The role of carbon price signal in green innovation: Evidence from China.

The carbon emission trading scheme (ETS), a market-based environmental regulation, is critical for in combating climate change in China. According to the Porter effect, ETS can promote green innovation, while the specific impact of carbon price on a company's green innovation, and the role of price signals, remain unclear. This study examines the impact of carbon price on green innovation, utilizing a dataset of A-share listed companies spanning from 2010 to 2020. The difference-in-differences estimation results demonstrate that carbon price significantly increases green innovation, promoting both invention patents and utility model patents. Our findings further indicate that moderating variables such as ESG disclosure, larger institutional investor shareholding, local environmental penalties, price stabilization mechanisms, and free quotas allocation can all amplify the constructive influence of carbon pricing on sustainable innovation. Moreover, heterogeneity analysis suggests that state-owned and large-scale companies, particularly those in the power sector and polluting sector, are more sensitive to price signals impacting green innovation. Additionally, we discovered a spatial externality of carbon pricing on green innovation in a radius ranging from 150 km to 300 km. This paper provides specific policy recommendations to stabilize the carbon price signal.

Design, simulation and feasibility of the innovative agricultural warehouse receipt system through dynamic programming and agent-based models.

To address marketing challenges in the agricultural sector, provide financial support for small-scale farmers over marketing seasons, manage price risks of agricultural products, and enhance the functioning of agricultural mercantile exchanges, it is attainable to implement an efficient and compliant warehouse receipt system (WRS) that aligns with the legal, institutional, social, and economic-financial conditions of a country. The aim of this study is to design, simulate, and assess the feasibility of an innovative WRS in the agricultural sector. To achieve this, a WRS was designed and evaluated for maize crop in Iran. The research methodology of this study is divided into three main parts: design, simulation, and feasibility assessment of the WRS. The design process incorporated the FAO-provided (2009) warehouse receipt system development, considering the experiences of various countries and the institutional and financial regulations specific to Iran. Additionally, a dynamic programming model was used to simulate the system, and an agent-based model was utilized for system feasibility assessment. The study results demonstrated that it is possible to design an innovative and efficient WRS by involving five key actors, including farmers, buyers (maize traders), banks, mercantile exchange market, and the warehouse (governmental institution), and establishing clear communications among them. Based on simulation results using dynamic programming model, it was evident that four parameters, namely the annualized loan interest rate, the valuation coefficient for loan collateral, price volatility of the product over the marketing season, and the warehouse cost-to-product value ratio, significantly impact the adoption of the WRS by farmers. In conclusion, the findings from the agent-based model revealed that setting the annualized loan interest rate at 8%, a collateral valuation coefficient of 85%, price fluctuations over the non-harvest time at 60%, and a warehouse cost-to-product value ratio of 2% can result in the participation of nearly 100% of farmers in the proposed WRS.

From petrodollars to green power: Fiscal stability and green transition in GCC economies.

This study examines the influence of fiscal stability, characterized by fiscal buffers, on the green transition in the Gulf Cooperation Council (GCC) economies from 2005 to 2023. We apply two-way fixed effects, system generalized method of moments (GMM), method of moments quantile regression (MMQR), and panel causality analysis to explore these dynamics. Our findings underscore that a positive fiscal buffer resulting from a significant spread between oil prices and fiscal breakeven levels exerts a substantial positive impact on green transition efforts in GCC countries. The results reveal that countries with higher levels of renewable energy production capacity and urbanization demonstrate more pronounced advancements in green transition initiatives. Conversely, countries grappling with unstable macroeconomic conditions, such as high inflation and external debt, face considerable challenges in achieving significant improvements in green transition outcomes. Additionally, our analysis shows that fossil fuel energy fiscal subsidies negatively influence green transition efforts in the GCC region. Our study emphasizes that policymakers in GCC countries should pursue a dual-pronged strategy: leveraging positive fiscal buffers to diversify their economies in light of potential benefits from high oil prices, and channelling oil and fiscal revenues towards enhancing renewable energy production capacities. This approach aims not only to diversify economic foundations but also to strategically strengthen the infrastructure necessary for sustainable renewable energy transitions in the region over the long term.

