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The study investigates the impact of inflation on the variability of relative prices in the context of Lebanon.

Unlike the traditional method, which relies on the variance of cross-sectional price changes measured at specific points in time to gauge the variability in relative prices, we employ a more appropriate approach. Under this approach, we capture the dispersion in relative prices by estimating how widely (or closely) a set of commodity prices drift apart over a span of time, offering a more comprehensive assessment. Firstly, we employ Johansen’s cointegration test on rolling subsamples to determine the number of statistically significant cointegrating vectors among the prices of 12 major commodity groups. Under this approach, an increase in the number of significant cointegrating vectors indicates a reduction in relative price variability, while a decrease suggests the opposite. Subsequently, we employ ordinary least squares regression to analyze how the fluctuations in inflation affect the variability in relative prices. The sample period ranges from December 2007 to April 2021.

The empirical results indicate that there exists a certain threshold inflation rate corresponding to which the variability in relative prices is minimized. More importantly, consistent with the theoretical predictions, the results suggest that it is not inflation per se, but the deviation of inflation from the 3% threshold level in either direction that causes higher dispersion in relative prices.

The empirical findings from this study have crucial implications for the operation of monetary and fiscal policy. In particular, these findings suggest that stabilizing long-term inflation around a certain threshold rate will not only help to anchor inflation expectations effectively but will also minimize the welfare costs associated with inflation.

Given the rising inflationary pressure in the recent past and its welfare costs, the study assumes crucial importance in understating how fluctuations in inflation distort the relative price structure and eventually cause resource misallocations and economic inefficiency.

Several farm safety net strategies are available to farmers as a source of financial protection against losses due to price instability, government policies, weather fluctuations and global market changes. Producers can employ these strategies combining crop insurance policies with countercyclical policies for several crops and production areas; however, less is known about the efficiency of these strategies in enhancing profit and reducing its variability. In this study, we examine the efficiency of these strategies at minimizing inter crop year farm profit variability.

We utilized relative mean of profit and coefficient of variation, to compare counterfactually calculated farm safety net strategies for a sample of 28,615 observations across 2,486 farms and four dryland crops (corn, soybean, sorghum and wheat) in Kansas spanning nine crop years (2014–2022). A no farm safety net strategy is used as the benchmark for every alternative strategy to ascertain whether a policy customization is statistically different from a no farm safety case.

The general pattern of the results suggests that program combination strategies that have a high-profit enhancement potential necessarily have low profit risk for dryland wheat and sorghum production. On the contrary, such a connection is absent for dryland corn and soybeans production. Low-cost farm safety net strategies that enhance corn and soybeans profits do not necessarily lower profit risks.

This paper is one of the first to use a large sample of actual farm-level observations to evaluate how combinations of safety net programs offered under the Title I (PLC, ARCCO and ARCIC) and XI (FCIP) of the U.S. Farm Bill rank in terms of profit level enhancement and profit risk reduction.

In the fashion of Martin (2012), we develop an innovative application to a standard, well-grounded methodology to investigate resilience in two critical dimensions: recovery and resistance. Our novel approach allows us to investigate the resilience performance to the 2008 financial crisis within countries of this macro-region according to their shock isolation and absorptive capacities.

By individually estimating six open economy DSGE models within the Central Europe and Baltic macro-region, we identify the business-cycle-volatility drivers for each country. Then, we use the outcome of our six estimates to conduct a principal component analysis to determine structural common characteristics required to explain economic resilience in the CEB macro-region.

In terms of resilience, Central European economies exhibit quite similar paths in terms of recovery, meaning they have similar economic structures. By contrast, Baltic countries behave differently, being outliers in opposite extreme positions. The contrary occurs for resistance: Baltic countries share a similar ranking, whereas Central European economies exhibit substantial differences.

It is important to acknowledge that a limitation of the analysis is that we explicitly consider each country as a stand-alone open economy which are subject to stochastic disturbances. Precisely, we do not model trade or other interactions across countries within the CEB region and with the rest of the world. Consequently, spillover effects in the aftermath of the shock are not accounted for.

We estimate the relative vulnerability or sensitivity of economies within the macro-region to disturbances and disruptions (resistance) and the speed and extent of recovery from such a disruption or recession (recovery). First, we built two different kinds of measures of resilience by aggregating the estimated parameters through non-centered and centered principal component analysis. Then, we use our model to investigate the relation between financial shock and the economic resilience across the region. The approach can be applied to several case studies, parsimoniously.

This study aims to delve into the mechanisms through which financial statements readability (FSR) may impact the probability of stock price crashes. It specifically examines how information asymmetry and stock liquidity mediate this relationship.

