Price Variation Research Articles

Value sharing strategy of digital empowerment high-quality commercialization of agricultural products

Using digital to enable high-quality commercialization of agricultural products, accelerate the transformation of agriculture to market, and transform agricultural products to quality are the key to leverage the upgrading of agricultural industry and meet consumer demand. The implementation of reasonable value sharing is of great significance to ensure the smooth commercialization of high quality agricultural products. This study considers the digital input of multiple subjects, builds a value-added sharing model of the high-quality commercialized value of digitally empowered agricultural products, and uses the Shapley value method to explore reasonable conditions for value-added sharing based on identifying the value-added value of multiple subjects after high-quality commercialization of digitally empowered agricultural products. The three main interests of the study are farmers, logistics companies, and retail enterprises. The findings indicate that various entities involved in the superior commercialization of agricultural products enabled by digital technology will partake in the value-added benefit of such commercialization, and the extent of digital investment made by these entities and the outcomes of their superior commercialization will determine how value-added sharing is differentiated. Farmers and logistics companies are more willing to participate in digitalization when the cost difference that retail companies pay them exceeds the cost change of their digital enabling high-quality commercialization and a reasonable amount of value-added value is obtained. This helps to form a logically closed loop of “digital empowerment — quality enhancing — value increasing” of value-added sharing. To achieve the high-quality commercialization of agricultural products based on the principle of acceptable value value-added sharing, various subjects should develop a fair value value-added sharing plan that takes into account the varying degrees of digital investment and price variations.

What Drives Commodity Price Variation?

Abstract We investigate the importance of time-varying discount rates for commodity prices using an index based on 23 commodities for the period 1959-2024. We show that in commodities markets, unlike other financial markets, time variation in discount rates plays a much smaller role. Instead, prices forecast cash flows as well as discount rates. A high price for a commodity today, measured as a low percentage net convenience yield, forecasts both a high future convenience yield and a low expected return. For longer horizons, variation in percentage net convenience yields seems mainly driven by net convenience yield growth, making commodities much closer to the classical textbook view of price changes representing news about cash flows.

Oil prices and exchange rates causality: New evidences from decomposed oil prices shocks and parametric in quantile analysis

In this paper, the researchers reassess the causality between oil prices and exchange rates by applying the parametric quantile analysis to decomposed oil prices shocks and exchange rates returns data of both low income, emerging and developed oil exporting countries from 1993.11 to 2021.10. Unlike the existing researches using the causality in quantile analysis, our study outcomes support the causal relationship from exchange rates to oil prices shocks at upper and lower quantiles in developed oil exporting countries; this is also true regarding the bidirectional causality observed in low income and emerging oil exporting countries .These findings imply that, important positive and negative oil shocks cause extremes changes in the exchange rate returns of low income and emerging oil exporting countries and reciprocally. However only extreme fluctuations of exchange rate returns of developed oil exporting countries such as Norway and Canada can cause oil prices variations. The results of non-causality at middle quantiles also suggest that the monetary authorities in both developing and developed oil exporting countries resist the exchange rates adjustments when oil prices fluctuations are significant. From these results we recommend sound policies in order to mitigate internal and external shocks during crisis, structural reforms that support diversification of energy production by developing other energy sources and reduce crude oil dependence, as well as the whole economy diversification mostly for developing countries and finally, multiple exchange rates to diversify portfolio and hedge the risks associated to oil prices fluctuations for investors. Keywords: Exchange Rates, Oil Prices Shocks, Quantile Causality.

Is more always better? The returns to alcohol by volume—Evidence from the Austrian “Spirits Trophy 2023”

Abstract Hedonic models that seek to explain the observable variation in wine and beer prices have so far included alcohol by volume (ABV) only as a control variable without paying much attention to the magnitude of the coefficient and without questioning the implicit assumption that the impact of ABV on bottle prices is indeed linear. Using data from the “Austrian Spirits Trophy 2023,” we find the relationship between ABV and bottle prices to be nonlinear, with statistically significant effects observed for linear, squared, and cubic terms of alcohol content. Moreover, we find expert evaluations of spirits to demonstrate a nonlinear relationship with ABV too.

