nep-dcm New Economics Papers
on Discrete Choice Models
Issue of 2023‒07‒17
37 papers chosen by
Edoardo Marcucci
Università degli studi Roma Tre
  1. Stated and Inferred Precedence-Dependent Ordering Effects in Hypothetical and Real Discrete Choice Experiments By Jiang, Qi; Penn, Jerrod; Hu, Wuyang
  2. What drives battery electric vehicle adoption? Willingness to pay to reduce emissions through vehicle choice By Gore, Christina C.; Carrel, Andre; Irwin, Elena G.
  3. Exploring consumer preferences and the willingness to pay for domestically produced finfish in the Kingdom of Saudi Arabia By Gomez, Miguel I.; Mohammed, Broom; Li, Jie; Ballco, Petjon; Zhang, Yanan
  4. Incorporating Domain Knowledge in Deep Neural Networks for Discrete Choice Models By Shadi Haj-Yahia; Omar Mansour; Tomer Toledo
  5. Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis By Hung Tran; Tien Mai
  6. Estimating Willingness to Pay for Kale with Organic and Local Labels By Strickland, George; De Figueiredo Silva, Felipe; Vassalos, Michael; Ureta, Joan
  7. What are the attributes that actually catch our eye? A choice experiment to understand Latin American coffee & cocoa consumers' preferences By Duran Gabela, Carlos F.; Sarasty, Oscar; Lamino Jaramillo, Pablo; Boren-Alpizar, Amy
  8. Meta-analysis of Consumer's willingness to pay for broadband By Regmi, Sabina; Kim, Ayoung; Mills, Devon P.; Green, John
  9. The Impact of Endogenous and Exogenous Factors on Farmer Willingness-to-Pay for Biofortified Bean Seed: A Field Experiment in Rural Zimbabwe By Herrington, Caitlin L.; Maredia, Mywish K.; Ortega, David L.; Reyes, Byron A.
  10. Identification in Discrete Choice Models with Imperfect Information By Cristina Gualdani; Shruti Sinha
  11. Evaluating Stakeholder Preferences in Aquatic Invasive Plant Management: Evidence from a Choice Experiment in Florida By Rajan, Abhishek; Savchenko, Olesya; Prince, Candice; Leary, James
  12. Willingness to pay for meal kits among low-income households By Cao, Ting; House, Lisa A.; Chambers, Kerri-Ann; Mathews, Anne; Shelnutt, Karla
  13. Does Bid Quantity Matter? Comparing Farmer Willingness-to-Pay for Specified vs Open-Ended Quantities of Biofortified Bean and Maize Seed in a Non-hypothetical Field Experiment By Herrington, Caitlin L.; Ortega, David L.; Maredia, Mywish K.; Reyes, Byron A.
  14. How Much Are Machine Assistants Worth? Willingness to Pay for Machine Learning-Based Software Testing By Mehler, Maren F.; Vetter, Oliver A.
  15. Control over future payouts and willingness-to-pay for insurance: Experimental evidence from Kenyan farmers By Kramer, Berber; Waweru, Carol; Malacarne, Jonathan G.
  16. Improving wholesale local food procurement: a farmer choice experiment By Wasserman-Olin, Rebecca; Gomez, Miguel I.; Schmit, Todd M.; Bjoerkman, Thomas
  17. Can Information Improve Visual Attention and Attribute Attendance to Sugar Content? Evidence from an Incentivized Beverage Choice Experiment (Abstract) By Wei, Xuan; McFadden, Brandon R.; Khachatryan, Hayk
  18. Summing the parts: How does “bundling” affect willingness-to-pay for seeds and insurance in a sample of Kenyan farmers? By Kramer, Berber; Waweru, Carol; Malacarne, Jonathan G.