The African cigarette price data landscape: an overview of gaps and opportunities.

INTRODUCTION: Africa has the lowest cigarette taxes of any region. Price data are needed to produce evidence that informs tax policy change. OBJECTIVE: To locate and describe datasets that provide information on cigarette prices in African countries and to identify opportunities for expanding the region's cigarette price information base. METHODS: Three data repositories and relevant literature were searched to locate free datasets that provide cigarette prices for African countries. 13 categories of information were extracted from each dataset, including the type of data collected (self-reported vs observational). The number of outliers and summary statistics, for the price of 20 cigarette sticks, was calculated using the most recent data available from each data collection project identified in a country. T-tests were conducted to compare these statistics for the 14 countries with both self-reported and observational price data available. RESULTS: A total of 131 datasets, covering 39/47 African countries, contain information on cigarette prices. Most datasets (N=111/131) provide self-reported information. Only 11 countries have used the three large-scale surveys that repeatedly collect information on smoking behaviour across Africa (Multiple Indicator Cluster Survey, Demographic and Health Survey and STEPwise) to collect information on cigarette prices. No active projects that collect self-reported price data sample from the youth (aged<15). Prices collected through retail observations exhibit fewer outliers than self-reported prices (p<0.05). CONCLUSION: The tobacco-control community should lead the process of using existing data collection efforts to enhance Africa's cigarette price information base. Increasing observational data collection efforts at retailers could improve our understanding of the prices paid for cigarettes in the region.

Database of twitter influencers in cryptocurrency (2021-2023) with sentiments.

OBJECTIVES: With the expansion of social networks such as Twitter, many experts share their opinions on various topics. The opinions of experts, who are also known as influencers, can be very influential. Combining these tweets and the historical prices of cryptocurrencies makes it possible to predict their price trends accurately. A Hybrid of RoBERTa deep neural network and BiGRU has been used for Sentiment Analysis (SA). Sentiments of tweets can be of great help to investors to understand the future behavior of the market and manage the stock portfolio. Unlike the tweets that are only extracted using the cryptocurrency name hashtag, the tweets of this dataset have specialized opinions and can determine the market trend. DATA DESCRIPTION: The dataset created in this research concerns the opinions of more than 52 influencers (persons or companies) regarding eight cryptocurrencies. This dataset was collected through the Apify Twitter API for eight months, from February 2021 to June 2023. This dataset contains five Excel files and tweets, compound score, importance coefficient of each tweet, sentiment polarity, and historical prices of four cryptocurrencies: Bitcoin, Ethereum, Binance, and other information. These tweets cover the opinions of 52 influencers on more than 300 cryptocurrencies, although most comments are related to Bitcoin, Ethereum, and Binance. For this reason, three Excel files containing the historical prices of polarity and compound sentiment related to Bitcoin, Ethereum, and Binance cryptocurrencies have been placed separately in the dataset. The polarity of sentiment in these Excel shows the maximum number of polarities by applying the importance coefficient, which determines the dominant polarity of sentiment related to a particular day for the cryptocurrency.

Soliton wave profiles and dynamical analysis of fractional Ivancevic option pricing model.

This study dynamically investigates the mathematical Ivancevic option pricing governing system in terms of conformable fractional derivative, which illustrates a confined Brownian motion identified with a non-linear Schrödinger type equation. This model describes the controlled Brownian motion that comes with a non-linear Schrödinger type equation. The solution to comprehend the market price fluctuations for the suggested model is developed through the application of a mathematical strategy. The modified Kudryashov analytical method is applied to find the fractional analytical exact soliton solution. The restrictions on the parameters required for these solutions to exist were also the result of this approach. The dynamical insights are examined and significant aspects of the phenomenon under study are discussed through the use of the bifurcation analysis. In the related dynamical system, the phase portraits of market price fluctuations are displayed at equilibrium points and for different parameter values. Additionally, the chaos analysis was carried out to show the quasi-periodic and periodic chaotic patterns. In order to track changes in market price, the sensitivity analysis of the studied model is also looked at and presented at different initial conditions. It was discovered that the model experienced price fluctuations as a result of minute changes in initial conditions.

