Price Variation Research Articles

Barriers to access to antihypertensive medicines: insights from the HEARTS initiative in latin American and Caribbean region.

Hypertension significantly impacts the global cardiovascular disease burden, presenting a pronounced challenge within Latin America and the Caribbean. The Pan American Health Organization's (PAHO) HEARTS initiative endeavours to meet this challenge by enhancing comprehensive cardiovascular risk management, inclusive of improved access to antihypertensive medications. This study scrutinises the challenges and barriers in accessing these medications, which are crucial for effective hypertension management in these regions. The research employed a two-phase approach: an initial analysis of National Essential Medicines Lists (NEMLs) from 22 countries involved in the HEARTS initiative for the presence of antihypertensive medications, followed by an in-depth pharmaceutical market analysis in six selected countries to evaluate the availability, pricing, and procurement practices of these medications. The study revealed notable inconsistencies in the inclusion of recommended antihypertensive medications across NEMLs, particularly the lack of fixed-dose combinations (FDCs). The market analysis brought to light significant limitations in medicine registration and substantial variations in pricing, which adversely impact the accessibility and affordability of essential antihypertensive treatments. Furthermore, an examination of procurement practices identified considerable diversity across countries, highlighting potential areas for optimisation, including the use of the PAHO Strategic Fund. The barriers to accessing essential antihypertensive medications in Latin America and the Caribbean are multifaceted, stemming from outmoded NEMLs, limited market availability of advised medications, and disparate procurement processes. Leveraging pooled procurement mechanisms such as the PAHO Strategic Fund, coupled with vital updates to NEMLs, stands to markedly improve both the accessibility and affordability of these treatments.

Did the entry of the corn ethanol industry in Brazil affect the relationship between domestic and international corn prices?

AbstractThis study investigates the relationship between corn prices in Brazil and the international market, considering a rise in corn ethanol production in Brazil's Midwest region since 2017. Did the entry of the corn ethanol industry in Brazil affect the relationship between domestic and international corn prices? A cointegration analysis was made with a monthly time series from May 2005 to August 2023 controlling for different exogenous events or shocks that may have affected global agricultural markets. The study's findings indicate a stable long‐term relationship between the international and Brazilian corn prices, with changes in international prices being predictors of variations in Brazilian domestic prices, while the opposite was not statistically supported. The study also found no evidence to support the claim that the entry of the corn ethanol industry into Mato Grosso state in 2017 impacted the prices paid to local producers. Therefore, the increase in Brazilian corn prices cannot be attributed to additional demand for corn by local mills, an important result that contributes to discussions such as food versus fuel and indirect land use change.

A multi-objective planning tool for the optimal supply of green hydrogen for an industrial port area decarbonization

This study addresses the challenge of decarbonizing highly energy-intensive Industrial Port Areas (IPA), focusing on emissions from various sources like ship traffic, warehouses, buildings, cargo handling equipment and hard-to-abate industry, typically hosted in port areas. The analysis and proposal of technological solutions and their optimal integration in the context of IPA is a topic of growing scientific interest with considerable social and economic implications. Representing the main novelties of the work, this study introduces (i) the development of a novel IPA energy and green hydrogen hub located in a tropical region (Singapore); (ii) a multi-objective optimization approach to analyse, synthesize and optimize the design and operation of the hydrogen and energy hub, with the aim of supporting decision-making for decarbonization investments. A sensitivity analysis identifies key parameters affecting optimization results, indicating that for large hydrogen demands, imported ammonia economically outperforms other green hydrogen carriers. Conversely, local hydrogen production via electrolysis becomes economically viable when the capital cost of alkaline electrolyser drops by at least 30 %. Carbon tax influences the choice of green hydrogen, but its price variation mainly impacts system operation rather than design. Fuel cells and batteries are not considered economically feasible solutions in any scenario.

Oil Palm in India: Market Arrivals and Price Movements

Oil palm is native to West Africa. It is one of nine major oils traded in the global edible oil. The study focuses on the trends in oil palm arrivals and market prices in India, particularly in Andhra Pradesh and Telangana states. There was a significant increase in annual oil palm arrivals from in Andhra Pradesh and Telangana, with a percentage change of 92.43% and 84.83% respectively. This increase was statistically significant at a 10% level, R² values are 0.9243 and 0.8483 respectively. A linear trend analysis for prices of oil palm from 2010 to 2022 indicated a consistent rise in oil palm prices in both states, with Andhra Pradesh seeing an annual increase of 737.05 Rupees and Telangana 757.72 Rupees. The percentage change in prices was 59.18% and 66.08% respectively, with R² values of 0.5918 and 0.6608. Positive and significant correlation coefficient is observed in both the states. Seasonal variations in prices were also analyzed, highlighting that oil palm prices were typically drop after harvest it is influenced by peak harvesting periods and imports of crude oil from other countries. In Andhra Pradesh, the seasonal price index for oil palm Fresh Fruit Bunches (FFBs) was highest in May and April months, then decreased to a minimum level due to increase in the oil palm arrivals. Understanding these seasonal patterns is crucial for effective decision-making by farmers, traders, and policymakers.

