An Empirical Analysis of Price Discrimination Mechanisms and Retailer Profitability

2005 ◽  
Vol 42 (4) ◽  
pp. 516-524 ◽  
Author(s):  
Romana J. Khan ◽  
Dipak C. Jain
Keyword(s):  
Price Discrimination ◽  
Optimal Pricing ◽  
Parameter Estimates ◽  
Quantity Discounts ◽  
Demand Model ◽  
Parameter Heterogeneity ◽  
Package Size ◽  
Structural Demand ◽  
The Impact ◽  
Second Degree

Retailers typically engage in some form of price discrimination to increase profitability. In this article, the authors compare the impact on retailer profitability of two price discrimination mechanisms: quantity discounts based on package size (second-degree price discrimination) and store-level pricing or micromarketing (third-degree price discrimination). Whereas the latter has been well addressed in the marketing literature, there is limited empirical research on the use of quantity discounts for price discrimination. Using store-level sales data, the authors estimate a structural demand model, accounting for parameter heterogeneity and price endogeneity. They combine the parameter estimates with a model of retailer pricing to conduct optimal pricing and profitability simulations under several scenarios, ranging from constraining the retailer not to engage in any form of price discrimination to the least restrictive scenario of setting nonlinear price schedules specific to each store. The pricing simulations enable the decomposition of profitability as a result of the different forms of price discrimination. Profits are greatest when retailers combine second- and third-degree price discrimination. The authors find that the ability to engage in second-degree price discrimination contributes more to retailer profitability than does third-degree price discrimination.

scholarly journals Intertemporal Price Discrimination in Storable Goods Markets

2013 ◽  
Vol 103 (7) ◽  
pp. 2722-2751 ◽  
Author(s):  
Igal Hendel ◽  
Aviv Nevo
Keyword(s):  
Simple Model ◽  
Price Discrimination ◽  
Consumer Welfare ◽  
Optimal Pricing ◽  
Intertemporal Price Discrimination ◽  
Level Data ◽  
Future Consumption ◽  
The Impact ◽  
Total Welfare

We study intertemporal price discrimination when consumers can store for future consumption needs. We offer a simple model of demand dynamics, which we estimate using market-level data. Optimal pricing involves temporary price reductions that enable sellers to discriminate between price sensitive consumers, who stockpile for future consumption, and less price-sensitive consumers, who do not stockpile. We empirically quantify the impact of intertemporal price discrimination on profits and welfare. We find that sales (i) capture 25–30 percent of the gap between non-discriminatory profits and (unattainable) third-degree price discrimination profits, (ii) increase total welfare, and (iii) have a modest impact on consumer welfare. (JEL D11, D12, L11, L12, L81)

scholarly journals Nonlinear Pricing in Village Economies

Econometrica ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 207-263 ◽  
Author(s):  
Orazio Attanasio ◽  
Elena Pastorino
Keyword(s):  
Price Discrimination ◽  
Conditional Cash Transfer ◽  
Nonlinear Pricing ◽  
Cash Transfer ◽  
Reduced Form ◽  
Quantity Discounts ◽  
Transfer Program ◽  
Market Participation ◽  
Using Data ◽  
The Impact

This paper examines the prices of basic staples in rural Mexico. We document that nonlinear pricing in the form of quantity discounts is common, that quantity discounts are sizable for basic staples, and that the well‐known conditional cash transfer program Progresa has significantly increased quantity discounts, although the program, as documented in previous studies, has not affected unit prices on average. To account for these patterns, we propose a model of price discrimination that nests those of Maskin and Riley (1984) and Jullien (2000), in which consumers differ in their tastes and, because of subsistence constraints, in their ability to pay for a good. We show that under mild conditions, a model in which consumers face heterogeneous subsistence or budget constraints is equivalent to one in which consumers have access to heterogeneous outside options. We rely on known results to characterize the equilibrium price schedule, which is nonlinear in quantity. We analyze the effect of nonlinear pricing on market participation as well as the impact of a market‐wide transfer, analogous to the Progresa one, when consumers are differentially constrained. We show that the model is structurally identified from data on prices and quantities from a single market under common assumptions. We estimate the model using data on three commonly consumed commodities from municipalities and localities in Mexico. Interestingly, we find that relative to linear pricing, nonlinear pricing is beneficial to a large number of households, including those consuming small quantities, mostly because of the higher degree of market participation that nonlinear pricing induces. We also show that the Progresa transfer has affected the slopes of the price schedules of the three commodities we study, which have become steeper as consistent with our model, leading to an increase in the intensity of price discrimination. Finally, we find that a reduced form of our model, in which the size of quantity discounts depends on the hazard rate of the distribution of quantities purchased in a village, accounts for the shift in price schedules induced by the program.