Economics and Equity of Large Language Models: Health Care Perspective.

Large language models (LLMs) continue to exhibit noteworthy capabilities across a spectrum of areas, including emerging proficiencies across the health care continuum. Successful LLM implementation and adoption depend on digital readiness, modern infrastructure, a trained workforce, privacy, and an ethical regulatory landscape. These factors can vary significantly across health care ecosystems, dictating the choice of a particular LLM implementation pathway. This perspective discusses 3 LLM implementation pathways-training from scratch pathway (TSP), fine-tuned pathway (FTP), and out-of-the-box pathway (OBP)-as potential onboarding points for health systems while facilitating equitable adoption. The choice of a particular pathway is governed by needs as well as affordability. Therefore, the risks, benefits, and economics of these pathways across 4 major cloud service providers (Amazon, Microsoft, Google, and Oracle) are presented. While cost comparisons, such as on-demand and spot pricing across the cloud service providers for the 3 pathways, are presented for completeness, the usefulness of managed services and cloud enterprise tools is elucidated. Managed services can complement the traditional workforce and expertise, while enterprise tools, such as federated learning, can overcome sample size challenges when implementing LLMs using health care data. Of the 3 pathways, TSP is expected to be the most resource-intensive regarding infrastructure and workforce while providing maximum customization, enhanced transparency, and performance. Because TSP trains the LLM using enterprise health care data, it is expected to harness the digital signatures of the population served by the health care system with the potential to impact outcomes. The use of pretrained models in FTP is a limitation. It may impact its performance because the training data used in the pretrained model may have hidden bias and may not necessarily be health care-related. However, FTP provides a balance between customization, cost, and performance. While OBP can be rapidly deployed, it provides minimal customization and transparency without guaranteeing long-term availability. OBP may also present challenges in interfacing seamlessly with downstream applications in health care settings with variations in pricing and use over time. Lack of customization in OBP can significantly limit its ability to impact outcomes. Finally, potential applications of LLMs in health care, including conversational artificial intelligence, chatbots, summarization, and machine translation, are highlighted. While the 3 implementation pathways discussed in this perspective have the potential to facilitate equitable adoption and democratization of LLMs, transitions between them may be necessary as the needs of health systems evolve. Understanding the economics and trade-offs of these onboarding pathways can guide their strategic adoption and demonstrate value while impacting health care outcomes favorably.

Abstract 4136705: Complexity and Inconsistency of Echocardiogram Prices at Top-Rated Hospitals in the United States

Background: Recent U.S. policies require hospitals and health insurers to post payer-specific negotiated prices for services. Few studies, if any, have thoroughly investigated the accuracy of these payer-specific prices for individual hospitals. Hypothesis: Top-rated medical centers charge consistent prices for echocardiograms, which are reflected in price transparency files. Aim: To evaluate the concordance of prices of echocardiograms using three datasets: 1) hospital price transparency, 2) payer price transparency data, and 3) Electronic Remittance Advice (ERA) claims data, reflecting actual payments. Methods: We extracted prices for transthoracic echocardiograms (CPT 93306) at 22 top-rated hospitals by US News&World Report from 1/1/2022 to 12/31/2022. We used price transparency data aggregated by Turquoise Health and compared with ERA claims data. This analysis included 9,171 insurance claims, matched to either payer or hospital-reported prices. For each claim, concordance between datasets was determined by evaluating whether each claim payment fell within a $10 margin of a publicly reported payer-specific price. Findings: In the payer and hospital price transparency files, reported prices ranged from $1.3 to $34,424. In the ERA claims data, the median payment for the professional fee was $203 (Q1 = $116, Q3 = $245), and the median payment for the facility fee was $2682 (Q1 = 1829, Q3 = $4531). There was a ten-fold difference in median price between the cheapest and most expensive facility. Only 27.4% of claim payments had a concordant price in the payer price transparency dataset, and 11.9% in the hospital dataset. Conclusion: Deriving accurate price estimates for patients requires precise matching of the insurance plan, place of service, and fee type. This process highlights the complexity of insurance contracting, and it is not surprising that there is some disagreement in prices. Regardless, we are hopeful that highlighting variation and complexity in prices is a step towards a better system of healthcare payments in the United States.

