Local Prices Research Articles

Unveiling an asymmetric relationship between global crude oil and local food prices in an oil-importing economy

AbstractRecent swift comovements of local food and global crude oil prices have attracted the attention of policymakers and researchers. To evaluate this relationship, many studies have used time series models to explore global crude oil and local food prices. However, robust research based on advanced nonlinear time series models that incorporate control variables for their formation is lacking. In this paper, nonlinear techniques are applied to assess the asymmetric nexus between Brent oil prices and local retail food prices in Slovakia. To estimate this value, we extend the single-threshold NARDL approach to the MTNARDL model. The nominal exchange rate and industrial production index are used as the control variables. Compared with conventional NARDL models, the MTNARDL model provides a more detailed representation of global oil‒local food price linkages and detects the asymmetric effect of global oil prices on food prices from both long- and short-term perspectives. Interestingly, with respect to long- and short-term food price volatility, changes in response to oil price fluctuations are greatest under a regime with rather a small number of positive and moderate changes.

Data‐sparse price indexes by spatio‐temporal regularization and PCA: An application to the Australian housing market

AbstractWe present two novel approaches to overcome the limitations of data‐sparse local house price indexes and combine them into a single model pipeline that is simple, computationally efficient, and interpretable. The first contribution is a new spatio‐temporal regularization of least squares dummy variable models, such as the repeat sales regression used here. This regularization encodes prior knowledge of the proximity of houses in space and their sales in time. It handles missing values in a natural way. The second is nonlocal regularization using truncated principal component analysis (PCA) applied to the resulting national collection of local price indexes. The PCA loadings show that there are important underlying socioeconomic factors that can be leveraged in the construction of Australian market indexes. This PCA reveals important socioeconomic factors, showing that many local markets can be described by a few broad aspects of the national market, consisting of a general trend that contrasts regions influenced by the mining industry with Sydney and Melbourne, and another trend that highlights lifestyle.

Optimization of Marginal Price Forecasting in Mexico through applying Machine Learning Models

The Local Marginal Price (LMP) represents the value of energy at a specific moment and location, and its proper management is crucial for the development of the country's strategic sectors. This study compares the ADR, RPSG, SARIMA, and LSTM-H models for predicting the LMP, achieving an approximate effectiveness of 88%. By implementing it in 28 nodes of the three interconnection systems (SIN, BCA, and BCS) in Mexico, the results of the enhanced LSTM network analysis are presented through sensitivity analysis and an ensemble with Prophet, yielding the following metrics: MAE: 0.0189, MSE: 0.0101, RMSE: 0.1007, and MAPE: 12.18, at node 05PAR-115 in Hidalgo del Parral, Chihuahua. This model can construct tree diagrams (ADR) that identify the critical variables for predicting the LMP of any node, significantly contributing to the accuracy of predictive analysis models.

Comparing costs associated with management of antibiotic-recalcitrant recurrent urinary tract infections for one year pre- and post-electrofulguration.

To quantify and compare recurrent urinary tract infection costs between 1 year before and 1 year after electrofulguration. Following IRB approval, a well-characterized cohort of non-neurogenic women with >3 symptomatic urinary tract infections(UTIs)/year, a negative upper and lower urinary tract evaluation, and inflammatory bladder lesions (cystitis) on office cystoscopy who underwent fulguration of these lesions was analyzed. Cost of visits, imaging, labs, and medications were summed for 1-year pre- and post-fulguration using the Medicare Physician Fee Schedule, local pharmacy pricing, and institutional expenses. Before fulguration, all patients underwent clinic visit, noninvasive flow study, and flexible cystoscopy, and post-fulguration, 6-week follow-up visit and 6-month cystoscopy. Ninety-threewomen met study criteria (mean age 64), with 100% 1-year follow-up. Before fulguration, 73% of patients used daily antibiotic suppression, 6% self-start antibiotics, and 5% postcoital prophylaxis. Some also used vaginal estrogens (17%), urinary analgesics (13%), and cranberry or d-mannose supplements (7%). At 1 year post-fulguration, 82% had 0-1 infections and no cystoscopy evidence of cystitis, while 14% required additional fulguration for new cystitis sites and recurrent infections. Patients had on average 0.7 infections in the 1-year post-fulguration, which was significantly lower than pre-fulguration (p < 0.05). Mean 1-year pre-fulguration cost was $1328 (median $1071, range $291-$5564). Mean 1-year post-fulguration cost was $617 (median $467, range $275-$4580). On average, post-fulguration costs were $710 lower than pre-EF (p < 0.05). For women with antibiotic-refractory recurrent urinary tract infections and cystoscopy evidence of cystitis, fulguration was associated with a significant reduction in UTI-related costs in the 1-year post-fulguration.