  19. Reference-Dependent Food Choice with Various Reference Prices By Nian, Yefan; Duan, Dinglin; Uddin, Md Azhar; Gao, Zhifeng
  20. How Does Information Avoidance Determine the Effect of Information on Consumer Willingness to Pay: A Case Study on Genetically Modified and Gene Edited Crops By Marson, Corissa; Abbey, Marie; Yue, Chengyan; Smith, Alan; Stowers, Carrie
  21. Is biomass co-firing a means to end or extend coal-based electricity production in the US? Evidence from a choice experiment By Santhosh, Harikrishnan; Colson, Greg; Mullen, Jeffrey D.
  22. Consumer preferences and WTP for value-added attributes of whole-grain foods: evidence from China By Zhang, Xin; Wang, Jingjing; Fan, Shenggen
  23. An Empirical Analysis of the Effect of Ballot Truncation on Ranked-Choice Electoral Outcomes By Mallory Dickerson; Erin Martin; David McCune
  24. Avoiding Fraudulent Meat: Muslim Consumer Preferences for Halal Meat Retailers By Hopkins, Kelsey A.; McKendree, Melissa G. S.; Ortega, David L.
  25. Model Choice, Hypothetical Bias and Risk Aversion: A Charitable Donation Application By Penn, Jerrod; Howard, Gregory E.; Hu, Wuyang
  26. Regression and matching in hedonic analysis: Empirical guidance for estimator choice By Moeltner, Klaus; Puri, Roshan; Johnston, Robert J.
  27. Driving, Dropouts, and Drive-Throughs: Mobility Restrictions and Teen Human Capital By Bostwick, Valerie; Severen, Christopher
  28. Computing Revealed Preference Goodness of fit Measures with Integer Programming By Thomas Demuynck; John Rehbeck
  29. How do local food producers participate in state-sponsored marketing programs? Evidence from real choice data in Missouri By Tran, Lan T.; Su, Ye; McCann, Laura M.
  30. The Economic Value of State Parks: Revealed Preference Estimates Using Cell Phone Data By Del Rossi, Gemma; Kling, Catherine L.; Rudik, Ivan
  31. On the Revealed Preference Analysis of Stable Aggregate Matchings By Thomas Demuynck; Umutcan Salman
  32. Eliciting Choice Across Borders: Preferences for U.S. Rice Among Ethnic Chinese in China and the United States By Moeltner, Klaus; Neill, Clinton L.; Ramsey, Austin F.; Wang, Huaiyu
  33. Nash Equilibrium and Axiom of Choice Are Equivalent By Conrad Kosowsky
  34. The Influence of Information on Consumer Beliefs and Preferences for Ground Beef By Samper, Bailey A.; Crocker, Andrew; Williams, Ryan Blake
  35. Climate Crisis Attitudes among Financial Professionals and Climate Experts By Elisabeth Gsottbauer; Michael Kirchler; Christian König-Kersting
  36. Price as a Quality Indicator in Choice Experiments: The Case of Meat Demand in China By Kang, Qi; Carpio, Carlos E.; Wang, Chenggang; Boonsaeng, Tullaya; Hudson, Michael Darren
  37. The effect of weather on the willingness to pay for residential energy-efficiency By Sejas Portillo, Rodolfo
  1. By: Jiang, Qi; Penn, Jerrod; Hu, Wuyang
    Keywords: Environmental Economics and Policy, Research Methods/Statistical Methods, Institutional and Behavioral Economics