Bike-Share Systems: Accessibility and Availability

2020 ◽  
Vol 66 (9) ◽  
pp. 3803-3824 ◽  
Author(s):  
Ashish Kabra ◽  
Elena Belavina ◽  
Karan Girotra
Keyword(s):  
New York ◽  
Operations Management ◽  
Demand Model ◽  
User Interest ◽  
Structural Demand ◽  
Using Data ◽  
Set Up ◽  
The Impact ◽  
The City ◽  
Bike Share

The cities of Paris, London, Chicago, and New York (among many others) have set up bike-share systems to facilitate the use of bicycles for urban commuting. This paper estimates the impact of two facets of system performance on bike-share ridership: accessibility (how far the user must walk to reach stations) and bike-availability (the likelihood of finding a bicycle). We obtain these estimates from a structural demand model for ridership estimated using data from the Vélib’ system in Paris. We find that every additional meter of walking to a station decreases a user’s likelihood of using a bike from that station by 0.194% (±0.0693%), and an even more significant reduction at higher distances (>300 m). These estimates imply that almost 80% of bike-share usage comes from areas within 300 m of stations, highlighting the need for dense station networks. We find that a 10% increase in bike-availability would increase ridership by 12.211% (±1.097%), three-fourths of which comes from fewer abandonments and the rest of which comes from increased user interest. We illustrate the use of our estimates in comparing the effect of adding stations or increasing bike-availabilities in different parts of the city, at different times, and in evaluating other proposed improvements. This paper was accepted by Vishal Gaur, operations management.

scholarly journals The effect of non-recurring gains and losses on the CEO’s compensation

2006 ◽  
Vol 3 (3) ◽  
pp. 161-177
Author(s):  
Mahmoud M. Nourayi ◽  
Giorgio Canarella ◽  
Sudha Krishnan
Keyword(s):  
Firm Size ◽  
Performance Model ◽  
Parameter Estimates ◽  
Cross Sectional ◽  
Public Companies ◽  
Total Compensation ◽  
Parameter Heterogeneity ◽  
Wide Range ◽  
Cash Compensation ◽  
The Impact

In this study we examine the impact of ‘Non-recurring Items (NRI),’ reported on the income statements of public companies, on the CEOs’ pay-performance relationship. Using panel (timeseries cross-sectional) data for 435 companies from a wide range of industries over the period 1998-2002, we first revisit the pay-performance model estimated by Gaver and Gaver (1998). We then extend the Gaver and Gaver (1998) model by analyzing a) the impact of NRI on total (cash plus non cash) compensation of CEOs, b) the role of firm size, and c) the influence of multiple NRI reporting by the same firm. Our results indicate that the Gaver and Gaver (1998) findings are robust to the inclusion of firm size in the case of cash compensation. This, however, does not hold in the case of total compensation. The pay-performance model for cash compensation and total compensation yields significantly different results with respect to NRI as well. Our results indicate that multiple reporting of NRI by the same firm affects the parameter estimates. Finally, we examine the issue of parameter heterogeneity using the quantile regression approach, and report findings which provide some evidence that parameter heterogeneity may deserve attention in executive compensation studies.

The Impact of Missing and Error-Prone Auxiliary Information on Sparse-Matrix Sub-Population Parameter Estimates

Methodology ◽  
2015 ◽  
Vol 11 (3) ◽  
pp. 89-99 ◽  
Author(s):  
Leslie Rutkowski ◽  
Yan Zhou
Keyword(s):  
Sparse Matrix ◽  
Small Body ◽  
Auxiliary Information ◽  
Poor Quality ◽  
Quality Data ◽  
Estimation Methods ◽  
Parameter Estimates ◽  
Population Parameter ◽  
Conditioning Model ◽  
The Impact

Abstract. Given a consistent interest in comparing achievement across sub-populations in international assessments such as TIMSS, PIRLS, and PISA, it is critical that sub-population achievement is estimated reliably and with sufficient precision. As such, we systematically examine the limitations to current estimation methods used by these programs. Using a simulation study along with empirical results from the 2007 cycle of TIMSS, we show that a combination of missing and misclassified data in the conditioning model induces biases in sub-population achievement estimates, the magnitude and degree to which can be readily explained by data quality. Importantly, estimated biases in sub-population achievement are limited to the conditioning variable with poor-quality data while other sub-population achievement estimates are unaffected. Findings are generally in line with theory on missing and error-prone covariates. The current research adds to a small body of literature that has noted some of the limitations to sub-population estimation.