Comparative Statistical Analysis of Changes in Average Asking Prices for Housing in the Administrative Districts of Moscow

The article presents the results of a comparative analysis of the residential real estate market in nine administrative districts of Moscow in 2016 and 2024. The paper focuses on the features of housing price formation and considers factors influencing pricing, including socio-economic conditions and transport infrastructure. The study outlines the role of housing construction and the investment attractiveness of various districts of the capital.The authors used official data from Russia's largest real estate technology service (CIAN) on asking prices for apartments for sale depending on their parameters. The article draws on such indicators as the local average price per square meter of the total area of dwellings and the number of offers of apartments for sale. It is shown that apartment prices in two administrative districts (AO) of Moscow have a stable growth trend in 2024, which was not observed in 2016. It is concluded that the real estate markets in the districts of Moscow in 2016 (exception for the Central AO) were strongly differentiated in price offers in 2024, and, therefore, it is incorrect to use the average price per square meter of Moscow residential real estate as a characteristic of housing quality in 2024.The article lists events that influenced changes in the Moscow residential real estate market without specifying the relative strength of their impact. According to the authors, each event had its causes, but their almost simultaneous appearance led to tension in the housing market for all participants.In conclusion, the significant impact of inflation and the restoration of residential premises (after renting) on the growth of residential property values and the potential for using housing as an investment object is emphasized

Impact of waterborne E. coli outbreaks on local communities: evidence from housing transactions in Michigan

Abstract Waterborne Escherichia coli (E. coli) represents a pervasive water quality problem across the United States. In Michigan, the presence of E. coli has become problematic for many areas where agricultural run-off and ineffective policies have made these outbreaks endemic. Combining the universe of housing transaction datasets from 2009 to 2017 with the State of Michigan water sampling dataset, we investigate and quantify the negative impacts of E. coli outbreaks on local housing prices. Our difference-in-differences model estimates an overall impact of −8.94% for houses in the treatment group relative to the control group. However, this effect is only short term, as sales prices recover after the outbreak has ended.

DYNAMO-HIA: A health impact assessment tool fit for quantifying tobacco control policy

Abstract DYNAMO-HIA is a generic, publicly-available and easy to use software tool for health impact assessments of changes in health risk-factors such as obesity, air pollution, processed meat consumption, physical activity, alcohol taxation, or smoking. The tool uses a graphic use interface, does not require programming skills, and has comparatively modest data requirements. Health outcomes that can be investigated are mortality, life expectancy, healthy-life expectancy, disability and prevalence of various chronic disease such as chronic obstructive pulmonary disease (COPD), cancer, or coronary heart diseases. DYNAMO-HIA can in principle be used for any tobacco control policy or ordinance-e.g. price increases or pictorial health warning labels-that target changes in smoking prevalence or smoking initiation/quitting rates. Past applications of DYNAMO-HIA for tobacco control policy included country studies for Belgium, Mongolia, or Korea and several comparative country studies. In this presentation we will give a general overview into the use of DYNAMO-HIA and show-case a recently published study. This study quantified the reduction in COPD burden that would occur in Italy, England and Sweden over 40 years if tobacco prices were increased by 5%, 10% and 20% over current local prices.

The spillover and contagion effects of sovereign risk on stock markets

This study examines the spillover effects of sovereign risk on international stock markets using a dynamic spatial Durbin model (SDM) on a panel of forty countries from 2009Q1 to 2024Q2. The findings show that an increase in sovereign risk, as measured by CDS spreads, leads to a significant decrease in both local and foreign stock prices, with 0.027 percent reductions across foreign markets on average. These effects are transmitted through geographical, and economic and financial channels, with exchange rates, gross fixed capital formation, and industrial production playing crucial roles in influencing stock market performance. Additionally, the negative (positive) spillover effects from rising gross fixed capital formation and sovereign risk (industrial production) demonstrate how foreign markets are more impacted than domestic ones. The study highlights the interconnected nature of global financial markets, reinforcing the need for coordinated international policy responses to mitigate the transmission of sovereign risk across borders. These findings provide new insights into how global economic factors influence stock market volatility, particularly through specific transmission channels, underscoring the importance of multilateral collaboration in managing sovereign risk.

