Price Distribution Research Articles

Raising the Bar: Certification Thresholds and Market Outcomes

Certification of sellers by trusted third parties helps alleviate information asymmetries in markets, yet little is known about the impact of a certification’s threshold on market outcomes. Exploiting a policy change on eBay, we study how a more selective certification threshold affects the distribution of quality and incumbent behavior. We develop a stylized model that shows how changes in selectivity change the distribution of quality and prices in markets. Using rich data from hundreds of online categories on eBay.com , we find support for the model’s hypotheses. Our results help inform the design of certification selectivity in electronic and other markets. (JEL D82, L11, L15, L86)

Capacity optimization of hybrid energy storage system for microgrid based on electric vehicles’ orderly charging/discharging strategy

The high penetration rate of electric vehicles (EVs) will aggravate the uncertainty of both supply and demand sides of the power system, which will seriously affect the security of the power system. A microgrid (MG) system based on a hybrid energy storage system (HESS) with the real-time price (RTP) demand response and distribution network is proposed to deal with uncertainties. Through the guidance of RTP, the electricity consumption behavior of consumers and car owners is more adaptable to the output uncertainty of renewable energy source (RES) to ensure that the system has sufficient elastic capacity within the schedule cycle. The bi-level programming optimization model of HESS capacity is established, maximizing the MG's reliability and minimizing the comprehensive operating cost (COC). In addition, an EV's orderly charging/discharging strategy is formed, which effectively reduces operating costs and peak-to-valley load differences. The results show that EVs can effectively mitigate the peak-to-valley load difference by 20.5% under 100% participation in orderly charging/discharging. Under RTP-based demand response, MG can reduce the COC by 25.5%. In addition, the number of EVs participating in vehicle-to-grid also has different effects on scheduling. Based on the experimental data from Guizhou, results show that 200 electric private vehicles (EPVs) and ten electric buses (EBs) are the optimal number of vehicles to be solved; namely, the optimal capacity of the HESS is achieved.

Spatial–Temporal Variation and Influencing Factors on Housing Prices of Resource-Based City: A Case Study of Xuzhou, China

The steady development of the real estate market is an important link in the transformation of resource-based cities. Taking the main urban area of Xuzhou, a typical resource-based city in China, as an example, this study used a spatial–temporal geographically weighted regression (GTWR) method guided by characteristic price theory to analyze the evolution of the spatial–temporal pattern of house prices in resource-based cities and the influencing factors. The results of the study showed that, from the time trend, Xuzhou’s main city house price trend underwent the obvious stages; from June 2015 to September 2016, the trend was stable, and from March 2017 to June 2018, it was an “up–down–up” trend. With respect to the spatial distribution of house prices, from June 2015 to June 2017, the traditional business district and the major city in the Xuzhou metropolitan area were the areas of highest house prices, and after September 2017, the structural features of the traditional business district, along with the main city of the metropolitan area and the new city, formed the areas of highest importance. With regard to the factors influencing house prices, the type of dwelling, according to the building characteristics, had the largest impact on house prices, and the enrichment of housing types became an effective way to regulate housing prices. The impact of location characteristics on house prices varied depending on differences in the public infrastructure surrounding housing, with the contribution of the planned metro stations to house prices not effectively emerging. The impact of neighborhood characteristics on house prices varied, with tertiary care hospitals having a ‘neighborhood avoidance effect’ on house prices, given that hospital-generated waste was too densely distributed around houses, suppressing neighborhood house prices. The results of this study indicated that, in the process of real estate market development in resource-based cities, the planning department should consider the different functions and division of labor in each region and scientifically formulate urban development plans to provide a good external environment for the healthy development of the city’s property market.

Efficiency and price clustering in Islamic stocks: evidence from three Asian countries