The impact of regulations on labor markets in the U.S.

2019 ◽  
Vol 1 (1) ◽  
pp. 36-40
Author(s):  
Souad Adnane
Keyword(s):  
Supply And Demand ◽  
Demand Model ◽  
Lead Teachers ◽  
Associate's Degree ◽  
Childcare Workers ◽  
Educational Requirements ◽  
The Cost ◽  
Women's Labor Force Participation ◽  
The Impact ◽  
The U.S

The District of Columbia (DC) Office of the Superintendent of Education (OSSE) issued in December 2016 new educational requirements for childcare workers, according to which, all childcare center directors in the District must earn a bachelor’s degree by December 2022 and all lead teachers an associate’s degree by December 2020 (Institute for Justice, 2018). Moreover, DC has one of the lowest staff-child ratios in the country. How are regulations pertaining to childcare workers’ qualifications and staff-child ratio affecting the childcare market in DC? The present paper is an attempt to answer this question first by analyzing the effects of more stringent regulations on the cost and availability of childcare in the U.S based on existing studies. It also uses the basic supply and demand model to examine the possible impact of the new DC policy on the cost, quality and supply of childcare in the District and how it will affect working parents, especially mothers. Next, the paper discusses the impact of deregulation based on simulations and regressions conducted by studies covering the U.S., and implications for quality. It concludes that more stringent childcare regulations, regarding educational requirements and staff-child ratios, are associated with a reduced number of childcare centers and a higher cost, and eventually affects women’s labor force participation.

Examining Nonnormal Latent Variable Distributions for Non-Ignorable Missing Data

2021 ◽  
Vol 45 (3) ◽  
pp. 159-177
Author(s):  
Chen-Wei Liu
Keyword(s):  
Missing Data ◽  
Latent Variable ◽  
Bivariate Normal Distribution ◽  
Parameter Estimates ◽  
Missing Responses ◽  
Variable Distribution ◽  
Item Responses ◽  
Nonnormal Distribution ◽  
The Impact ◽  
Biased Estimates

Missing not at random (MNAR) modeling for non-ignorable missing responses usually assumes that the latent variable distribution is a bivariate normal distribution. Such an assumption is rarely verified and often employed as a standard in practice. Recent studies for “complete” item responses (i.e., no missing data) have shown that ignoring the nonnormal distribution of a unidimensional latent variable, especially skewed or bimodal, can yield biased estimates and misleading conclusion. However, dealing with the bivariate nonnormal latent variable distribution with present MNAR data has not been looked into. This article proposes to extend unidimensional empirical histogram and Davidian curve methods to simultaneously deal with nonnormal latent variable distribution and MNAR data. A simulation study is carried out to demonstrate the consequence of ignoring bivariate nonnormal distribution on parameter estimates, followed by an empirical analysis of “don’t know” item responses. The results presented in this article show that examining the assumption of bivariate nonnormal latent variable distribution should be considered as a routine for MNAR data to minimize the impact of nonnormality on parameter estimates.

Examining the Impact of and Sensitivity of Fit Indices to Omitting Covariates Interaction Effect in Multilevel Multiple-Indicator Multiple-Cause Models

2021 ◽  
pp. 001316442199240
Author(s):  
Chunhua Cao ◽  
Eun Sook Kim ◽  
Yi-Hsin Chen ◽  
John Ferron
Keyword(s):  
Root Mean Square ◽  
Interaction Effect ◽  
Intraclass Correlation ◽  
Information Criteria ◽  
Parameter Estimates ◽  
Mean Square ◽  
Fit Indices ◽  
Cluster Number ◽  
Multiple Indicator ◽  
The Impact

This study examined the impact of omitting covariates interaction effect on parameter estimates in multilevel multiple-indicator multiple-cause models as well as the sensitivity of fit indices to model misspecification when the between-level, within-level, or cross-level interaction effect was left out in the models. The parameter estimates produced in the correct and the misspecified models were compared under varying conditions of cluster number, cluster size, intraclass correlation, and the magnitude of the interaction effect in the population model. Results showed that the two main effects were overestimated by approximately half of the size of the interaction effect, and the between-level factor mean was underestimated. None of comparative fit index, Tucker–Lewis index, root mean square error of approximation, and standardized root mean square residual was sensitive to the omission of the interaction effect. The sensitivity of information criteria varied depending majorly on the magnitude of the omitted interaction, as well as the location of the interaction (i.e., at the between level, within level, or cross level). Implications and recommendations based on the findings were discussed.

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