Price Setting Research Articles

Analyzing The Effect of Body Size and Body Weight of Local Horses on Their Selling Price at The Tolo Horse Market, Jeneponto

Determining the selling price of horses in the Tolo Horse Market has been using the estimated system due to the limitations of weighing equipment. So, it is necessary to research the accuracy of determining the price seen from the body size and body weight of livestock and its effect on the selling price of local horses. This research was conducted in Tolo Horse Market. This research used a mixed method with a convergent parallel design approach. The results showed that the size of body length and chest circumference of horses is relatively more varied than waist height and pelvic height. Chest circumference and body length significantly (p<0.05) affect the selling price of horses in Tolo Horse Market. In conclusion, the selling price of local horses in the Tolo horse market is significantly influenced by body size, namely chest circumference, body length, and body weight. In comparison, the hip height and pelvic height of horses do not affect the selling price of horses. The ability to estimate the price is quite reasonable based on the linearity between the body weight of the research results and the selling price set. However, the use of measuring instruments is still recommended for accuracy.

Training on Calculation of Production Cost and Determination of Product Selling Price to Increase Competitiveness of MSMEs in Alai Village

Micro, Small and Medium Enterprises (MSMEs) play a crucial role in the Indonesian economy, including in job creation, improving people's welfare, and strengthening the national economic structure. One effective solution to address these challenges is through training on cost of goods produced (COGS) calculation and selling price determination. This study focuses on the training provided to MSMEs in Alai Village, with the aim of improving the understanding of business actors regarding the components of COGS and effective selling price setting strategies. The method used in this research is the field practice method which goes through three stages, namely preparation, implementation and evaluation. The results showed that participants gained better knowledge about COGS calculations and were able to set competitive selling prices, thereby increasing operational efficiency and profit margins. This was proven when researchers conducted periodic research at 2 stages, namely before the presentation of the material (pretest) and after the presentation of the material (posttest). Thus, this training makes a positive contribution to business sustainability and the economic welfare of local communities, increases the competitiveness of MSMEs, and has a positive impact on local economic growth

Analysis of Promotion Strategies through the Addition of Play Areas with Cats at Mostcat Cafe Batam

Mostcat Cafe is the first cafe in Batam that integrates the concept of direct interaction with cats. Adopting the "cat cafe" model popular in Taiwan since 1998, this cafe offers a relaxing experience while enjoying food and drinks, as well as the opportunity to interact with various breeds of cats. This study aims to analyze the marketing strategy implemented by Mostcat Cafe, including the Marketing Mix elements: product, price, promotion, and location. The method used is a qualitative approach with in-depth interviews and field observations. The results showed that the addition of a play area with cats increased customer loyalty and frequency of visits, and the price set was in line with the experience provided. Mostcat Cafe succeeded in creating a solution for cat owners and providing a space for cat lovers to gather, thus strengthening the cafe's position in the thematic culinary industry in Batam. This research is expected to provide valuable insights for similar business owners in developing effective marketing strategies.

Pricing of display advertising as indication of the roots of market power of the digital platforms

Multi-sided digital platforms such as GAFAM (Google, Apple, Facebook, Amazon, Microsoft) provide services at zero cost by monetizing user attention and data through advertising. Testing the hypotheses on the determinants of display advertising price set by three largest digital platforms — Google, YouTube and Facebook, — contributes to the explanations of the roots of platforms market power. During the period from January 2018 to March 2024 cost per mile (CPM) increases with the number of unique visitors and traffic (total visits), as well as with the time that the user spends on the platform. However, the second effect is more pronounced. We interpret this result as an evidence that the market power in digital advertising depends more on the data of particular user’s preferences (digital footprint) and therefore on the ability to personalize advertising messages than on the number of users and data traffic on the platforms. The results high-light the economic value of personal data under monetization of cross-platform externalities through digital advertising.