The study uses data from 107 nonfinancial firms listed on the Egyptian Stock Exchange between 2016 and 2019 to investigate the mediating roles of information asymmetry and stock liquidity using structural equation modeling (SEM). To enhance robustness, the author incorporates the Bootstrap method, conducting 5,000 iterations for consistent validation of results.

The findings of this study identify two crucial mediators in the correlation between the readability of financial statements and stock price crash risk. First, information asymmetry partially mediates this association. Complex financial statements allow managers to hide adverse news, thereby increasing information asymmetry. Consequently, investors face challenges in assessing the company’s risk and performance, elevating the probability of stock price crashes when such concealed information is disclosed. Second, the results indicate that stock liquidity plays a key mediating role. Less-readable financial statements hinder stock liquidity, making it more difficult for investors to trade shares efficiently. This reduced liquidity amplifies the influence of negative news, potentially increasing the crash risk. Importantly, our findings demonstrate robustness across various measures, encompassing two readability indicators and two crash risk proxies, validated through both SEM and Bootstrap methods.

Although this research provides valuable insights, it is critical to acknowledge its limitations. The relatively limited sample size may affect the broader applicability of the findings. Moreover, this study was carried out in the Egyptian setting, where financial reporting is conducted in Arabic. This linguistic and cultural specificity could influence the interpretation and generalizability of the findings beyond the Egyptian and Arab contexts. To overcome this limitation, this paper recommends conducting comparative research in diverse linguistic and cultural environments.

The outcomes of this research carry substantial implications for policymakers and regulators, emphasizing the need for ongoing efforts to enhance financial reporting standards. Clear and readable financial reports contribute not only to market transparency but also to the overall stability and resilience of financial markets. Policymakers are encouraged to consider our findings when shaping or revising standards to ensure readability and transparency, potentially reducing the risk of market disruptions. Furthermore, companies should recognize the adverse impact of complex financial reports, prioritizing transparent and readable reporting to foster investor trust and mitigate crash risks.

This research comprehensively analyzes the intricate relationships among FSR, information asymmetry, stock liquidity and stock price crash risk. Focusing on the mediating roles of information asymmetry and stock liquidity, this paper provides novel insights, advancing theoretical understanding and practical implications for risk management and financial reporting. This study expands the current body of knowledge on how FSR is related to the probability of stock price crashes.

This paper aims to compare the efficiency of spatial and nonspatial hedonic price models in capturing housing submarkets dynamics for cities in developing countries. This study expects to contribute to a better understanding of the housing price determinants from both nonspatial and spatial perspectives. In addition, this paper fills a gap in the literature on the study of housing prices from a spatial perspective in Latin American cities.

This study uses a comparative analysis between an ordinary least squares regression and a geographical weighted regression, GWR. The study also assesses the performance of two distinct data sources: the city’s cadastral records and a real estate sales web portal.

The results suggest that compared to the traditional regression model, the spatial regression models are more effective at capturing housing market variations on a fine scale. Moreover, they reveal interesting findings on the spatial varying, sometimes contradictory effects of some housing attributes on housing prices in different areas of the city, suggesting the potential impact from segregation.

The availability of data on housing prices and characteristics in Latin American cities is fragmented and complex. The level of detail, granularity and coverage is not consistent over time. For this reason, this study combines and compares data sets from official and unofficial sources in an effort to close this gap. Likewise, the socioeconomic variables that come from the census must be carefully analyzed, knowing the historical context in which they were constructed, what they represent and their interpretation.

This paper suggests that despite the improvement on the spatial models, the selection of a specific one should always be based on the diagnosis of it as it highly depends on the data used and the objectives of the study.

This study enriches the limited body of literature on spatial hedonic price models of housing in Latin American cities. It also shed light on the importance of spatial approaches to identify complex housing submarkets.

Local food impacts tourist satisfaction and drives the choice of a tourist destination. However, it is not clear which attributes of local food products drive tourists’ preferences. More specifically, little is known about potential segments in the tourist market. Acknowledging consumers’ divergent inclinations, we explore tourist preferences toward local food attributes through a market segmentation approach.

This study uses the Best-Worst Scaling method to examine the preferences of 311 tourists for attributes of local food products that are known to affect their choices. We employ a Latent Class Analysis to identify market segments with distinct preferences.

Results indicate that tourists prioritize taste, quality, authenticity, healthiness, connection to local culture and tradition and environmental friendliness over price, appearance, enhancement of local economy, availability and packaging. We further identify three segments: Sensory Seekers, Cultural Experiencers, and Price Conscious. The largest segment, Sensory Seekers, is driven by taste and quality attributes, while Cultural Experiencers prioritize connection to local culture and authenticity. The Price Conscious segment places a greater emphasis on price than the other segments. Cultural Experiencers demonstrate the highest willingness to pay for local food products.