Methods and data needs to estimate the economic market value of livestock at different spatial scales.

Assessing the economic value of livestock such as cattle, goats, sheep, pigs, chickens and fish can offer information about their financial performance and economic importance at farm, national and global scale. Such information is needed for decision-making surrounding livestock finance, investment and strategic development. The aim of this study was to provide an overview of the key livestock valuation methods and associated data requirements. The study was conducted using a literature review. Five key livestock valuation methods were identified and described: historical costs, net current market value, replacement costs, net present value and cost of production. The findings of this study may be of interest to livestock scientists, veterinarians, policy-makers and other stakeholders who aim to assess the economic value of livestock herds. The Global Burden of Animal Diseases programme relied on the outcomes of this study to identify methods for the estimation of the economic value of livestock at the global scale and for its Ethiopia and Indonesia case studies.

L'estimation de la valeur économique des animaux d'élevage (bovins, caprins, ovins, porcins, poulets et poissons d'élevage) peut apporter un éclairage sur leurs performances financières et leur intérêt économique à l'échelle d'une exploitation, d'un pays ou du monde. Ces informations sont indispensables pour étayer les décisions de financement, d'investissement et de développement stratégique des élevages. L'étude présentée par l'autrice vise à donner une vue d'ensemble des principales méthodes d'estimation de la valeur des animaux d'élevage et des exigences qui leur sont associées en termes de données. L'étude repose sur un examen de la littérature sur le sujet. Cinq méthodes principales d'estimation de la valeur des animaux d'élevage y sont répertoriées et décrites : coûts historiques, valeur marchande courante nette, coûts de remplacement, valeur actuelle nette et coûts de production. Les résultats de cette étude pourraient intéresser les spécialistes de l'élevage, les vétérinaires, les décideurs politiques et d'autres parties prenantes qui cherchent à évaluer la valeur économique des cheptels. Le programme " Impact mondial des maladies animales " s'est appuyé sur les résultats de cette étude pour retenir les méthodes d'estimation de la valeur économique des animaux d'élevage appliquées à l'échelle mondiale ainsi que dans les études de cas conduites en Ethiopie et en Indonésie.

Evaluar el valor económico del ganado, como vacas, cabras, ovejas, cerdos, pollos y peces, puede ofrecer información sobre su desempeño financiero y su importancia económica tanto a nivel de la explotación como a escala nacional y mundial. Esta información es necesaria para la toma de decisiones en materia de financiación, inversión y desarrollo estratégico de la ganadería. El objetivo de este estudio era ofrecer una visión general de los principales métodos de valoración del ganado y de las necesidades de datos conexas. Para ello, se llevó a cabo una revisión bibliográfica y se distinguieron y describieron cinco métodos fundamentales de valoración de la ganadería: costes históricos, valor actual de mercado neto, costes de reposición, valor actual neto y costes de producción. Las conclusiones de este estudio pueden ser de interés para científicos que trabajan en el ámbito de la ganadería, veterinarios, responsables de la toma de decisiones y otras partes interesadas en la evaluación del valor económico del ganado. El programa sobre el Impacto Global de las Enfermedades Animales se basó en los resultados de este estudio para definir métodos de estimación del valor económico del ganado a escala mundial y para sus estudios de caso de Etiopía e Indonesia.

Combating foodborne disease through household food handling behavior improvement: A comparison between education and price interventions.