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335800&r=dcm
  2. By: Gore, Christina C.; Carrel, Andre; Irwin, Elena G.
    Keywords: Environmental Economics and Policy, Resource/Energy Economics and Policy, Institutional and Behavioral Economics
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335437&r=dcm
  3. By: Gomez, Miguel I.; Mohammed, Broom; Li, Jie; Ballco, Petjon; Zhang, Yanan
    Keywords: Agribusiness, Marketing, Agricultural and Food Policy
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335699&r=dcm
  4. By: Shadi Haj-Yahia; Omar Mansour; Tomer Toledo
    Abstract: Discrete choice models (DCM) are widely employed in travel demand analysis as a powerful theoretical econometric framework for understanding and predicting choice behaviors. DCMs are formed as random utility models (RUM), with their key advantage of interpretability. However, a core requirement for the estimation of these models is a priori specification of the associated utility functions, making them sensitive to modelers' subjective beliefs. Recently, machine learning (ML) approaches have emerged as a promising avenue for learning unobserved non-linear relationships in DCMs. However, ML models are considered "black box" and may not correspond with expected relationships. This paper proposes a framework that expands the potential of data-driven approaches for DCM by supporting the development of interpretable models that incorporate domain knowledge and prior beliefs through constraints. The proposed framework includes pseudo data samples that represent required relationships and a loss function that measures their fulfillment, along with observed data, for model training. The developed framework aims to improve model interpretability by combining ML's specification flexibility with econometrics and interpretable behavioral analysis. A case study demonstrates the potential of this framework for discrete choice analysis.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.00016&r=dcm
  5. By: Hung Tran; Tien Mai
    Abstract: In many choice modeling applications, people demand is frequently characterized as multiple discrete, which means that people choose multiple items simultaneously. The analysis and prediction of people behavior in multiple discrete choice situations pose several challenges. In this paper, to address this, we propose a random utility maximization (RUM) based model that considers each subset of choice alternatives as a composite alternative, where individuals choose a subset according to the RUM framework. While this approach offers a natural and intuitive modeling approach for multiple-choice analysis, the large number of subsets of choices in the formulation makes its estimation and application intractable. To overcome this challenge, we introduce directed acyclic graph (DAG) based representations of choices where each node of the DAG is associated with an elemental alternative and additional information such that the number of selected elemental alternatives. Our innovation is to show that the multi-choice model is equivalent to a recursive route choice model on the DAG, leading to the development of new efficient estimation algorithms based on dynamic programming. In addition, the DAG representations enable us to bring some advanced route choice models to capture the correlation between subset choice alternatives. Numerical experiments based on synthetic and real datasets show many advantages of our modeling approach and the proposed estimation algorithms.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.04606&r=dcm
  6. By: Strickland, George; De Figueiredo Silva, Felipe; Vassalos, Michael; Ureta, Joan
    Keywords: Marketing, Consumer/Household Economics, Institutional and Behavioral Economics
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335712&r=dcm
  7. By: Duran Gabela, Carlos F.; Sarasty, Oscar; Lamino Jaramillo, Pablo; Boren-Alpizar, Amy
    Keywords: Institutional and Behavioral Economics, International Development, Community/Rural/Urban Development
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335928&r=dcm
  8. By: Regmi, Sabina; Kim, Ayoung; Mills, Devon P.; Green, John
    Keywords: Community/Rural/Urban Development, Teaching/Communication/Extension/Profession, Research Methods/Statistical Methods
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335602&r=dcm
  9. By: Herrington, Caitlin L.; Maredia, Mywish K.; Ortega, David L.; Reyes, Byron A.
    Keywords: Institutional and Behavioral Economics, Research Methods/Statistical Methods, International Development
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335918&r=dcm
  10. By: Cristina Gualdani (Queen Mary University of London); Shruti Sinha (Toulouse School of Economics, University of Toulouse Capitole, Toulouse, France.)
    Abstract: We study identification of preferences in static single-agent discrete choice models where decision makers may be imperfectly informed about the state of the world. We leverage the notion of one-player Bayes Correlated Equilibrium by Bergemann and Morris (2016) to provide a tractable characterization of the sharp identified set. We develop a procedure to practically construct the sharp identified set when the state of the world is continuous following a sieve approach, and provide sharp bounds on counterfactual outcomes of interest. We use our methodology and data on the 2017 UK general election to estimate a spatial voting model under weak assumptions on agents’ information about the returns to voting. Counterfactual exercises quantify the consequences of imperfect information on the well-being of voters and parties.