The positioning of local products should target Cultural Experiencers. Agri-firms and state agencies promoting a tourist destination internationally can also use the connection to local culture and authenticity in their marketing campaigns.

Our research contributes to food marketing literature by showing which local food attributes drive tourist preferences. Moreover, we uncover unobservable heterogeneous preferences among tourists.

The study explores the relationship between self-control and various online promotional methods faced by consumers simultaneously, simulating how consumers with distinct levels of self-control act in the current Chinese Internet market.

The sample was collected from response of participants to the survey containing a self-control test and attitudes towards different promotional methods. This is a quantitative study using regression and analysis of variance (ANOVA).

Initially, the empirical study focuses on discount margin for both price-discounts and full-discounts, which implies that high discount margin has a negative impact on the quality perception of consumers. However, this impact is weakened under full-discounts. Subsequently, the study identifies that the negative influence of high discounts on quality perception is insignificant for the high self-control group facing various promotional activities simultaneously. Furthermore, it is found that consumers with a high level of self-control rely more on their quality perception rather than the discount margin of products.

The study covers self-control of consumers, a variety of promotional methods and simulation of the present Chinese online market, enriching the research topic of consumer behavior as well as Internet marketing.

The purpose of this paper is to describe sustainability of hollow and solid blocks in sub-Saharan Africa.

Indicators of stakeholder value are proposed for measuring block sustainability based on comparisons of user building value price and carbon emissions. Block manufacturing processes in Tanzania and Uganda are described and assessed in this context.

The results from Uganda indicate that there are economic and environmental advantages in using hollow blocks as long as they are produced to statutory compliance levels. However, where blocks are not produced to standard requirements, the results indicate that it is better to use solid blocks. This surprising result seems to indicate that blocks prepared using low additions of cement might have sufficient functional quality for simple residential building applications even though they might not meet current standard strength requirements and have low cement productivity. These results also indicate that the improvement potential indicated previously cannot be realised when hollow blocks are used for simple construction needs.

Clear benchmarks for the best practical level of cement block sustainability seem to be missing. The first reasons is that the lowest acceptable compressive strength has not been defined since standard requirements might not be relevant in the studied context. The second one is that the lowest possible practically achievable cement content with acceptable cement productivity has not been established.

Understanding sustainability can be very difficult and substantial work needs to be done to introduce operational sustainability indicators.

The results contribute to the discussion of understanding, defining and measuring sustainability.

The COVID-19 pandemic was a shock to the food supply chain without modern precedent. Challenges in production, manufacturing, distribution and retailing led to the highest rates of food price inflation in the US since the 1970s. The major goal of this paper is to describe statistically the impact of the pandemic of food price inflation and volatility in the US and to discuss implications for industry and for policymakers.

We use Bureau of Labor Statistics data to investigate food prices in the US, 2020–2021. We apply 16 statistical approaches to measure price changes and volatility and three regression approaches to measure counterfactuals of food prices, had the pandemic not occurred.

Food price inflation and volatility increased substantially during the early months of the pandemic, with a great deal of heterogeneity across food products and geographic regions. Food price inflation was most pronounced for meats, and contrary to expectations, highest in the western US Forecasting approaches demonstrate that grocery prices were about 7% higher than they would have been without the pandemic as of the end of 2021.

The research on COVID-19 and the food system remains in its nascent stage. As findings on food loss and waste, employment and wages, food insecurity and more proliferate, it is vital to understand how food prices were connected to these phenomena and affected. We also motivate several ideas for future work.

House price fluctuations send vital signals to many parts of the economy, and long-term predictions of house prices are of great interest to governments and property developers. Although predictive models based on economic fundamentals are widely used, the common requirement for such studies is that underlying data are stationary. This paper aims to demonstrate the usefulness of alternative filtering methods for forecasting house prices.

We specifically focus on exponential smoothing with trend adjustment and multiplicative decomposition using median house prices for Sydney from Q3 1994 to Q1 2017. The model performance is evaluated using out-of-sample forecasting techniques and a robustness check against secondary data sources.

Multiplicative decomposition outperforms exponential smoothing at forecasting accuracy. The superior decomposition model suggests that seasonal and cyclical components provide important additional information for predicting house prices. The forecasts for 2017–2028 suggest that prices will slowly increase, going past 2016 levels by 2020 in the apartment market and by 2022/2023 in the detached housing market.

We demonstrate that filtering models are simple (univariate models that only require historical house prices), easy to implement (with no condition of stationarity) and widely used in financial trading, sports betting and other fields where producing accurate forecasts is more important than explaining the drivers of change. The paper puts forward a case for the inclusion of filtering models within the forecasting toolkit as a useful reference point for comparing forecasts from alternative models.

To the best of the authors’ knowledge, this paper undertakes the first systematic comparison of two filtering models for the Sydney housing market.