Predicting pharmaceutical prices. Advances based on purchase-level data and machine learning

BackgroundIncreased costs in the health sector have put considerable strain on the public budgets allocated to pharmaceutical purchases. Faced with such pressures amplified by financial crises and pandemics, national purchasing authorities are presented with a puzzle: how to procure pharmaceuticals of the highest quality for the lowest price. The literature explored a range of impactful factors using data on producer and reference prices, but largely foregone the use of data on individual purchases by diverse public buyers.MethodsLeveraging the availability of open data in public procurement from official government portals, the article examines the relationship between unit prices and a host of predictors that account for policies that can be amended nationally or locally. The study uses traditional linear regression (OLS) and a machine learning model, random forest, to identify the best models for predicting pharmaceutical unit prices. To explore the association between a wide variety of predictors and unit prices, the study relies on more than 200,000 purchases in more than 800 standardized pharmaceutical product categories from 10 countries and territories.ResultsThe results show significant price variation of standardized products between and within countries. Although both models present substantial potential for predicting unit prices, the random forest model, which can incorporate non-linear relationships, leads to higher explained variance (R2 = 0.85) and lower prediction error (RMSE = 0.81).ConclusionsThe results demonstrate the potential of i) tapping into large quantities of purchase-level data in the health care sector and ii) using machine learning models for explaining and predicting pharmaceutical prices. The explanatory models identify data-driven policy interventions for decision-makers seeking to improve value for money.

Curriculum learning empowered reinforcement learning for graph-based portfolio management: Performance optimization and comprehensive analysis

Portfolio management (PM) is a popular financial process that concerns the occasional reallocation of a particular quantity of capital into a portfolio of assets, with the main aim of maximizing profitability conditioned to a certain level of risk. Given the inherent dynamicity of stock exchanges and development for long-term performance, reinforcement learning (RL) has become a dominating solution for solving the problem of portfolio management in an automated and efficient manner. Nevertheless, the present RL-based PM methods just take into account the variations in prices of portfolio assets and the implications of price variations, while overlooking the significant relationships among different assets in the market, which are extremely valuable for managerial decisions. To close this gap, this paper introduces a novel deep model that combines two subnetworks; one to learn a temporal representation of historical prices using a refined temporal learner, while the other learns the relationships between different stocks in the market using a relation graph learner (RGL). Then, the above learners are integrated into the curriculum RL scheme for formulating the PM as a curriculum Markov Decision Process, in which an adaptive curriculum policy is presented to enable the agent to adaptively minimize risk value and maximize cumulative return. Proof-of-concept experiments are performed on data from three public stock indices (namely S&P500, NYSE, and NASDAQ), and the results demonstrate the efficiency of the proposed framework in improving the portfolio management performance over the competing RL solutions.

Immigration Narrative and Home Prices

This study characterizes the relationship between U.S. National Home Prices and Immigration Narrative as portrayed on TV news. Integrating Narrative Economics and Natural Language Processing (NLP) sentiment analysis, I analyze a large dataset of 1.96 million TV news transcripts spanning July 2009 to December 2023 to capture the sentiment of the Immigration Narrative. Immigration Narrative Sentiment and U.S. Home Prices are associated. One standard deviation orthogonalized shock to the sentiment Granger-causes a statistically significant and economically meaningful increase in the Case–Shiller U.S. National Home Price. The cumulative increase is equivalent to about 26 percent of the average monthly change during the sample period. Moreover, the effect of a shock to the Immigration Narrative Sentiment on Home Prices is permanent, suggesting that the Immigration Narrative contains fundamental information about Home Prices not captured by standard economic variables. Conversely, there is no evidence that Home Price variation affects Immigration Narrative.