PurposeThis paper aims to examine the extent of price clustering in a selection of Islamic stocks listed in Indonesia, Malaysia and Pakistan and also investigates the determinants of the phenomenon at the firm level.Design/methodology/approachThe author test the uniformity of price distribution in the selected securities. Then, the determinants of price clustering were investigated through multivariate analysis based on a binary logistic regression model. Following the arguments of Narayan et al. (2011), who emphasize the importance of considering firm heterogeneity when studying the phenomenon, the author conducts the empirical study at the firm level.FindingsThe evidence indicates that Islamic stocks show a mild level of price clustering. Only half of the stocks under analysis rejected the uniformity test in the distribution of prices. In these cases, investors exhibited a preference for prices ending at zero and five. The evidence does not confirm the cultural clustering theories. Price clustering is found to be positively associated with price level and relative bid-ask spread. Overall, the negotiation hypothesis, which predicts that investors prefer round prices to minimize the costs associated with negotiations, best explains most of our results.Research limitations/implicationsThe existence of price clustering is difficult to reconcile with the prediction of the efficient market hypothesis that prices should follow a random walk. Moreover, the evidence indicates that Muslim investors share a preference for round prices in some settings, under the assumption that Islamic stocks are mostly traded by Muslim investors.Originality/valueTo the author’s best knowledge, this is the first study to address the subject of price clustering in Islamic stocks.

Insights into Cryptocurrency Market Prices Fluctuation Correlated to COVID-19 Pandemic spillovers: Whether Bitcoin Can Act as a Safe Haven

In this article, I examine the relationship between COVID-19 daily additional cases and Bitcoin daily closing prices by applying Quantile-on-Quantile Regression (QQR) approach. The results reveal that the changing intensity level of COVID-19 does have an impact on Bitcoin prices. Whats more, my research captures the dependence between the distribution of Bitcoin prices and the severity of the COVID-19 pandemic. Levels of COVID-19 cause a rise in Bitcoin prices. I give a fresh look at combining every day confirmed number of new infections of COVID-19 and behavioral economics with fluctuating daily Bitcoin prices. And as the confirmed case has stabilized, although it is still at a relatively high position, investors would develop speculative psychology and take abnormal and seemingly inexplicable actions, e.g., they prefer to invest in Cryptocurrency rather than hold US dollars cash. Bitcoin offers a relatively steady performance in enduring the pandemic shocks; thus, it can be considered a safe-haven asset.

Land-use reforms and housing costs: Does allowing for increased density lead to greater affordability?

We generate the first cross-city panel dataset of land-use reforms that increase or decrease allowed housing density and estimate their association with changes in housing supply and rents. To generate reform data, we use machine-learning algorithms to search US newspaper articles between 2000 and 2019, then manually code them to increase accuracy. We merge these data with US Postal Service information on per-city counts of addresses and Census data on demographics, rents, and units affordable to households of different incomes. We then estimate a fixed-effects model with city specific time trends to examine the relationships between land-use reforms and the supply and price of rental housing. We find that reforms that loosen restrictions are associated with a statistically significant 0.8% increase in housing supply within three to nine years of reform passage, accounting for new and existing stock. This increase occurs predominantly for units at the higher end of the rent price distribution; we find no statistically significant evidence that additional lower-cost units became available or moderated in cost in the years following reforms. However, impacts are positive across the affordability spectrum and we cannot rule out that impacts are equivalent across different income segments. Conversely, reforms that increase land-use restrictions and lower allowed densities are associated with increased median rents and a reduction in units affordable to middle-income renters.

Probabilistic commodity price projections for unbiased techno-economic analyses

Techno-economic analysis is a core methodology for assessing the feasibility of new technologies and processes. The outcome of an analysis is largely dictated by the product’s price, as selected by the practitioner. Representative future price distributions are required as inputs to investment, sensitivity, and uncertainty analyses across the 20 to 25 year plant life. However, current price selection procedures are open to subjective judgment, not adequately considered, or neglected by calculating a minimum selling price. This work presents a machine learning methodology to produce unbiased projections of future price distributions for use in a techno-economic analysis. The method uses an ensemble of 100 neural network models with Long Short-Term Memory layers. The models are trained on the Energy Information Administration’s (EIA) long-term crude oil projections and a commodity’s historic price data. The proposed method is demonstrated by projecting the price of five commodity chemicals 26 years into the future using 12 years of historic data. Alongside the economic outlook extracted from the EIA projections, the five commodity price distributions capture stochastic and deterministic elements specific to each commodity. A statistically significant difference was observed when using the price projections to revise the Net Present Value distributions for two previous techno-economic analyses. This suggests that relying on heuristics when selecting price ranges and distributions is unrepresentative of a commodity’s price uncertainty. The novelty of this work is the presentation of an unbiased machine learning approach to project long-term probabilistic prices for techno-economic analyses, emphasising the pitfalls of less rigorous approaches.