    Keywords: Discrete choice model, Bayesian Persuasion, Bayes Correlated Equilibrium, Incomplete Information, Partial Identification, Moment Inequalities, Spatial Model of Voting.
    JEL: C01 C25 D72 D80
    Date: 2023–06–21
    URL: http://d.repec.org/n?u=RePEc:qmw:qmwecw:949&r=dcm
  11. By: Rajan, Abhishek; Savchenko, Olesya; Prince, Candice; Leary, James
    Keywords: Environmental Economics and Policy, Institutional and Behavioral Economics, Research Methods/Statistical Methods
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335956&r=dcm
  12. By: Cao, Ting; House, Lisa A.; Chambers, Kerri-Ann; Mathews, Anne; Shelnutt, Karla
    Keywords: Food Consumption/Nutrition/Food Safety, Health Economics and Policy, Marketing
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335693&r=dcm
  13. By: Herrington, Caitlin L.; Ortega, David L.; Maredia, Mywish K.; Reyes, Byron A.
    Keywords: Research Methods/Statistical Methods, Institutional and Behavioral Economics, International Development
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335919&r=dcm
  14. By: Mehler, Maren F.; Vetter, Oliver A.
    Abstract: Machine Learning (ML) technologies have become the foundation of a plethora of products and services. While the economic potential of such ML-infused solutions has become irrefutable, there is still uncertainty on pricing. Currently, software testing is one area to benefit from ML services assisting in the creation of test cases; a task both complex and demanding human-like outputs. Yet, little is known on the willingness to pay of users, inhibiting the suppliers' incentive to develop suitable tools. To provide insights into desired features and willingness to pay for such ML-based tools, we perform a choice-based conjoint analysis with 119 participants in Germany. Our results show that a high level of accuracy is particularly important for users, followed by ease of use and integration into existing environments. Thus, we not only guide future developers on which attributes to prioritize but also which characteristics of ML-based services are relevant for future research.
    Date: 2023–06–14
    URL: http://d.repec.org/n?u=RePEc:dar:wpaper:138317&r=dcm
  15. By: Kramer, Berber; Waweru, Carol; Malacarne, Jonathan G.
    Keywords: International Development, Research Methods/Statistical Methods, Labor and Human Capital
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335632&r=dcm
  16. By: Wasserman-Olin, Rebecca; Gomez, Miguel I.; Schmit, Todd M.; Bjoerkman, Thomas
    Keywords: Marketing, Agribusiness, Risk and Uncertainty
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335520&r=dcm
  17. By: Wei, Xuan; McFadden, Brandon R.; Khachatryan, Hayk
    Keywords: Food Consumption/Nutrition/Food Safety, Research Methods/Statistical Methods
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335465&r=dcm
  18. By: Kramer, Berber; Waweru, Carol; Malacarne, Jonathan G.
    Keywords: International Development, Research Methods/Statistical Methods, Risk and Uncertainty
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335636&r=dcm
  19. By: Nian, Yefan; Duan, Dinglin; Uddin, Md Azhar; Gao, Zhifeng
    Keywords: Marketing, Institutional and Behavioral Economics, Research Methods/Statistical Methods
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335817&r=dcm
  20. By: Marson, Corissa; Abbey, Marie; Yue, Chengyan; Smith, Alan; Stowers, Carrie
    Keywords: Institutional and Behavioral Economics, Research Methods/Statistical Methods, Marketing