Dynamic spatial price equilibrium, dynamic user equilibrium, and freight transportation in continuous time: A differential variational inequality perspective

In this paper we provide a statement of dynamic spatial price equilibrium (DSPE) in continuous time as a basis for modeling freight flows in a network economy. The model presented describes a spatial price equilibrium due to its reliance on the notion that freight movements occur in response to differences between the local and distant prices of goods for which there is excess demand; moreover, local and distant delivered prices are equated at equilibrium. We propose and analyze a differential variational inequality (DVI) associated with dynamic spatial price equilibrium to study the Nash-like aggregate game at the heart of DSPE using the calculus of variations and optimal control theory. Our formulation explicitly considers inventory and the time lag between shipping and demand fulfillment. We stress that such a time lag cannot be readily accommodated in a discrete-time formulation. We provide an in-depth analysis of the DVI's necessary conditions that reveals the dynamic user equilibrium nature of freight flows obtained from the DVI, alongside the role played by freight transport in maintaining equilibrium commodity prices and the delivered-price-equals-local-price property of spatial price equilibrium. By intent, our contribution is wholly theoretical in nature, focusing on a mathematical statement of the defining equations and inequalities for dynamic spatial price equilibrium (DSPE), while also showing there is an associated differential variational inequality (DVI), any solution of which is a DSPE. The model of spatial price equilibrium we present integrates the theory of spatial price equilibrium in a dynamic setting with the path delay operator notion used in the theory of dynamic user equilibrium. It should be noted that the path delay operator used herein is based on LWR theory and fully vetted in the published dynamic user equilibrium literature. This integration is new and constitutes a significant addition to the spatial price equilibrium and freight network equilibrium modeling literatures. Among other things, it points the way for researchers interested in dynamic traffic assignment to become involved in dynamic freight modeling using the technical knowledge they already possess. In particular, it suggests that algorithms developed for dynamic user equilibrium may be adapted to the study of urban freight modelled as a dynamic spatial price equilibrium. As such, our work provides direction for future DSPE algorithmic research and application. However, no computational experiments are reported herein; instead, the computing of dynamic spatial price equilibria is the subject of a separate manuscript.

Flammable futures—storylines of climatic impacts on wildfire events and palm oil plantations in Indonesia

Abstract Wildfire events are driven by complex interactions of climate and anthropogenic interventions. Predictions of future wildfire events, their extremity, and their impact on the environment and economy must consider the interactions between these drivers. Economic policy and land use decisions influence the susceptibility of an area to climate extremes, the probability of burning, and future decision making. To better understand how climate-driven drought events and adaptation efforts affect burned area, agricultural production losses, and land use decisions, we developed a storyline approach centered on Indonesia’s 2015 fire events, which saw significant (&gt;5%) production losses of palm oil. We explored analogous events under three warming conditions and two storylines (multi-model ensemble mean climate change and high impact). We employed a model chain consisting of CMIP6 climate modeling to quantify climate change impacts, a wildfire climate impacts and adaptation model (FLAM) to predict burned areas, and the Global Biosphere Management Model (GLOBIOM) to predict resultant production losses and socio-economic consequences in the oil palm sector in Indonesia and, by extension, the EU. FLAM is a mechanistic fire model used to reproduce and project wildfires based on various criteria and input variables; GLOBIOM is a global economic land use model, which assesses land use competition and provides economic impacts based on scenario data. We found that total burned area and production loss can increase by up to 25% and lead to local price increases up to 70%, with only minor differences beyond 2.5 degrees of warming. Our results highlight the importance of considering the interactions of future warming, drought conditions, and extreme weather events when predicting their impacts on oil palm losses and burned area. This study sets the stage for further exploration on the impacts of land management policies on local and international environments and economies in the context of global warming.

The influence of urban green and recreational areas on the price of housing in Zagreb

Urban green and recreational spaces are important for residents and one of the foundations for the functioningof cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit.In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed using a geographic information system. A local hedonic price model was developed. The results show that some of the observed green and recreational areas increase the price of housing due to their proximity, while others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further research is needed on socio-economic indicators and other changes in the region.