Analysis Of Product Price For Creative Economic Business Yogyakarta Special Region Using Data Scrapping

The government in the National Economic Recovery (PEN) launched a strategy to increase business activity, namely by maximizing the potential for product sales through E-Commerce. E-Commerce sites in Indonesia display product information with different descriptions and prices. Through the pages on E-Commerce can collect data to form a useful information. Collection of data variations requires a method or system that can automatically collect the data provided on the Web page. In addition to data collection, another thing that needs to be considered is the meaning of knowledge from the collection of information that has been obtained. For these data to be useful, it is necessary to design a place for data management. Data must be selected and arranged properly so that it is easy to use so that it gives a good meaning. This research requires methods and tools in the process. The method used is Web Scrapping while the tools used are Tableau. There are two objectives in this research. First, obtain product price data for creative economy entrepreneurs in the Special Region of Yogyakarta through E-Commerce, especially Tokopedia and Bukalapak. Second, present product price data visually to facilitate the process of price distribution analysis so that it can provide information to local governments, especially product managers from creative economy business actors to support strategic policies in determining product prices from creative economy business actors in the Special Region of Yogyakarta to support promotion and tourism.

Intertemporal Price Discrimination via Randomized Promotions

Problem definition: The undesirable but inevitable consequence of running promotions is that consumers can be trained to time their purchases strategically. In this paper, we study randomized promotions, where the firm randomly offers discounts over time, as an alternative strategy of intertemporal price discrimination. Methodology/results: We consider a base model where a monopolist sells a single product to a market with a constant stream of two market segments. The segments are heterogeneous in both their product valuations and patience levels. The firm precommits to a price distribution, and in each period, a price is randomly drawn from the committed distribution. We characterize the optimal price distribution as a randomized promotion policy and show that it serves as an intertemporal price discrimination mechanism such that high-valuation customers would make a purchase immediately at a regular price upon arrival, and low-valuation customers would wait for a random promotion. Compared against the optimal cyclic pricing policy, which is optimal within the strategy space of all deterministic pricing policies, the optimal randomized pricing policy beats it if low-valuation customers are sufficiently patient and the absolute discrepancy between high and low customer valuations is large enough. We extend the model in three directions. First, we consider the case where a portion of customers are myopic and would never wait. We show that the existence of myopic customers is detrimental to the firm’s profitability, and the expected profit from an optimal randomized pricing policy decreases as the proportion of myopic customers in the population increases. Second, we consider Markovian pricing policies where prices are allowed to be intertemporally correlated in a Markovian fashion. This additional maneuver allows the firm to reap an even higher profit when low-valuation customers are sufficiently patient by avoiding consecutive promotions but, on average, running the promotion more frequently with a smaller discount size. Lastly, we consider a model with multiple customer segments and show that a two-point price distribution remains optimal, and our conclusion from the two-segment base model still holds under certain conditions that are adopted in the literature. Managerial implications: Our results imply that the firm may want to deliberately randomize promotions in the presence of forward-looking customers. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201124], the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757 and RGPIN-2021-04295], and the Youth Project of the Humanities and Social Science Foundation of the Ministry of Education of China [Grant 22YJC630006]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1194 .