The hockey stick Phillips curve and the effective lower bound

We show that the interplay between a binding effective lower bound (ELB) on nominal interest rates and the costs of external financing weakens the disinflationary effect of financial shocks. In normal times, the real costs of production factors dominate in firms' marginal costs and are therefore key for inflation dynamics. In contrast, financing costs normally play a subordinate role as higher credit spreads are balanced-out by lower nominal rates. At the ELB, however, higher spreads following financial shocks can offset the effect of lower production factor costs on firms' price setting. The relationship between inflation and output hence features a hockey stick shape: the Phillips curve is flat at the ELB, but conventionally upward-sloping during normal times. This mechanism also weakens the power of forward guidance at the ELB, since such policy reduces spreads and financing costs.

Forecasting Digital Asset Return: An Application of Machine Learning Model

ABSTRACTIn this study, we aim to identify the machine learning model that can overcome the limitations of traditional statistical modelling techniques in forecasting Bitcoin prices. Also, we outline the necessary conditions that make the model suitable. We draw on a multivariate large data set of Bitcoin prices and its market microstructure variables and apply three machine learning models, namely double deep Q‐learning, XGBoost and ARFIMA‐GARCH. The findings show that the double deep Q‐learning model outperforms the others in terms of returns and Sortino ratio and is capable of one‐step‐ahead sign forecast of the returns even on synthetic data. These critical insights in forecasting literature will support practitioners and regulators to identify an economically viable cryptocurrency forecasting return model.

Linking Non-Financial Performance Determinants with IPO Underpricing: Evidence from Indonesia

Study Background/Rationale: This study aims to investigate the determinants that may influence the underpricing in Initial Public Offerings (IPO), a phenomenon found in stock exchange markets globally. Underpricing occurs when the intrinsic value is higher than the offer price set by the issuing firm. As a result, the share’s price skyrockets upon its market debut, offering an instant profit to investors, but potential damage to the company itself. Method: The focus of this study is the Indonesian stock market, which is seen as one of the leading growth economies among emerging markets and other developing economies, with accelerated GDP and having topped the list of Southeast Asian (ASEAN) countries regarding the number of IPOs. This research utilises multiple linear regression models and samples of firms that engaged in IPO in the Indonesia Stock Exchange (IDX) between 2018 and 2022. Findings: Using prospectus’ non-financial performance determinants, this study finds that underwriter reputation, public ownership, and firm size have a positive influence on underpricing; additionally, offering size and firm age have a negative and statistically significant influence on underpricing. Furthermore, this study adds to the existing research by emphasising how various determinants are linked to IPO underpricing in Indonesia. In addition, this study demonstrates that prospectuses provide relevant information to minimise information asymmetry and ex-ante uncertainty between firms and shareholders. Conclusion: Considering the present shortage of studies on the determinants of IPOs underpricing as a starting point, this empirical research can be valuable since it could enhance the existing research by providing latest data for comparisons across nations.

CANVAS: A Canadian behavioral agent-based model for monetary policy

We develop the Canadian behavioral Agent-Based Model (CANVAS) that complements traditional macroeconomic models for forecasting and monetary policy analysis. CANVAS represents a next-generation modeling effort featuring enhancements in three dimensions: introducing household and firm heterogeneity, departing from rational expectations, and modeling price and quantity setting heuristics within a production network. The expanded modeling capacity is achieved by harnessing large-scale Canadian micro- and macroeconomic datasets and incorporating adaptive learning and simple heuristics. The out-of-sample forecasting performance of CANVAS is found to be competitive with a benchmark vector auto-regressive (VAR) model and a DSGE model. When applied to analyze the COVID-19 pandemic episode, our model helps explain both the macroeconomic movement and the interplay between expectation formation and cost-push shocks. CANVAS is one of the first macroeconomic agent-based models applied by a central bank to support projection and alternative scenarios, marking an advancement in the toolkit of central banks and enriching monetary policy analysis.