Foodborne disease cases are increasingly occurring in home kitchens because of improper food handling behavior. Human factors are considered major determinants of such behavior, although economic factors, which have attracted little attention, may also be important influencers. Taking the service time of kitchen towels as an example, we construct a theoretical model to analyze food handling behavior under an economic framework and empirically explore its economic determinants. Empirically, we use a randomized controlled trial (RCT) coupled with pre- and postsurveys in rural China. The RCT intervention includes information with tips for proper kitchen towel use and in-kind subsidies of one, two, or three packs of kitchen towels, which is regarded as a price intervention. We find that information alone and information plus one pack of towels are not enough to stimulate behavior improvement, whereas information plus two or three packs is sufficient. This implies that the quantity of kitchen towels used increases only as the towel price drops below a certain threshold. As an early attempt, we indicate that food handling behavior is economically driven, suggesting that a well-designed policy should combine educational campaigns and appropriate economic incentives to improve such behavior to reduce the risk of foodborne disease.

Short- and long-term impacts of the National Essential Medicines Policy on drug availability, price, and usage in a deprived rural county in southwestern China: an interrupted time series analysis across 8 years.

Background: China's National Essential Medicines Policy (NEMP) has been implemented for over 15 years; yet empirical evidence on its long-term impacts is lacking, particularly in remote and rural regions. This study aims to assess the short-and long-term effects of NEMP on the drug availability, price, and usage in a deprived rural county in southwestern China. Methods: A quasi-experimental design was employed, featuring a single-group pre-and-post comparison. We gathered 74,436 procurement records spanning from 2009 to 2016 from the drug warehouses of local medical institutions. Pharmaceutical data were analyzed quarterly, considering various policy and therapeutic attributes. Fisher's Drug Price Index (DPI-F) was calibrated for the retail and wholesale prices of a consistent collection of 405 medications. We conducted interrupted time-series analysis to examine the immediate and enduring impacts of NEMP's initial (commencing in January 2011) and second (starting from December 2015) stages. Results: After initiation of NEMP, the number of available essential medicines surged by 115 but subsequently faced a steady quarterly decline (-9.1) in township healthcare centers (THCs, primary care). Conversely, county hospitals (secondary care) initially saw a reduction of 40 in drug availability but later exhibited a steady increase (+4.2 per quarter) up to the second-stage NEMP. Regarding price, THCs encountered abrupt (-26.1%/-15.9% in retail/wholesale price) and sustained (-0.2%/-0.3% per quarter) price drops after NEMP. The immediate price change after NEMP in county hospitals were milder but significant in non-essential medicines, and long-term declines were also observed in all drugs. As for total sales, a significant long-term disparity emerged between THCs (+0.9% per quarter) and county hospitals (+3.3% per quarter). Following the second-stage NEMP, retail prices in county hospitals further decreased, although wholesale prices did not; however, following price upward trends were observed in both THCs and county hospitals. Lastly, the influences of NEMP varied across different therapeutical categories of medicines. Conclusion: NEMP has successfully regulated drug prices in primary and secondary healthcare facilities in remote and rural areas, both short-term and long-term. However, a remarkable disparity in medicine availability and utilization was observed between different levels of facilities over time. Continuous monitoring is essential, with increased attention needed on the uneven impacts of the policy on diverse drugs, facilities, regions, and demographics.

Effects of transparent online open procurement on prices, volumes, and costs of medicines: an interrupted time series study in Ningxia, China.

Objectives: To assess the effects of the transparent online open procurement arrangement on the prices, volumes, and costs of medicines in Ningxia, China. Methods: Data were extracted from the Ningxia pharmaceutical procurement platform, covering 16 months of purchase orders (December 2019 to March 2021) prior to the implementation of the transparent online open procurement policy and 20 months of purchase orders after the implementation of the policy (April 2021 to November 2022). Interrupted time series (ITS) analysis was performed to evaluate the effects of the transparent online open procurement policy on the prices, volumes, and total costs of the purchase orders. Results: After implementation of the transparent online open procurement policy, the average price of purchased medicines showed a declining trend by 0.012 Yuan per month, while the total volume of purchase orders declined at a rate by 1.741 million per month measured by the smallest formulation units and the total costs of the purchase orders decreased at a rate by 5.525 million Yuan per month. Conclusion: The transparent online open procurement policy resulted in reduced prices, lowered volumes, and lowered total costs of purchased orders of medicines.