การใช้บริการร้านอาหารพื้นถิ่นของผู้บริโภค จังหวัดภูเก็ต

The purposes of this research are: 1) to study the personal factors of consumers choose local restaurant in Phuket Province, 2) to study the consumption factors of local restaurant in Phuket Province, 3) to study the factors of marketing mix in the local restaurant in Phuket Province, 4) to compare the level of importance of the marketing mix factors in the local restaurant in Phuket Province classify by personal factors of respondents, 5) to compare the level of importance of the marketing mix factors in the consumers' use of local restaurant in Phuket Province classify according to the use of local restaurants , and 6) presenting guidelines for local restaurant development Phuket Province. A sample group of 400 people and local restaurant operators Phuket has 16 cases. The research instruments are questionnaires and interviews. The statistics are use for data analysis frequency, percentage, mean, standard deviation, one-way analysis of variance and analyze descriptive data. The research was found that as follows : 1) most consumers were female, aged 36-40 years, have a bachelor's degree, by were being a private company employee, with estimated income 10,001–20,000 baht and they domiciled in Phuket. 2) Choosing to used local restaurants, it was found that most of the consumers chose be to consume local food,because of their unique identity and different from conventional food. The only food that was commonly being consumed Hokkien fried noodles. As for the main food and side dishes that were commonly being consumed Stir Fried Liang Bai with Shrimp Roe for the popular savory snack was Hu Shae, the sweet snacks were using Gosui and the popular desserts to be consumed Oh Aew. Most of them consume local food once a month and was paying approximately 901- 1,300 baht for each meal. 3) Consumers value the overall were marketing mix at a high level. The sides were in descending order, local food, price, construction personnel and physical presentation and distribution service marketing promotion. 4) Compare the level of importance of the marketing mix factors in the consumers will have been using of local restaurants. Phuket Province was found that gender, age, education level, occupation, average monthly income and different domiciles pay attention to the factors of marketing mix no difference except the sexes were not different with statistical significance at the .05 level. 5) Compare the level of importance of the marketing mix factors in the consumers wiil have been using of local restaurants. Phuket Province was found that the reasons for consuming local food popular types of local food popular local food items frequency of local food consumption and the cost of consumption per time was different. Pay attention to the factors of marketing mix. There was no statistically significant difference at the .05 level. And 6) guidelines for local restaurant development Phuket Province were to selecting raw materials directly from the producer source ,in order to maintain freshness and quality. In addition, a standard price threshold should be set was suitable for the quality of local food and should be increased distribution channels. They could be ordered online and were also advisable to advertise through various media such as websites or other media in order to disseminate customers to knew more restaurants. It should be also focus on training employees to recognized the importance of service. Focus on educating employees will have been service skills. At the same time, the restaurant should be designed to be unique, easy to remember and technology should be adopted, for example in the matter was receiving food orders to achieve accuracy, speed and error prevention.

The impact of the El Niño phenomenon on electricity prices in hydrologic‐based production systems: A switching regime semi‐nonparametric approach

AbstractElectricity production in highly hydrological‐dependent systems is determined by different weather phenomena, which strongly impact spot prices. To account for such stylized facts, we propose a stochastic process with a mean reversion and switching regime component to represent the dynamics of the spot price. The short‐term movements are represented by semi‐nonparametric (SNP) distributions, in contrast to previous studies that traditionally assume Gaussian processes. We consider the Colombian electricity market as a study case, in which 68% of its electrical generation comes from water resources, and the El Niño phenomenon represents a critical source of risk for maintaining long‐term supply, sustainability of investments, and efficiency of prices. We show that under scarcity seasons, the spot price mean, variance, and some superior‐order moments of electricity price distribution increase, as does the risk level of the system. In particular, the switching regime model with SNP distributions for the random components outperforms traditional models, leading to accurate estimates and simulations, thus being a helpful tool for resource planning, risk management, and policy‐makers for electricity markets with high climatic dependency.

A multimodal approach for improving market price estimation in online advertising

Learning the distribution of market prices is an important and challenging issue for demand-side platforms (DSPs) that serve as advertisers’ agents to compete for online advertising placements in real-time bidding (RTB) systems. Many existing approaches make an assumption that the market prices follow an unimodal distribution. However, based on analytical insights from real-world datasets, we found the distinct multimodal characteristics underlying the distribution of market prices. Moreover, the impression-level features for each ad are also ignored by these approaches in prediction, reducing the accuracy further. To address these problems, a Gaussian Mixture Model (GMM) is proposed in this paper to describe and discriminate the multimodal distribution of market price by utilizing the impression-level features. To further improve its robustness, GMM is extended into a censored version (CGMM) to handle the right-censored challenge in RTB systems (i.e., the market price is only visible to the winner of the ad auction. Thus, the dataset is always biased). Extensive experiments on two real-world public datasets demonstrate that GMM and CGMM significantly outperform 10 state-of-the-art baselines in terms of Wasserstein distance, KL-divergence, ANLP and MSE. To the best of our knowledge, this paper is the first work to simultaneously deal with the multimodal nature of market price distribution and the right-censored challenge in existing RTB systems. It will enable future RTB systems to develop more realistic bidding strategies to enhance the efficiency of online advertising placement auctioning.