Commercial insurers' market power and hospital prices in Medicaid managed care.

To examine the relationship between insurers' commercial market power and negotiated prices in Medicaid Managed Care (MMC) plans for hospital care. MMC prices from hospital-disclosed price transparency data as of July 2023 compiled by Turquoise Health, insurance enrollment information from the 2021 Clarivate InterStudy enrollment data. Log-transformed linear regression with hospital and procedure fixed effects estimating the within-hospital MMC price variation as a function of insurers' commercial market share quartile and MMC market share for 15 common outpatient hospital services. A total of 39,049 MMC price samples measured at hospital-procedure-MMC insurer level are merged with county-insurer level market share data. Around 25% of price variation in MMC plans are driven by within-hospital factors. Compared with MMC insurers from the lowest commercial market share quartile (<0.8%), those from the highest commercial market share quartile (>17%) are associated with negotiating 4.6% (95% confidence interval: [2.8%-6.4%], p < 0.001) lower MMC prices for outpatient hospital care, including 3.6% (p < 0.05) for medical/surgical procedures, 3.6% (p < 0.01) for radiology, and 6.7% (p < 0.001) for emergency department visits. MMC insurers with substantial commercial market share negotiate lower MMC prices for multiple outpatient hospital services.

Unveiling Extreme Dependencies between Oil Price Shocks and Inflation in Tunisia: Insights from a Copula Approach

We follow a non-linear dynamic correlation approach using a combination of a DCC-GARCH model and a copula model to capture the dependence between oil price changes and inflation in Tunisia. The case of Tunisia is particularly instructive since after having been an exporter and a major producer, it became a net oil importer in the 2000s. The study, based on monthly data spanning decades, selects a Gumbel copula and shows that beyond weak average dependencies, there is a strong correlation between extreme values, suggesting that inflation in Tunisia is more sensitive to extreme (positive) variations in oil prices than to average variations. The implications of these empirical results for economic policy are crucial for the Tunisian economy.Copyright© 2024 The Author(s). This article is distributed under the terms of the license CC-BY 4.0., which permits any further distribution in any medium, provided the original work is properly cited.

Overcoming Product Catalog Challenges in Telecom : A Technical Perspective

This comprehensive article explores the intricate challenges telecom providers face in managing complex product catalogs across multiple customer touchpoints. With the global telecom market projected to reach $2.47 trillion by 2028, efficient catalog management is crucial for maintaining competitiveness and customer satisfaction. The article delves into the multifaceted complexities of telecom product catalogs, including regional pricing variations, dynamic promotional offers, and the need for real-time synchronization across platforms. It outlines key challenges such as catalog complexity, slow data retrieval, frequent updates, third-party integration latencies, high traffic spikes, and campaign management inconsistencies. The article then presents innovative technical solutions and best practices, including the adoption of industry-leading catalog solutions, leveraging ElasticSearch for faster data retrieval, implementing differential processing for updates, preprocessing third-party integrations, utilizing dynamic UI templates, employing advanced campaign management tools, and implementing inventory prefetching. These solutions aim to enhance operational efficiency, improve customer experiences, and accelerate time-to-market in the rapidly evolving telecom industry.