The Russia-Ukraine conflict, soaring international energy prices, and implications for global economic policies.

This study examines the economic impact of soaring international energy prices during the Russia-Ukraine conflict from February 23, 2022, to May 31, 2022. Notably, by applying a CGE model, this study offers insights into energy policies at both macroeconomic and industrial levels, emphasizing the model's utility in analyzing complex economic interactions under geopolitical stress. Findings indicate that: (1) Russia, a critical energy-producing country, faced severe economic setbacks due to sanctions, with its GDP contracting by 5.5 %, household income decreasing by 4 %, and consumer spending dropping by 3.5 %. This was accompanied by a significant reduction in domestic investment by 6 %, a decline in output by 5 %, and a decrease in societal welfare indicators. (2) Other energy-producing countries or regions, such as the Middle Eastern oil-producing countries, Australia, Canada, Mexico, and Southeast Asia, experienced economic benefits from the global energy market's "crowding-out effect." These regions saw an increase in GDP ranging from 2 % to 4.5 %, output growth by 3 %-6 %, and household income and consumption improvements by approximately 3 %-5 %. However, these benefits were tempered by a 1 %-2.5 % decline in domestic investment due to rising local energy costs. (3) Developed and developing regions, suffered adverse impacts, including the US, UK, EU, Japan, China, South Asia, Middle Eastern non-oil-producing countries, and Africa. These regions reported a decrease in GDP by 0.5 %-3 %, a decline in household income by 2 %-4 %, and lower consumption rates by 1.5 %-3.5 %. The economic strain was further exacerbated by an inflation increase of up to 2 % across these economies. This research offers valuable insights for governments and policymakers globally to address the challenges posed by the Ukraine crisis-induced energy crisis, underscoring the need for strategic energy policy adjustments and economic resilience planning.

Over- and underreporting of prices: most hospitals are not compliant with the Hospital Price Transparency Rule.

Concern has been raised about the effectiveness of the Hospital Price Transparency Rule to facilitate a clear understanding of health care prices due to poor reporting by hospitals. However, the relationship between what services the hospital provides and what prices they report is not clear. We assessed reported prices in the Turquoise Health database and compared them at the hospital level with the CMS Provider of Services File to identify if a shoppable service was provided at a hospital. We found significant mismatch between the hospital prices being reported and the services being provided. For example, 56% of hospitals providing at least 1 shoppable service that requires public price reporting did not report any prices. Of hospitals reporting prices, most hospitals (66%) reported prices for only a portion of the services they provide. In addition, 12% of hospitals reported prices for services they do not provide. Only 6% of hospitals had complete concordance with price reporting and services they actually provide. Current compliance enforcement and penalties do not appear to be adequate to achieve the goals of the Hospital Price Transparency Rule.

Does bride price harm women? Using ethnography to think about causality.

Many institutions claim that bride price - where the groom's family transfers wealth to the bride's family at marriage - harms women. Owing to its long-term engagement with communities that practise bride price, ethnography is well placed to identify causal mechanisms at play in this issue, and there is a substantial literature on its effects in a variety of world regions, including Melanesia. Here, we condense this literature, drawing out key causal arguments made about bride price in various Melanesian societies. This reveals a complex, multi-causal picture: rather than being singularly harmful, bride price may involve a mixture of drawbacks and benefits, making it a double-edged sword with contested implications. Bride price may constrain women's options before and during the marriage but also serves as a safety net that enhances their status. Its effects are probably influenced by many other variables, including age, kinship networks and residence structures. These dynamics have been transformed by conversion to Christianity, the (post-)colonial state, market integration, urbanisation and formal education, often yielding ambiguous outcomes. Rather than reducing ethnography to a collection of datapoints, we show that it can serve as a source of verbal arguments that can be used to challenge reductive narratives about sensitive issues and to formulate hypotheses for testing with quantitative data.