Changes in sugar-sweetened beverage purchases across the price distribution after the implementation of a tax in Mexico: a before-and-after analysis

BackgroundA tax of one-Mexican peso per liter of sugar-sweetened beverage (SSB) came into effect in January 2014 in Mexico as a national health policy to tackle the high overweight and obesity prevalence. Previous studies have shown an overall reduction in SSB purchases after the tax implementation. However, it remains unknown whether and to what extent SSB consumers switched to cheaper taxed beverages, attenuating the potential effect of the policy. Our study’s objective was to estimate changes in household purchases of taxed SSBs by tertiles of SSB prices (low, middle, and high) in urban areas after the SSB tax implementation in 2014.MethodsBased on purchase data for 2012–2015 from households living in 54 Mexican cities with a population > 50,000 inhabitants, we calculated unit-value SSB prices for the full period and sorted them on a monthly basis to create monthly price tertiles. We merged these price tertiles to household purchases and created average monthly ml/capita/day SSB purchases by price tertile at the city level. We assessed SSB purchase switching patterns before and after the tax implementation through price-tertile stratified linear models. The main variable in the models was a dummy indicator that allowed us to identify the pre-tax period (2012–13) and post-tax period (2014–15). We controlled our models for time trends and contextual economic variables.ResultsIn the regression adjusted models, we found a statistically significant purchase reduction ranging between 10.80 and 13.79 ml/capita/day (p-value < 0.001) across taxed beverages from the middle-price SSB after the tax implementation. We observed no statistically significant reductions in purchases of low-price SSBs and high-price SSBs.ConclusionsOur findings show purchase reductions in the middle-price SSBs, which represents ≈30% of the overall SSB purchases in urban Mexico. Future studies should be conducted to test if the redesign of the current the tax, by either doubling the tax amount or taxing sugar content, might reduce more effectively purchases across all SSBs.

Machine Learning Model for Determination of the Optimal Strategy in an Online Auction

We apply a machine learning model to determine the optimal strategy in an online auction for the rent of computing resources using the best-choice model. The best-choice model allows clients to minimize the expected cost of renting a computing resource based on the spot price distribution function. The spot price dynamics platform is investigated. The most suitable price distributions in an auction are the normal distribution and its mixtures. In this case, the problems of determining the number of components in the mixture and estimating its parameters arise. One of the well-known methods for determining the number of components in a mixture of normal distributions is the BIC criterion. The EM algorithm is a basic tool for estimating the parameters of a mixture of distributions if we know the number of components. However, parameter estimation by this method takes more time when both the sample size and the number of components of the mixture increase. To automate and expedite the process of determining the number of components for a mixture of normal distributions and estimating its parameters, a classification machine learning model based on a convolutional neural network is developed. The results of the model training and validation are presented. The suggested model is compared with other algorithms which do not use neural networks. The results show that the suggested model performs well in determining the most appropriate number of components for a mixture of normal distributions and in reducing the time spent on applying the EM algorithm to estimate its parameters. This model can be used in different arias, for example, in finance or for determination of the optimal strategy in an online auction for the rent of computing resources.

Distributionally robust day-ahead combined heat and power plants scheduling with Wasserstein Metric

Combined heat and power (CHP) plants are main generation units in district heating systems that produce both heat and electric power simultaneously. Moreover, CHP plants can participate in electricity markets, selling and buying the extra power when profitable. However, operational decisions have to be made with unknown electricity prices. The distribution of unknown electricity prices is also not known exactly and uncertain in practice. Therefore, the need of tools to schedule CHP units’ production under distributional uncertainty is necessary for CHP producers. On top of that, a heating network could serve as a heat storage and an additional source of flexibility for CHP plants. In this paper, a distributionally robust short-term operational model of CHP plants in the day-ahead electricity market is developed. The model accounts for the heating network and considers temperature dynamics in the pipes. The problem is formulated in a data-driven manner, where the production decisions explicitly depend on the historical data for the uncertain day-ahead electricity prices. A case study is performed, and the resulting profit of the CHP producer is analyzed. The proposed operational strategy shows high reliability in the out-of-sample performance and a profit gain of the CHP producer, who is aware of the temperature dynamics in the heating network.

Foreign Exchange Options on Heston-CIR Model Under Lévy Process Framework

In this paper, we consider the Heston-CIR model with Lévy process for pricing in the foreign exchange (FX) market by providing a new formula that better fits the distribution of prices. To do that, first, we study the existence and uniqueness of the solution to this model. Second, we examine the strong convergence of the Lévy process with stochastic domestic short interest rates, foreign short interest rates and stochastic volatility. Then, we apply Least Squares Monte Carlo (LSMC) method for pricing American options under our model with stochastic volatility and stochastic interest rate. Finally, by considering real-world market data, we illustrate numerical results for the four-factor Heston-CIR Lévy model.