Economic analysis of hydrogen production from electrolyzed water technology by provinces in China

A novel model for measuring the economics of hydrogen generation via electrolytic water projects was constructed. The model overcomes the current problem of incomplete and inaccurate assessments of the price of producing hydrogen via water, which are caused by ignoring the indirect carbon costs of different power generation sources in the process of determining the cost of producing hydrogen via water. The model was used to analyze the price of producing hydrogen via water electrolysis and its sensitivity to the electricity costs of hydrogen production and carbon prices in various provinces of China. With the continuing increase in the penetration of novel energy in China’s power system and the gradual decline in electricity prices, the price of producing hydrogen via electrolytic water is expected to be close to or even lower than that of producing hydrogen via coal in the future. Geographical differences also have a significant impact on the price of producing hydrogen, which is typically higher in the southeastern coastal region than in the western region, because of the local price of electricity and the composition of the energy sources. Provinces that have been effective in developing novel energy sources, such as Qinghai, Sichuan, and others, have been effective in the hydrogen energy industry. Sichuan and other provinces with significant new energy development have a clear advantage in the hydrogen industry. Because provinces with low hydrogen production costs can transport hydrogen to provinces with high hydrogen production costs through pipelines, hydrogen pipelines are planned from Shaanxi to Henan and from Xinjiang to Nei Mongol. These study results reveal the relative economic advantages of producing hydrogen via water electrolysis under various energy and electricity price policies and provide new perspectives on China’s energy strategy and the growth of the hydrogen energy sector.

Bilateral Trade Welfare Impacts of India’s Export Ban of Non-Basmati Rice Using the Global Partial Equilibrium Simulation Model (GSIM)

India, the world’s leading rice exporter, banned the export of non-Basmati white rice, accounting for 25% of its total exports (or 10% of the global rice trade). The ban aims to ensure availability to domestic Indian consumers and reduce domestic market prices, impacting global rice market accessibility, consumers, and producers across twelve regions. The study utilized the global simulation model (GSIM) to analyze the effects of trade restrictions on industries. The model uses national product differentiation to assess trade policy changes at global, regional, or national scales. It examined importer and exporter effects on trade values, tariff revenues, exporter surplus, and importer surplus. It found that India’s Voluntary Export Restraint (VER) ban on non-Basmati rice resulted in a higher local price and a negative global net welfare impact of USD 1.7 billion. The losses decreased to USD 1.4 billion when importing countries responded by reducing rice import tariffs by 25% and USD 1.1 billion when importing countries reduced tariffs by 75%. Sub-Saharan Africa, the Middle East, North Africa, and the Gulf Cooperation Council regions were most affected. The study also found minimal impact on consumer surplus in India due to inelastic rice demand.

Economic and environmental considerations for the deployment of industrial very high temperature heat pumps in European markets

Very high temperature heat pumps (VHTHP) have begun to emerge on the market during the past decade, providing an alternative to fuel-fire boilers for generating industrial process heat up to 200 °C. Large temperature lifts are common for VHTHPs, resulting in a reduced coefficient of performance (COP) compared to traditional heat pumps. Thus, the economic feasibility of operating VHTHPs becomes more dependent on local price conditions, and the environmental impact is affected by increased electricity usage. Both the economics and the environmental impact of operating industrial VHTHPs in place of fossil fuel-fired boilers in Europe are studied in this article. Discounted payback periods are estimated for the varying price conditions in Europe, and life cycle assessment tools are used to quantify the environmental impact of operating VHTHPs. Finland, Sweden, and Denmark were found to be ideal countries for VHTHP deployment, as they showcase both favorable economic conditions as well as generally small environmental impacts in the endpoint categories Human health, Climate change, and Resource usage, if local grid electricity is used. The effect on the endpoint category Ecosystem impacts was generally larger for most countries, including the aforementioned three, mostly due to the use of biomass in the electricity grid.

Comercio justo, eje central del desarrollo en productores locales y exportadores de cacao Theobroma cacao L. en el cantón Quevedo

The study was conducted in Quevedo Canton, Los Ríos Province, Ecuador. The objective was to analyze fair trade, a central axis of development for local cocoa producers and exporters (Theobroma cacao L.). Descriptive research methods were applied, along with analytical and deductive approaches. Surveys using a Likert scale were employed. The survey’s reliability was assessed using Cronbach’s alpha coefficient, yielding a value of 0.81 for 14 elements. The variables considered were education level, quality of life, fair trade, and economic development. Statistical analysis was performed using SPSS (Version 22.0). The results demonstrate a positive impact on international, national, and local market prices, contributing to the sustainable development of cocoa producers. Exploring fair trade practices in production reveals significant interest and potential when adopting measures that promote equity and sustainability within the cocoa value chain. Education emerges as a key driver of development through training programs, enabling producers to fully engage in productive, economic, and commercial activities. Ultimately, fair trade practices are closely tied to pricing and productive activities in cocoa cultivation.