The Impact of Crude Oil Price Shock: Evidence from Bangladesh

This study aims to estimate the impact of crude oil price on the DSEX broad index of the Dhaka stock exchange using the Vector Error Correction Model (VECM) for the study period 2013:01-2022:12. It has decomposed the oil price shock into supply side and demand side oil shock. The empirical result shows that the stock price decreases significantly with crude oil prices driven by the shock in oil production. In contrast, the improvement in global economic activity results in a shock in the oil price which has a significant positive impact on the stock price. Furthermore, the accumulated shock in oil prices has a significant positive impact on stock prices. The variance decomposition under the VEC model shows that the demand shock of oil price contributes more to the variation in stock price for the oil-importing country Bangladesh. Since different oil price shocks have different impacts, the policy maker and investors implement decisions based on the understating of the sources of the shocks.

Commercial inpatient hospital price growth driven by system affiliation and nonprofit-status hospitals.

As policymakers continue to grapple with rising health care costs and prices, understanding trends and variations in inpatient prices among hospital characteristics is an important benchmark to allow policymakers to craft targeted policies. In this study, we provide descriptive trends on variation in inpatient prices paid by commercial health plans stratified by hospital characteristics using data from Health Care Cost Institute's employer-sponsored insured claims data. Our analyses found evidence of considerable variation among inpatient price levels and growth among system affiliation and profitability. Prices among system-affiliated hospitals grew from $14 281.74 in 2012 to $20 731.95 in 2021, corresponding to a 45.2% increase during this period. On the other hand, prices among independent hospitals grew more slowly, from $13 460.50 in 2012 to $18 196.90 in 2021, corresponding to a 35.2% increase. We did not observe a similar trend in growth rates among case mix index by hospital characteristics, implying that differential inpatient price growth is not driven by changes in case mix by hospital characteristics. Heterogeneity in hospital prices and price growth by type of hospital suggests that public and private policymakers aiming to rein in health spending should consider policies that address this variation.

Decarbonization and waste reduction in a disrupted food supply chain: a step towards achieving food sustainability

PurposeThis study aims to reduce carbon emissions and minimize waste in the event of disruptions in a short and fast-food perishable such as fruits, vegetables, packaged food items, etc. supply chain through optimal investment in green and preservation technologies.Design/methodology/approach This study utilized a Hessian matrix approach to optimize decision variables with an objective to maximize the profit function.FindingsThe study demonstrates that investing in both green and preservation technology within a short and fast-food supply chain is highly beneficial for decarbonization and waste reduction and it leads to profit maximization. It has been shown with the help of a numerical experiments with investment in both green and preservation technology that total profit is 3.09% higher than without investment made in either technology.Practical implicationsThis study aids the industry in achieving food sustainability by minimizing waste of perishables and also minimizes carbon emissions which is essential for environmental protection. It assists industries in determining the optimal investment in preservation technology to minimize waste and in green technology to reduce emissions, thereby maximizing profits.Originality/valueThe current study formulates an inventory model that helps in decarbonization and waste reduction in food supply chain with the consideration of machine learning, demand disruption, preservation technology investment, screening of purchased items, waste disposal, a double triangular distribution deterioration rate, green technology investment, carbon emissions from various supply chain activities, carbon tax policy and fuel price variation over time for perishable food products in a two-warehouse system.

House Prices and Rents

Abstract Variation in monthly metro area house prices unrelated to expected rents clouds the information about future rents in price-rent ratios and lagged changes in house prices. The variation in house prices unrelated to expected rents is, however, correlated across areas, and the problem is mitigated by measuring rent growth regression variables net of their monthly cross-section (across-area) means. This control for price variation unrelated to expected rents substantially enhances the information about future rents that we extract from price-rent ratios and lagged changes in house prices.