Price discounts and cheapflation during the post-pandemic inflation surge

We study how within-store price variation changes with inflation, and whether households exploit it to attenuate the inflation burden. We use micro price data for food products sold by 91 large multi-channel retailers in ten countries between 2018 and 2024. Measuring unit prices within narrowly defined product categories, we analyze two key sources of variation in prices within a store: temporary price discounts and differences across similar products. Price changes associated with discounts grew at a much lower average rate than regular prices, helping to mitigate the inflation burden. By contrast, cheapflation – a faster rise in prices of cheaper goods relative to prices of more expensive varieties of the same good – exacerbated it. Using Canadian Homescan Panel Data, we estimate that spending on discounts reduced the change in the average unit price by 4.1 percentage points, but expenditure switching to cheaper brands raised it by 2.8 percentage points.

Monetary Policy without Moving Interest Rates: The Fed Non-Yield Shock

Existing high-frequency monetary policy shocks explain surprisingly little variation in stock prices and exchange rates around FOMC announcements. Further, both of these asset classes display heightened volatility relative to non-announcement times. We use a heteroskedasticity-based procedure to estimate a “Fed non-yield shock”, which is orthogonal to yield changes and is identified from excess volatility in the S and P 500 and various dollar exchange rates. A positive non-yield shock raises stock prices in the U.S. and around the globe, and depreciates the dollar against all major currencies. The non-yield shock is essentially uncorrelated with previous monetary policy shocks and its effects are large in comparison. Its strong effects on the VIX and other risk-related measures point towards a dominant risk premium channel. We show that the non-yield shock can be related to Fed communications and that its existence has implications for the identification of structural monetary policy shocks.

Fertilizer prices and deforestation in Africa

This study explores the impact of fertilizer (urea) prices on local deforestation by exploiting the exogenous variation in the world price of urea inclusive of trade costs from the world market to the local market. The empirical analysis relies on a unique geo-coded dataset on the market price of urea across 158 subnational hub markets in 21 African countries and satellite-derived forest loss estimates over the period 2003 – 2012. The results suggest that fertilizer prices positively affect deforestation rate and forest cover loss. On average, a 1 % increase in local urea prices corresponds to 97.65 Ha of forest loss within a particular local market catchment. Specifically, the results reveal a price elasticity of approximatively 1 between urea prices and forest loss. The heterogeneity analysis suggests that the effects of urea prices on forest loss tend to be concentrated in markets with subtropical and tropical ecological features. Furthermore, I provide evidence for the land-conversion mechanism, implying that urea prices are positively associated with the expansion of agricultural land. Estimating an event study around exposure to a sustained jump in urea prices reveals a persistent response of deforestation and forest cover loss to fertilizer prices.

Cost-Utility Analysis of TNF-α Inhibitors, B Cell Inhibitors, and JAK Inhibitors Versus csDMARDs for Rheumatoid Arthritis Treatment.

Rheumatoid arthritis (RA) is a progressive and debilitating disease, causing persistent joint pain that limits daily activities requiring long-term treatment. Newer targeted therapies expand RA treatment options, but their high cost necessitates a focus on cost effectiveness. To address this, we aim to conduct a cost-utility analysis of these newer RA pharmacotherapies to support evidence-based policy decision-making. We analyzed the cost-utility of sequential treatment with TNF-α, B cell and JAK-inhibitors compared with conventional synthetic disease-modifying antirheumatic drugs (csDMARDs) for RA treatment in methotrexate (MTX) nonresponders. We used a Markov model with lifetime horizon and 6-month cycles from an Indian health system perspective. Costs (INR 2022) and quality-adjusted life years (QALYs) were used to determine the incremental cost-effectiveness ratios (ICERs) at a cost-effectiveness threshold of India's gross domestic product (GDP) per capita (2022). We assessed uncertainty using univariate, probabilistic sensitivity, and scenario analyses. Despite additional QALYs, TNF-α, B cell, and JAK inhibitors were not cost-effective for treating moderate-to-severe patients with RA unresponsive to csDMARDs (including MTX) in India, as increased costs outweighed their clinical benefits. ICERs ranged from 10,46,206 to 31,09,207Indian Rupees in the base case analysis, exceeding three times India's GDP per-capita [approximately USD $13,287 to $39,487 and GBP £10,776 to £32,025]. Sensitivity analyses confirmed the results' robustness. Scenario analysis suggested that a cost reduction of over 75% in drug prices could make most of the interventions cost effective compared with csDMARDs. TNF-α, B cell, and JAK-inhibitors are not cost-effective compared with csDMARDs for patients with RA who have not responded to MTX in India at the current prices. Cost-effectiveness estimates were highly influenced by drug pricing variations. Therefore, reducing the prices of these interventions could enhance affordability, potentially leading to their inclusion in publicly funded health programs.