Oil price uncertainty and corporate carbon performance: An international investigation.

Drawing on legitimacy theory and stakeholder theory, this study examines whether oil price uncertainty (OPU) affects corporate carbon performance (CCP) in the international context. Based on data extracted from CDP (previously known as the Carbon Disclosure Project), World Bank, and Thomson Reuters Eikon databases, the study's sample consists of 9074 firm-year observations over the period 2011-2018 for all non-financial multinational companies invited to take part in the CDP questionnaire. Using an ordinary least squares regression model, we identify a strong relationship between OPU and carbon emissions performance. Our findings are robust to a battery of sensitivity tests, all of which support our original results. This study contributes new knowledge regarding the influence of OPU on CCP. The results will be of interest to investors and policymakers as they provide a useful basis for understanding OPU and its impact on CCP to promote better decision-making.

Framework for multivariate carbon price forecasting: A novel hybrid model.

The complex characteristics of volatility and non-linearity of carbon price pose a serious challenge to accurately predict carbon price. Therefore, this study proposes a new hybrid model for multivariate carbon price forecasting, including feature selection, deep learning, intelligent optimization algorithms, model combination and evaluation indicators. First, this study collects and organizes the historical carbon price series of Hubei and Shanghai as well as the influencing factors in five dimensions including structured and unstructured data, totaling twenty variables. Second, data dimensionality reduction is performed and input variables are obtained using the least absolute shrinkage and selection operator, followed by the introduction of nine advanced deep learning models to predict carbon price and compare the prediction effects. Then, through the combination of models, three models with the best performance are combined with Pelican optimization algorithm to construct a hybrid forecasting model. Finally, the experimental results show that the developed forecasting model outperforms other comparation models in terms of prediction accuracy, stability and statistical hypothesis testing, and exhibits excellent prediction performance. Furthermore, this study also applies the developed model to European carbon market price prediction and uses the Hubei carbon market as an example for quantitative trading simulation, and the empirical results further verify its robust prediction performance and investment application value. In conclusion, the proposed hybrid prediction model can not only provide high-precision carbon market price prediction for the government and corporate decision makers, but also help investors optimize their trading strategies and improve their returns.

Driving change: Understanding consumers' reasons influencing electric vehicle adoption from the lens of behavioural reasoning theory.

The present study attempts to explore consumer-centric reasons affecting the adoption of electric vehicles (EVs) are investigated using behavioural reasoning theory (BRT). Our study is among the first to examine consumer's EV adoption intention using BRT through the integration of the reasons "for and against" electric vehicle (EV) adoption. On data of 312 urban consumers, second order confirmatory factor analysis (CFA) revealed the existence of underlying reasons and SEM helped in testing the proposed relationships. This study also investigates the interaction effect of financial incentive policy with the consumer reasons on EV adoption. Findings revealed that "reasons for" adoption are environmental concern, perceived technology, and maintenance of knowledge and "reasons against" adoption are scepticism, price, and instrumental utility. Environmental beliefs and values influence the "reasons for" consumer intentions to approve electric vehicle adoption. Financial incentives policy was found significant in dampening the impact of reasons against adoption of electric vehicle. The study delineates the strategies for strengthening the promotion of electric vehicles.

New Evidence of the Impact of the National Drug Price Negotiation Policy on the Availability, Utilization, and Cost of Anticancer Medicines in China: An Interrupted Time Series Study.