Do commodity prices matter for global systemic risk? Evidence from ML variable selection

We identify robust predictors of global systemic risk proxied by conditional capital shortfall (SRISK) among a comprehensive set of commodity prices for the period between January 2004 and December 2021. The search is based on a battery of ML variable selection algorithms which apply both to price levels and price shocks in the presence of control variables, including the first lag of SRISK, world industrial production, global economic policy uncertainty, geopolitical risk as well as the global stance of monetary and macroprudential policies. We find that these controls outweigh commodity prices as the predictors of global systemic risk. Of the commodities themselves, the prices for agricultural commodities, including food, e.g. chicken, bananas, beef, tea, cocoa, are more important predictors of global systemic risk than the prices for energy commodities, e.g. natural gas and oil prices. The financialization of agricultural commodities, bio-energy expansion as well as commodity-specific dependence of the major economies contributing to global systemic risk, e.g. China, account for our main finding. We also document the positive linkage between commodity prices and systemic risk for the majority of commodities. Thus, monitoring commodity prices to avoid their unbalanced growth is of vast importance to curb global systemic financial risk.

Price Setting and Price Stickiness: A Behavioral Foundation of Inaction Bands

Abstract This paper puts forward a behavioral theory of price setting where managers maximize perceived profits following a process of mental accounting. The theory predicts a pricing rule that is similar to—but crucially different from—that of a standard menu cost theory: there is an inaction band, but there are two rather than just one target prices, depending on whether the firm updates its price upwards or downwards. The calibrated model replicates two patterns of price microdata that standard menu cost models have difficulty accounting for: (i) The distribution of price changes has both small and large price changes, and (ii) the hazard function of price changes is downward sloping initially, that is, firms that have just recently changed their price have a higher probability of changing it again, while this probability becomes constant thereafter.

Do firms submit fewer tender bids with high inflation? An analysis of firms’ participation in public procurement in the EU

PurposeThis paper aims to explore the effect of sustained high inflation on public procurement participation in the European Union, both in terms of the average number of bids submitted and in the proportion of bids by small- and medium-sized enterprises (SMEs) out of all submitted bids.Design/methodology/approachRegression modelling, using contract award notices in the Tender Electronics Daily database between 2018 and 2022, at quarterly intervals.FindingsEach inflation point increase is associated with a decline in the average number of offers received per tender by 0.43%. A more marked reduction of 8.6% in the average number of offers and a decrease in 3.4 percentage points in the SME participation rate are observed for firms operating in sectors that experienced very high levels of inflation (>20% year-on-year rate of change), compared with a situation of low inflation (0–5%).Social implicationsClaims about difficulties in delivering public contracts for the set price should be taken with a grain of salt, unless businesses operate in sectors experiencing very high inflation levels. Measures to foster competition could also reduce price pressures.Originality/valueTo the best of the authors’ knowledge, this is the first study to quantitatively assess the association between high inflation and public procurement participation. Two methodological novelties are introduced: the operationalisation of sectoral-level inflation down to two-digit NACE codes, based mainly on producer prices; and a matching between two-digit NACE codes for inflation and the common procurement vocabulary codes to classify calls to bid for public contracts by economic activity.

Theoretical study on demand-side management to reduce imbalance between electricity supply and demand

The gap between electricity supply and demand, which is the electricity imbalance, can have negative economic impacts on retail electricity suppliers. This paper presents a methodology for designing demand response (DR) programs while mitigating the negative impacts. The DR programs require the retailers to set target electricity consumption and prices of economic incentives for customers who cooperate with the DR programs. First, the target electricity consumption is determined by solving a problem of social welfare maximization in which the objective function is the sum of economic surplus of the retailers and their customers and impacts of electricity imbalance. The optimal electricity prices or the optimal rebate levels are then calculated under the principle that rational customers act to maximize their economic surplus. Through numerical simulations, the authors confirmed that the proposed methodology mitigated the impacts of electricity imbalance. Meanwhile, results of the numerical simulations showed that in practical situations, there were cases where the retailers unable to reduce the electricity imbalance profitably. Therefore, the authors additionally discuss ways to assess the electricity imbalance, which motivates the retailers to reduce the imbalance, in such cases.