Exchange Rate Pass-Through to Domestic Prices: Is Inflation Targeting an Optimal Policy Choice for Pakistan?

This study investigates the effect of exchange rate dynamics and its impact on consumer prices in Pakistan. Using monthly data for the period 2004- 2022, the Recursive Vector Autoregressive (VAR) technique has been employed for the empirical analysis. The recursive VAR estimation technique yields impulse response functions that demonstrate the incompleteness of exchange rate pass-through (ERPT). The results of the VAR reflect that the consumer prices bring most of the error variance in itself for the period under consideration. The exchange rate has been established to be the second most significant source of variations in consumer prices. The empirical results suggest that the lower ERPT reduces the risk of exchange rate volatility. Hence the monetary authorities may take steps to adopt inflation targeting in Pakistan.

Forecasting urban expansion of Vadodara City, Gujarat (India): An integrated modeling approach with spatial, economic and demographical parameters

Urbanization is the inevitable phenomenon associated with the development of the region. The cities and towns tend to expand their territory exerting pressure on the environment and resources. Often this growth turns into sprawl, owing to irrational planning policies, coarse data, and generalized approaches irrespective of the underlying characteristics of the region. Thus, the need for urban planners is to understand the impact of past policies and their future course. The spatial urban expansion models like Cellular automata (CA) prove to be effective to trace urban development along with predicting the future. It equips the inclusion of the multiple spatial parameters to understand its degree of influence on regional urbanization. The present study focuses on the prediction of urbanization for 2031 in the Vadodara urban development authority (VUDA) region in Gujarat (India) using the cellular automata model. The study includes multiple variables comprising the social, demography, and land price variation along with the physical and site-situation suitability. A total of 32 years of time series dataset for identification of the land use pattern was generated. The result shows the city of Vadodara predominantly service industries centric growth and having industrial development on the peri-urban stretch will push the city limit to expand and according to the calibrated CA model, around 25 sq. km of the area is going to be transformed into the built-up usage in the VUDA region by the year 2031. The expansion of the built-up cover will mostly transform agricultural land, scrubland, and open land usage. Thus, a balanced horizontal versus vertical growth of the city will be required to have sustainable city development.

Cost analysis study of price variation among the various brands of proton pump inhibitors available in Benghazi-Libya

Background: A large proportion of patients in developing countries have to pay out of pocket for their medications. The prices of different brands of the same medications vary considerably and may vary from one community pharmacy to another. This study was undertaken to evaluate the variation in costs of proton pump inhibitors (PPI) and histamine H2-receptor antagonists available in Libya. Methods: Prices of various brands of PPI and various formulations were collected from four community pharmacies in the city of Benghazi. Both cost ratio and percentage variation were calculated and compared for various brands of the same strength and number of tablets, capsules, injections, or syrups. Results: The highest cost ratio and percentage price variation were found with omeprazole 20 mg, followed by omeprazole 40 mg. Other significant cost variations (>100%) were seen with pantoprazole 40 mg, Downoprazol (omeprazole + sodium bicarbonate) 40 mg, and esomeprazole 40 mg. Ampoules of omeprazole, cimetidine, and ranitidine had cost ratios of 1:1.7, 1:1.7, and 1:1.8, and cost variation ratios of 71.4, 66.7, and 75, respectively. Variations in prices of PPI and histamine H2 antagonists from the same manufacturer between different community pharmacies were common. The highest percentage cost variation (100) was seen with omeprazole 20 mg. Conclusions: Due to political instability, the prices of all drugs are controlled by importing private companies and the owners of community pharmacies instead of governmental authorities, that leads to cost variations. Therefore, the health authorities exert strict control on pricing of medications.

Determinants of housing prices: Serbian Cities’ perspective

This study investigates the spatial and temporal dynamics of housing prices in Serbia, addressing the critical need to understand the drivers of real estate prices and their implications for economic and social welfare. Employing a panel data analysis approach on a unique dataset covering 24 distinct urban areas in Serbia from 2011 to 2021, we examine the relevance of diverse economic, demographic, and infrastructural indicators, providing novel insights within a developing country context. Our findings reveal that the housing market stock-flow model effectively predicts housing price appreciation trends, explaining over 60 percent of variation in property prices. Notably, disparities in labour income, captured by average wages and registered employment rates, emerge as significant determinants of real estate prices, underlining socio-economic disparities within Serbian cities. Housing prices exhibit a positive response to the population/housing stock ratio, suggesting higher prices in cities experiencing faster population growth relative to housing supply. Intensified construction is associated with elevated housing prices. Additionally, we find positive association between the inflation variable and housing prices, underlining real estate’s potential as an inflation hedge. Public service provision and infrastructural amenities also emerge as contributors to higher housing prices in urban areas, emphasizing the importance of comprehensive urban planning strategies. Our study contributes to the literature by providing specific quantitative evidence, advancing the understanding of urban housing market dynamics in developing countries. By offering nuanced insights into determinants of housing prices, our research informs policymakers and urban planners seeking to foster equitable and sustainable urban development strategies.