Digital platforms to close the information asymmetry GAPS

Market intermediaries coordinate the actions of buyers and sellers. Digital platforms, including the case Platform as a Service (PaaS), take the roles of market intermediaries with some novel ones. In a multi-intermediary world, consumers and suppliers continue to incur search costs due to reacting to multiple intermediaries. Consumers and suppliers discount future net gains due to monetization of search time costs. Consumers have different levels of willingness to pay, suppliers have different opportunity costs, and intermediary firms have different transaction costs. These firms set both bid prices and ask prices. Consumers look for firms that offer a lower purchase price, and suppliers look for firms that offer a higher sale price. Due to such heterogeneity and search costs, the market equilibrium is a distribution of sale prices and a distribution of purchase prices. This equilibrium depends on the discount rate of consumers and suppliers, for whom a higher discount rate stands for a decrease in activity (the number of active consumers and suppliers), while a higher discount rate means an increase in the activity of intermediary firms (the number of active firms): a higher discount rate increases the costs of time-consuming search for consumers and suppliers. Intermediary firms then raise their purchase prices and lower their sale prices because consumers and suppliers are willing to pay a premium to avoid further search, thus increasing the returns to intermediation for firms and stimulating growth in the number of intermediary firms active at the market equilibrium. Thus, the discount rate determines the search costs. When this rate falls to zero, the search costs are eliminated and the relationships between the size of the bid-ask spread and transaction costs are revealed. Then the Walras equilibrium will be the limiting case of the intermediated market when transaction costs fall, and the supply and demand model can be considered an ideal case compatible with the market under consideration at the presence of search costs and price-setting firms. The cloud technologies are saving the general search costs. The two basic cases of providers for such technologies are monopoly and competition.

Economic Value of Food Commodities and Labor Shift due to Rice Field Conversion in Batu City, Indonesia

Increasing tourist activities in Batu City makes agricultural land conversion unavoidable. This research aims to determining the lost economic value of food commodities due to converted agricultural land area and labor shift. The conversion area of agricultural land in all areas can be identified through Landsat image 8 ETM+ in 2017 and 2021. An overlay analysis using ArcGIS 10.2 software with 7 variables was then utilized to determine land capability and land suitability. Then, it was combined with the survey results of 334 land price points to determine the distribution of agricultural land prices in Batu City by using Kriging interpolation analysis. The results of the research show that the agricultural land area has decreased in the past 4 years by 169.96 Ha, with a lost economic value of approximately 5 billion Indonesian rupiah. The agricultural land price in Batu City is divided into 4 classifications. The results indicate that the agricultural land price is high, so it accelerates the agricultural land conversion. Therefore, the government should pay close attention to land conversion.

Pricing strategy and marketing distribution channels on customer satisfaction and purchasing decision for green products

This paper aims to investigate the impact of pricing strategy and distribution channels on the decision-making process of customers when purchasing green products. The focus is on the satisfaction of customers who purchase these products. The study is conducted on the population of all customers who purchase green products from small and medium-sized enterprises (SMEs). The sample size is determined using a formula that considers the number of variables or indicators. The study uses Partial Least Squares Structural Equation Modelling (PLS-SEM), which is a method used to test variants-based structural equation models with the support of SmartPLS software. The results show that all seven hypotheses are supported, indicating that pricing strategy and distribution channels play a critical role in customer satisfaction and decision-making processes when purchasing green products. The results have implications for SMEs that sell green products as they need to focus on their pricing strategies and distribution channels to increase customer satisfaction and decision-making. This study provides essential insights into the impact of pricing strategy and distribution channels on customer satisfaction and decision-making when purchasing green products. The findings can guide SMEs in developing effective marketing strategies to persuade more customers to purchase green products and contribute to environmental sustainability.

Sticky Prices and Costly Credit

We develop a theory of money and credit as competing payment instruments, then put it to work in applications. Agents use cash and credit because the former (latter) is subject to the inflation tax (transaction costs). Frictions that make the choice of payment method interesting also imply equilibrium price dispersion. We derive closed- form solutions for money demand, and show how to simultaneously account for the price-change facts, cash-credit shares in micro data, and money-interest correlations in macro data. The effects of inflation on welfare, price dispersion and markups are discussed, as are nonstationary equilibria with dynamics in the price distribution.