An improved convolutional recurrent neural network for stock price forecasting

&lt;p&gt;Stock price forecasting is a challenging area of research, particularly due to the complexity and unpredictability of financial markets. The accuracy of prediction models is influenced by various factors, including nonlinearity, seasonality, and economic shocks. Deep learning has demonstrated better forecasts of stock prices than traditional approaches. This study, therefore, proposed a new approach to improve forecasting system based on an end-to-end convolutional recurrent neural network (CRNN) with attention mechanism. Our approach first investigates local stock price features using 1D convolutional neural network, and then employs a bidirectional long short-term memory (Bi-LSTM) network for forecasting. This model stands out by effectively utilizing contextual data and representing the temporal character of data. The Bi-LSTM is helpful for understanding the history and future contextual information since it uncovers both past and future contexts of stock data. Furthermore, integrating attention mechanism within the CRNN represents a significant improvement. This allows our model to handle long input sequences more effectively and capture the inherent stochasticity in stock prices, which is often missed by traditional models. The effectiveness of our approach is investigated using data on 10 stock indexes from Yahoo Finance. The results show that our method outperforms ARIMA, LSTM, and conventional methods.&lt;/p&gt;&lt;p&gt; &lt;/p&gt;

Exploring the Traditional Foodways for Nutritional Well-Being Amongst Vulnerable Communities: Insights from Ho Indigenous Community of Jharkhand, India

Nutritional well-being of Indigenous Peoples is shaped by foodways through their relationship with culturally vital indigenous foods. An exploratory mixed-methods study was conducted among Ho community of Jharkhand to get an insight into their traditional foodways. Study sites included ten randomly selected villages from three geographically distant blocks of West Singhbhum, including Sonua, Khuntpani and Chakradharpur. Qualitative enquiries included focus group discussions and village transect walk interviews which captured diverse information including food access from natural sources, market access, livelihood sources, social and cultural norms around foods, environmental factors, hygiene and sanitation conditions, and community health. Seasonal market surveys (monsoon and winter) in ten local markets provided information on food diversity and local prices. The findings revealed that foodways of Ho community are based on foraging, hunting, and traditional farming through use of available ecosystem resources. However, their traditional foodways are under threats due to livelihood and nutrition transitions. They have unique food traditions and cultures, yet their meals lack variety in terms of the foods consumed. Their smallholder farming systems are not profitable, pushing them towards rural-to-urban migration. Thus, it is crucial to promote traditional foodways of Hos to support bio-cultural knowledge, food justice and nutrition in this community. Keywords: foodways, Indigenous Populations, food beliefs, lifeways, smallholder farmers

Does accrual information impact municipal bond prices? Evidence from Japan using AI

IMPACT This article explains some aspects of the role of accrual-based information disclosed by local governments, which has been a subject of debate for some time. Using eXplainable AI (XAI), the authors demonstrate that accrual-based information is reflected in local government bond prices in Japan. The article has important findings for practitioners. The first is the pricing process for municipal bonds. This is useful for practices in municipal bond issuance and investment for financial instruments. The second is the applicability of machine learning in bond markets. The findings will also be useful to data scientists and public officers in local governments looking to create value through the use of machine learning (particularly XAI).

Assessing the Asymmetric Effect of Global Climate Anomalies on Food Prices: Evidence from Local Prices

This paper uses time-varying smooth transition autoregressive model to investigate the asymmetric nature of El Niño Southern Oscillation (ENSO) —an exogenous climatic factor—with respect to the nonlinear dynamics of food prices in sub-Saharan Africa. Curating food price series from more than 1100 markets from 36 SSA countries, the study finds that ENSO (linearly or nonlinearly) affects roughly half of food prices considered, with most nonlinear models exhibiting strong asymmetric properties with shock-inflicted persistence. Moreover, in terms of the location of the burden of ENSO impact, I find a geographical and food product divide. Specifically, ENSO appears to be more efficacious on maize prices in Southern, Eastern, and some parts of Central Africa. Conversely, local rice, cassava, millet, and animal products are least affected. The policy implication of this dichotomy is that response to ENSO news should be subregion-specific rather than region-specific, depending on how the subregions absorb the shock.

Limited (energy) supply, monetary policy, and sunspots

In a simple New Keynesian open economy setting, we analyze how local input shortages influence policy transmission and equilibrium determinacy. Shortages increase the elasticity of the local price of the scarce factor to domestic economic activity, affecting the cyclicality of marginal costs and incomes. As a result, the slope of both the Phillips and the IS curve is altered, crucially influencing monetary and fiscal policy transmission. These changes are affected by factor ownership and propensities to consume. Theoretically, shortages can also raise the risk of self-fulfilling fluctuations if a rising price of the constrained factor boosts incomes for agents with high propensities to consume. We illustrate these channels for the 2022 German energy crisis.