Research on the relationship between global oil prices and economic indicators based on linear regression and ARIMAX models

This report aims to analyze the relationship between global oil prices and various economic indexes by using linear regression and ARIMAX models. This study will predict global oil prices accurately and establish a reasonable system for regulating oil prices. The research uses the statistical approach to predict oil prices based on historical data (including independent variables and dependent variable). The study uses monthly average data of WTI crude oil prices from January 2000 to March 2023 and contains the analysis of various economic indicators such as Consumer Price Index (CPI), Personal Consumption Expenditures (PCE), Employment, Population, and Oil Price. The findings indicate that the linear regression model can explain about 40.89% of the variation in log oil price, with significant negative effects of log_PCE, log_EMPLOYMENT, and log_POPULATION, and a significant positive effect of CPI on log_price. However, there exists the probability that some other factors have impact on oil prices. In this study, the author employ the ARIMAX model with ARIMA(4,1,1) errors, which can describe a relatively good fit and small errors in training set measures. Overall, while the linear regression model partially explains the variability in global oil prices, further analysis on residuals is necessary. The study concludes that the ARIMAX model provides a better approach to capture the time-series nature of the data.

Food prices in New Zealand: implications for feeding people better

ABSTRACT There are ongoing concerns about food security, particularly around food affordability and the nutritional quality of diets in New Zealand. While recent food price inflation has sparked conversations about overall affordability, less attention has been directed toward understanding how variations in price increases across different food categories might impact nutrition and health. Using data from Statistics New Zealand on the prices of 85 food items between 2014 and 2023, we estimated 12-month moving averages of relative real prices of sweetened foods vis-à-vis those of five other food categories: dairy, fruits, vegetables, processed meats, and unprocessed meats. Non-parametric Mann-Kendall tests and linear regressions were used to assess the trends in relative food prices. The data show that sweetened foods have become relatively inexpensive over this period. On the premise that both absolute and relative prices are important in determining dietary choices and that these choices impact health, we consider the possible negative implications of these trends. Possible reasons for the recent trends and policies to meet the challenges they pose are discussed.

Optimal trading with regime switching: Numerical and analytic techniques applied to valuing storage in an electricity balancing market

Accurately valuing storage in the electricity market recognizes its role in enhancing grid flexibility, integrating renewable energy, managing peak loads, providing ancillary services and improving market efficiency. In this paper we outline an optimal trading problem for an Energy Storage Device trading on the electricity balancing (or regulating) market. To capture the features of the balancing (or regulating) market price we combine stochastic differential equations with Markov regime switching to create a novel model, and outline how this can be calibrated to real market data available from NordPool. By modelling a battery that can be filled or emptied instantaneously, this simplifying assumption allows us to generate numerical and quasi analytic solutions.We implement a case study to investigate the behaviour of the optimal strategy, how it is affected by price and underlying model parameters. Using numerical (finite-difference) techniques to solve the dynamic programming problem, we can estimate the value of operating an Energy Storage Device in the market given fixed costs to charge or discharge. Finally we use properties of the numerical solution to propose a simple quasi-analytic approximation to the problem. We find that analytic techniques can be used to give a benchmark value for the storage price when price variations during the day are relatively small.

Ensemble Learning with an Adversarial Hypergraph Model and a Convolutional Neural Network to Forecast Stock Price Variations

Ensemble Learning with an Adversarial Hypergraph Model and a Convolutional Neural Network to Forecast Stock Price Variations

External Adjustment in Commodity Exporting Economies During Energy Price Downturns

ABSTRACT Recent examples of energy price downturns did not alter trade surpluses in commodity exporting countries. Economic theory contends that such developments may indicate that the changes observed in energy prices are perceived as permanent by the market. We perform a trend-cycle decomposition of oil prices and find that permanent shocks account for about 90% of oil price variation. The observed variability of oil prices attributable to permanent shocks may be explained by the increased financialization of oil markets. This finding is in line with the empirical evidence that the current account surplus is generally unaffected by permanent terms-of-trade shocks.