-run relationship among the variables. In the long run, the interaction term has a positive and significant effect on inflation (coefficient = 0.7906), indicating that rising oil prices and government spending together increase inflation. Oil price alone reduces inflation in the long run (-0.5096), while the exchange rate is positively related to inflation (0.3437), and the real interest rate negatively impacts inflation (-0.0325). In the short run, oil price increases reduce inflation, but higher government spending during oil booms reverses this effect. The error correction term suggests that 33% of deviations from equilibrium are corrected annually.

Traditional financial models often fail construction firms by ignoring industry-specific volatility. This study proposes an Explainable AI (XAI) framework to predict Return on Assets (ROA) and Debt-to-Equity (D/E) ratios using KOSPI and KOSDAQ data (2015–2024). It compares a baseline financial dataset (Dataset 1) with an industry-augmented version (Dataset 2) that incorporates the Producer Price Index (PPI) and labor wages. Using ANN, LSTM, and RF, the study found that the RF model achieved the highest predictive power (R2=0.7978). Notably, Dataset 2 improved accuracy significantly, reducing the Mean Absolute Error (MAE) for ROA by approximately 56%. SHAP analysis revealed that rising rebar and steel prices (PPI_RB) negatively impact both profitability and stability. In contrast, the concrete price index (PPI_CO) showed a positive influence, reflecting firms’ ability to manage costs or adjust prices. Crucially, material costs proved far more influential than labor costs. These findings indicate that supply-chain and raw-material-price indicators are salient predictors, suggesting that management may prioritize monitoring and mitigating supply-chain volatility, while causal validation remains beyond the scope of this study. Ultimately, by leveraging an XAI-based interpretation, this study provides a decision-making basis for formulating procurement and hedging strategies using raw material price indices, while also proposing an analytical framework that explains financial performance in the construction industry from a volatility-oriented perspective.

Purpose This study aims to investigate the macroeconomic determinants that influence housing prices in Malaysia and Singapore, two Southeast Asian economies with distinct policy frameworks and housing market characteristics. The research seeks to identify both short-run and long-run dynamics that shape housing market behaviour and to provide comparative insights for policymakers and investors. Design/methodology/approach Quarterly time-series data from 2000 to 2024 are analysed using the Autoregressive Distributed Lag (ARDL) bounds testing approach to cointegration and the error correction model (ECM). The Granger causality test is used to determine the direction of relationships among variables. Housing price index (HPI) serves as the dependent variable, while gross domestic product (GDP), inflation (CPI), interest rate, unemployment rate and money supply (M3) act as the explanatory variables. Findings The empirical results indicate that both economies exhibit long-run cointegration among housing prices and the selected macroeconomic variables. Money supply and GDP growth emerge as dominant long-run drivers in both countries, while inflation and interest rates show mixed short-run effects. Singapore demonstrates a stronger feedback mechanism between housing prices and macroeconomic indicators, whereas Malaysia’s housing market appears more sensitive to monetary expansion and inflationary pressures. Research limitations/implications The findings highlight the need for coherent macroeconomic and housing policies. For Malaysia, stabilising money supply growth and enhancing income dynamics are vital to moderating housing price escalation. In Singapore, maintaining policy coordination between monetary and housing supply instruments remains key to ensuring market stability. Originality/value This study offers one of the few comparative empirical analyses of housing price determinants in Malaysia and Singapore using ARDL and Granger causality. The study contributes to the limited Southeast Asian literature by integrating macroeconomic theory with real estate market evidence to guide housing policy and investment strategies.