Akuntansi Belis dalam Adat Perkawinan Masyarakat Kabupaten Sikka

This research aims to describe the identification, measurement, presentation and disclosure of belis accounting in the marriage customs of the Watugong Village community. This research data was obtained from interviews, observations, documentation and literature studies regarding the stages of belis in the marriage customs of the Watugong village community. This research produces data that can be used as information and consideration in making decisions regarding accounting. The results of this research: identification in Accounting is said to be relevant because in the buying process economic transactions can be identified; This research shows the process of measuring belis, measured using fair value, which is in accordance with the market price set at that time; The researcher presents a journal description and a summary of profits or losses from both parties, which can be used as reference material for future purchasing processes, documentary evidence and economic considerations.

The impact of energy prices in decarbonizing buildings’ energy use in the EU27

The recent surge in end-use energy prices in the EU has intensified the debate on how price signals influence the decarbonization of the building sector, a key contributor to global energy consumption and emissions. This study investigates the role of energy price signals in driving the decarbonization of the EU-27 building sector using the Invert/EE-Lab model to simulate various scenarios up to 2050. The analysis examines the elasticity of energy demand, particularly how different price scenarios impact investment decisions in energy-related technologies. The findings reveal that a 10 % increase in energy prices can lead to a reduction in energy demand by up to 3 %, depending on the scenario. Furthermore, price elasticity varies across different policy and price settings, with stricter regulations resulting in lower elasticity. The results underscore that while stringent regulatory measures can achieve significant energy savings and reduce greenhouse gas emissions, they also limit the responsiveness to price signals, with heat pump shares reaching 47–86 % in high-efficiency scenarios. Conversely, more moderate policy measures increase price elasticity, leading to 15–55 % heat pump shares. This study highlights the importance of better understanding price elasticity in energy modeling to project better the outcomes of policy interventions aimed at achieving a low-carbon energy system.

A choice-based approach to dynamic capacitated multi-item lot sizing with demand uncertainty

With the purpose of planning and implementing pricing decisions on a tactical level as well as production decisions on an operational level, we consider – in an integrated form – the capacitated multi-item lot sizing problem with uncertain item demands and price-dependent discrete choice demand. The model is embedded into an overarching rolling horizon procedure allowing for adaptations to changes in demand and cost parameters. We first formulate the static problem version as a nonlinear mathematical program with underlying multinomial logit demand and subsequently linearize it to make it viable for mathematical programming solvers. Uncertainty of demands is taken into account by Monte Carlo simulation. More specifically, we generate random demand scenarios and utilize them as input data for the sample average approximation problem version. We further endow the problem setting with possibilities to incorporate pricing policy requirements such as restricting the number of price adaptations or defining periods without price adaptations. Overall, the developed approach yields a powerful tool for balancing item demands via pricing in a way favorable for adhering to available production capacities and thereby striking a balance between revenues and costs. Computational results confirm that adapting prices to time-dependent demand and cost parameters is exploited effectively to maintain a deliberately controlled production environment. Moreover, the integrated pricing and production setting allows to study the effect of pricing policy restrictions and demand uncertainties upon attainable profits.

A Soft Voting Ensemble Model for Hotel Revenue Prediction

In recent years, the hotel industry has faced unprecedented opportunities and challenges due to the increasing demand for travel and business trips. This growth not only presents significant opportunities but also brings challenges to resource management and price setting. Accurate hotel revenue prediction is crucial for the hotel industry as it influences pricing strategies and resource allocation. However, traditional hotel revenue prediction models fail to capture the diversity and complexity of hotel revenue data, resulting in inefficient and inaccurate predictions. Then, with the development of the ensemble learning, its application to hotel revenue prediction has emerged as an influential research direction. This study proposes a soft voting ensemble model for hotel revenue prediction, which includes six base models: Convolutional Neural Network, K-nearest Neighbors, Linear Regression, Long Short-term Memory, Multi-layer Perceptron, and Recurrent Neural Network. Firstly, the hyper-parameters of the base models are optimized with Bayesian optimization. Subsequently, a soft voting ensemble method is used to aggregate the predictions of each base model. Finally, experimental results on the hotel revenue dataset demonstrate that the soft voting ensemble model outperforms base models across six key performance metrics, providing hotel managers with more accurate revenue prediction tools to aid in scientific management decisions and resource allocation strategies. This study confirms the effectiveness of the soft voting ensemble model in enhancing the accuracy of hotel revenue forecasts, demonstrating its significant potential for application in strategic planning within the modern hotel industry.