Enhancing the Reliability of Weak-Grid-Tied Residential Communities Using Risk-Based Home Energy Management Systems under Market Price Uncertainty

This paper evaluates the reliability of smart home energy management systems (SHEMSs) in a residential community with an unreliable power grid and power shortages. Unlike the previous works, which mainly focused on cost analysis, this research assesses the reliability of SHEMSs for different backup power sources, including photovoltaic systems (PVs), battery storage systems (BSSs), electric vehicles (EVs), and diesel generators (DGs). The impact of these changes on the daily cost and the balance of energy source contribution in providing electrical energy to household loads, particularly during power outage hours, is also evaluated. To address the uncertainty of electricity market prices, a risk management approach based on conditional value at risk is applied. Additionally, the study highlights the impact of community size on energy costs and reliability. The proposed model is formulated as a mixed-integer nonlinear programming problem and is solved using GAMS. The effectiveness of the proposed risk-based optimization approach is demonstrated through comprehensive cost and reliability analysis. The results reveal that when electric vehicles are used as backup power sources, the energy index of reliability (EIR) is not affected by market price variations and shows significant improvement, reaching approximately 99.9% across all scenarios.

Predicting Housing Prices Using Supervised Machine Learning Models

Abstract: Variations in housing prices exert a profound impact on economic policies and personal financial decisions. This study aims to delve into the factors that affect housing prices and construct a predictive model using machine learning techniques. Machine learning enables computers to acquire knowledge from data and make predictions or decisions. This study forcasts house prices by analyzing the characteristics of data from Seattle, Washington using machine learning multiple linear regression, polynomial regression, and K-nearest neighbors regression (KNN). The findings of this investigation demonstrate that the polynomial regression model is the most accurate in predicting housing prices and can capture nonlinear relationships in the data more effectively than multiple linear regression and K-nearest neighbors regression. The main factors affecting housing prices are interior living space and the construction and design of buildings. These insights hold potential for enhancing government policies, facilitating effective land use decision-making by planners, and enabling investors to make more informed choices.

Seasonal price variation analysis of palm oil: case study of Nigeria (2016–2022)

To enhance the benefits derived from palm oil processing by the farmers, there is a need for the producers (farmers) and buyers of palm oil to understand the seasonal price for making informed decisions on when to buy and sell the commodity. In order to determine price at the appropriate time of the season, the study looked at the seasonal price variation analysis of palm oil and the relationship between producers and buyers. This was done by understanding buyers’ requirements, producers’ effort, measurements taken, and contracting with buyers’ credit from customers. A multiphase sampling strategy was used. Organized interviews and questionnaires were utilized to collect primary data, while the National Bureau of Statistics provided secondary time series data on the average monthly retail price of palm oil from 2016 to 2022. The need for bio-fuel, production growth, governmental regulations, market dynamics, and the seasonality of palm oil products are then identified by the study as important factors influencing palm oil price fluctuation throughout the year. The study then suggests that there’s a need for economic diversification to reduce reliance on a single sector. This study will serve as a basis for future research on palm oil price variation analysis.

Cost variation analysis of disease modifying anti rheumatic drugs in Indian market

Background: Rheumatoid arthritis (RA) is a chronic inflammatory disease with systemic complications, necessitating treatment to manage inflammation and prevent joint damage. In India, significant cost discrepancies exist among branded formulations of generic drugs, posing a financial burden on patients and impacting treatment adherence. This study focuses on cost differences of disease modifying anti rheumatic drugs (DMARDs) among Indian brands and advocating strict adherence to drug price control order (DPCO) rules and suggests scheduling non-scheduled drugs under DPCO. Methods: This observational study analysed the cost of 6 oral DMARDs across 16 tablet formulations using data from National Pharmaceutical Pricing Authority (NPPA) and DPCO ceiling prices 2024. Number of brands per formulation, cost ratios, percentage variations, and DPCO price violations was analysed. Statistical analysis was performed using Microsoft Excel Office 2021, and Zotero was utilized for managing references. Results: This study highlights significant price variations among DMARDs in the Indian market, with methotrexate 2.5mg exhibiting the highest cost ratio (1:3.63) and percentage cost variation (263.6%). Azathioprine 50mg has the most brands available (23), while sulfasalazine 500mg, sulfasalazine 1000mg, and tofacitinib 11mg are among the formulations with the fewest brands (3 each). Notably, sulfasalazine 500mg and hydroxychloroquine 200mg showed the most frequent instances of pricing violations above DPCO recommendations. Conclusions: Strict regulation for price control and monitoring should be implemented since the DPCO has not yet achieved its goal of enforcing price ceilings, and non-scheduled drugs should be included under DPCO regulations effectively to enhance adherence to RA therapy.