Purpose: The increasing global burden of cancer has become a significant challenge for public health. The Chinese government introduced the National Drug Price Negotiation (NDPN) policy with the goal of lowering the prices of innovative drugs and enhancing their accessibility. This study aims to evaluate the impact of the 2021 NDPN policy on the availability, utilization, and cost of anticancer medicines in China. Methods: Data was gathered from 1519 hospitals between April 2021 and December 2022, with a focus on eight anticancer drugs affected by the 2021 NDPN policy. The availability, Defined Daily Doses (DDDs), and cost per Defined Daily Dose (DDDc) before and after the intervention were evaluated through interrupted time series analysis. Results: The NDPN policy resulted in a substantial 5.10% increase in the availability of anticancer drugs (p < 0.001). Utilization also experienced a significant surge, with an immediate increase of 11,254.36 DDDs (p < 0.001) and a monthly increase of 1208.28 DDDs (p < 0.001) following policy implementation. The DDDc decreased by US$ 111.00 (p < 0.001) immediately after the policy. Disparities in regional drug utilization were evident, with higher usage in the eastern region. Conclusion: The 2021 NDPN policy has notably enhanced the availability and utilization of anticancer medications in China while reducing their cost, in line with the policy's objectives. However, continuous monitoring is essential to ensure sustained access and to tackle regional disparities in drug utilization.

Nonlinear Economic State Equilibria via van der Waals Modeling.

The renowned van der Waals (VDW) state equation quantifies the equilibrium relationship between the pressure P, volume V, and temperature kBT of a real gas. We assign new variable interpretations adapted to the economic context: P→Y, representing price; V→X, representing demand; and kBT→κ, representing income, to describe an economic state equilibrium. With this reinterpretation, the price elasticity of demand (PED) and the income elasticity of demand (YED) are non-constant factors and may exhibit a singularity of the cusp-catastrophe type. Within this economic framework, the counterpart of VDW liquid-gas phase transition illustrates a substitution mechanism where one product or service is replaced by an alternative substitute. The conceptual relevance of this reinterpretation is discussed qualitatively and quantitatively via several illustrations ranging from transport (carpooling), medical context (generic versus original medication), and empirical data drawn from the electricity market in Germany.

Navigating food price shocks in a pandemic: Food insecurity and coping mechanisms in Burkina Faso.

Global food prices rose substantially after the start of the COVID-19 pandemic. This paper examines the impact of rising food prices during the pandemic on food security in Burkina Faso. We aim to answer two primary questions. First, how do food price shocks affect household food insecurity? Second, what coping strategies do households adopt in response to these price shocks? Leveraging country-wide high-frequency longitudinal data, we employ household fixed effect models to examine the effects. In the absence of direct information on local food prices, we use household-reported price shocks to capture province-level price increases and show that the results are consistent with national-level price increases. We find significant and immediate increases in food insecurity following the price shocks, and this effect persists for at least two months. The price shocks most acutely affected the poorest households. Furthermore, food insecurity increased more in rural areas than in urban areas. The higher proportion of poorer households in rural areas explains part of this difference. We find that households primarily cope with the shock by relying on increased assistance from relatives in Burkina Faso and abroad. This study is the first to use panel data with household fixed effects to examine the repercussions of the rise in food prices during the pandemic on food insecurity in a developing country and to examine the coping mechanisms employed by households. Given that food prices are likely to remain high globally for an extended period, our findings carry implications for the broader developing world. Furthermore, given the disproportionate effect on the poorest and those living in rural areas, the findings highlight the need for policies to mitigate the negative impacts of the price shocks and enhance overall food security in countries like Burkina Faso.

The Theory of Planned Behavior and Antecedents of Attitude toward Bee Propolis Products Using a Structural Equation Model.

This work examines consumers' perceptions of products containing bee propolis using the theory of planned behavior as a theoretical foundation. As antecedents of attitude, this work employs price fairness, healthiness, eco-friendliness, and ease of use. A survey was issued to participants who had experience using bee propolis products and who were recruited using the Clickworker platform service. In total, 305 valid observations were collected for analysis. This study used a maximum likelihood-based structural equation model to test the research hypotheses and find that price fairness, healthiness, eco-friendliness, and ease of use positively affected attitude. Moreover, the intention to use is positively impacted by attitude, subjective norms, and behavioral control. This research contributes to the literature by demonstrating the explanatory power of the theory of planned behavior with respect to bee propolis products.