How competition play a role in dental pricing? A study on French medico-administrative and tax reports dataset

ObjectivesFrench dentists charge additional fees for dental prostheses. This paper aims to provide new information on the determinants of dental price setting and inform public decision-making in the context of the widespread rejection of prosthetic dental care for financial reasons. We focus on the competitive mechanism in the dental prosthetics market and measure the impact of the density of professionals and competitors' prices on the fees charged by dentists. MethodsWe use data merging from an administrative health insurance database and information from tax declarations of French dentists. We test the effect of competitor prices and competition on individual price-setting using instrumental variables. The database obtained included 29,220 dentists. ResultsPractitioners’ prices grow with competitors’ prices (+1€ in competitor prices entails an increase of + 0.37€ in the practitioner's price). Women set lower prices, and having a young child in the household predicts an increase in price of 6.8€ (p-value=0.014). Rural areas present lower fees than urban areas (+11.4€ (p value=0.000)). ConclusionProsthetic prices are strategic complements that are compatible with the application of monopolistic competition in the dental care market. We encourage the regulator to develop competitive mechanisms, for example, through a public offer at moderate prices.

Strategic demand information sharing in an e‐commerce supply chain: The effect of the supplier's pricing timing

AbstractThe paper studies strategic demand information sharing on online retail platforms and explores how it is influenced by the wholesale pricing timing. The supplier sells the product under a wholesale price contract indirectly and by paying a proportional commission fee directly. We investigate both ex ante and ex post information sharing arrangements. Under each arrangement, the supplier needs to determine the sequence of the wholesale price setting and the information sharing stage. We find that (1) under ex ante information sharing, if the supplier sets the wholesale price before, the online platform always shares private information; otherwise, it prefers to share only when the commission fee is large. Interestingly, the online platform's information sharing incentive is independent of signal accuracy and market variability. (2) Under ex post information sharing, the online platform may manipulate the signal shared with the supplier. We characterize the conditions and influencing factors of truthful information sharing. (3) If the conditions for information sharing can be achieved, the supplier prefers to set the wholesale price later. Otherwise, the supplier sets the wholesale price before inducing the online platform to share information.

Inflation and Economic Growth Tradeoff, and Its Impact on Indonesia Poverty

There was tradeoff between inflation and economic growth in Indonesian economy over the last three decades, from 1990 to 2021. When economic growth tended to increase, inflation would also increase. The increasing scale of business sectors due to the increasing of economic growth shifted price and wage setting in the same direction, then increasing labor demand, and decreasing poverty. This study used a VAR model to estimate tradeoff between inflation and economic growth, and OLS model to estimate the relationship between economic growth and the poverty, and also between inflation and poverty. A VAR model explained that there was a positive bi-direction causality between inflation and economic growth, however, there was uncertainty whether economic growth caused inflation, or whether inflation caused economic growth. Although economic growth had a positive effect on reducing poverty, but due to the tradeoff between inflation and economic growth, then economic policies had to be designed to fully controlling inflation during the persistence of economic growth to prevent an increasing of poverty. Expected inflation and long run inflation target should always be a major concern when setting monetary policy to avoid higher economic fluctuation that could stimulate increasing of inflation and poverty.

Monotonicity of extremal Walrasian prices in gross substitutes markets

We consider a market of indivisible items with all agents possessing gross substitutes valuations. In this setting, Walrasian prices are guaranteed to exist. Assume that some items become unsaleable, or some new gross substitutes agents enter the market. How does this influence the set of Walrasian prices? In this note, we demonstrate that minimal and maximal Walrasian prices exhibit monotonous behavior in response to changes in supply or demand in gross substitutes markets.