Rapid determination of the geographical origin of kimchi by Fourier transform near-infrared spectroscopy coupled with chemometric techniques.

Determining the geographical origin of kimchi holds significance because of the considerable variation in quality and price among kimchi products from different regions. This study explored the feasibility of employing Fourier transform near-infrared spectroscopy in conjunction with supervised chemometric techniques to differentiate domestic and imported kimchi products. A total of 30 domestic and 30 imported kimchi products were used to build datasets. Three categories of preprocessing methods such as scattering correction (multiplicative signal correction and standard normal variate), spectral derivatives (the first derivative and the second derivative), and data smoothing (Savitzky-Golay filtering and Norris derivative filtering) were used. K-nearest neighbors, support vector machine, random forest, and partial least squares-discriminant analysis were employed. By appropriately preprocessing spectral data, these four methods successfully distinguished between the two sample groups based on their origin. Notably, the k-nearest neighbors method exhibited exceptional performance, accurately classifying the sample groups irrespective of the preprocessing method employed and swiftly achieving this classification. In comparison, classification and regression tree as well as naïve Bayes methods were outperformed by the aforementioned four classification techniques. Particularly, the efficiency and accuracy of the k-nearest neighbors method make it the most recommended chemometric tool for determining the geographical origins of kimchi.

Preposterous Pricing: A Case of the Secondary Market for the Playstation 5

Volatile market conditions, likely caused by the COVID-19 pandemic, have caused a highly active secondary market for the Sony PlayStation 5 on online marketplaces since the console’s launch on 12th November 2020. Those who have purchased a console have found it profitable to resell it at premium prices on online auction websites, such as eBay. This research aims to use eBay’s online marketplace to gauge what factors impact consumers’ willingness to pay for the console in secondary markets. Our results show that aspects of console listings, such as the edition and included accessories, largely explain variation in final secondary market prices. Secondly, sellers’ reputation management does not appear to be crucial for business in online marketplaces.

Value relevance of accounting information in Morocco: impact of profitability and earnings management strategies

PurposeThis study examines the value relevance of accounting information for publicly listed Moroccan companies and analyzes how profitability affects stock market valuation. Additionally, the study explores earnings management strategies used to avoid losses and earnings decreases.Design/methodology/approachEmpirical tests use the Ohlson (1995) Price model to assess the extent to which accounting information explains variation in stock prices for the period 2012–2021. Moreover, the study applies the Burgstahler and Dichev (1997) approach to analyze earnings management strategies used by listed Moroccan companies.FindingsThe findings suggest a positive and significant relationship between the book value, net earnings and the market value of stocks. Additionally, profitability is identified as a crucial factor significantly influencing the relevance of accounting figures. Moreover, the analysis of the distribution of earnings reveals that listed Moroccan companies engage in earnings management to avoid losses and decreases in earnings.Practical implicationsThe findings hold practical significance for investors, corporate leaders, accountants and stakeholders relying on accounting information for decisions. They also offer insights for Moroccan stock market regulators to improve accounting quality and market efficiency.Originality/valueThe study shows the extent to which stock prices reflect the accounting information from an emerging country perspective, contributing to a predominantly unexplored area of literature regarding economies in transition. Moreover, the study offers crucial insights into the influence of profitability on the market value of listed Moroccan companies and sheds light on the utilized earnings management strategies.