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335801&r=dcm
  21. By: Santhosh, Harikrishnan; Colson, Greg; Mullen, Jeffrey D.
    Keywords: Environmental Economics and Policy, Resource/Energy Economics and Policy, Institutional and Behavioral Economics
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335804&r=dcm
  22. By: Zhang, Xin; Wang, Jingjing; Fan, Shenggen
    Keywords: Food Consumption/Nutrition/Food Safety, Research Methods/Statistical Methods, Marketing
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335909&r=dcm
  23. By: Mallory Dickerson; Erin Martin; David McCune
    Abstract: In ranked-choice elections voters cast preference ballots which provide a voter's ranking of the candidates. The method of ranked-choice voting (RCV) chooses a winner by using voter preferences to simulate a series of runoff elections. Some jurisdictions which use RCV limit the number of candidates that voters can rank on the ballot, imposing what we term a truncation level, which is the number of candidates that voters are allowed to rank. Given fixed voter preferences, the winner of the election can change if we impose different truncation levels. We use a database of 1171 real-world ranked-choice elections to empirically analyze the potential effects of imposing different truncation levels in ranked-choice elections. Our general finding is that if the truncation level is at least three then restricting the number of candidates which can be ranked on the ballot rarely affects the election winner.
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.05966&r=dcm
  24. By: Hopkins, Kelsey A.; McKendree, Melissa G. S.; Ortega, David L.
    Keywords: Marketing, Food Consumption/Nutrition/Food Safety, Agricultural and Food Policy
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335732&r=dcm
  25. By: Penn, Jerrod; Howard, Gregory E.; Hu, Wuyang
    Keywords: Environmental Economics and Policy, Risk and Uncertainty, Research Methods/Statistical Methods
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335998&r=dcm
  26. By: Moeltner, Klaus; Puri, Roshan; Johnston, Robert J.
    Keywords: Environmental Economics and Policy, Research Methods/Statistical Methods, Resource/Energy Economics and Policy
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335807&r=dcm
  27. By: Bostwick, Valerie (Kansas State University); Severen, Christopher (Federal Reserve Bank of Philadelphia)
    Abstract: We provide evidence that graduated driver licensing (GDL) laws, originally intended to improve public safety, impact human capital accumulation. Many teens use automobiles to access both school and employment. Because school and work decisions are interrelated, the effects of automobile-specific mobility restrictions are ambiguous. Using a novel triple-difference research design, we find that restricting mobility significantly reduces high school dropout rates and teen employment. We develop a multiple discrete choice model that rationalizes unintended consequences and reveals that school and work are weak complements. Thus, improved educational outcomes reflect decreased access to leisure activities rather than reduced labor market access.
    Keywords: mobility restrictions, human capital, teen employment, graduated driver licensing, multiple discreteness
    JEL: I20 J24 J22 C35 R48
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp16183&r=dcm
  28. By: Thomas Demuynck; John Rehbeck
    Date: 2023–06–01
    URL: http://d.repec.org/n?u=RePEc:ulb:ulbeco:2013/359107&r=dcm
  29. By: Tran, Lan T.; Su, Ye; McCann, Laura M.
    Keywords: Agribusiness, Community/Rural/Urban Development, Institutional and Behavioral Economics
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335979&r=dcm
  30. By: Del Rossi, Gemma; Kling, Catherine L.; Rudik, Ivan
    Keywords: Environmental Economics and Policy, Resource/Energy Economics and Policy, Research Methods/Statistical Methods
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335730&r=dcm
  31. By: Thomas Demuynck; Umutcan Salman
    Date: 2022–05–01
    URL: http://d.repec.org/n?u=RePEc:ulb:ulbeco:2013/359108&r=dcm
  32. By: Moeltner, Klaus; Neill, Clinton L.; Ramsey, Austin F.; Wang, Huaiyu
    Keywords: Marketing, Research Methods/Statistical Methods, Food Consumption/Nutrition/Food Safety
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335680&r=dcm
  33. By: Conrad Kosowsky
    Abstract: In this paper, I prove that existence of pure-strategy Nash equilibrium in games with infinitely many players is equivalent to the axiom of choice.
    Date: 2023–05
    URL: http://d.repec.org/n?u=RePEc:arx:papers:2306.01790&r=dcm
  34. By: Samper, Bailey A.; Crocker, Andrew; Williams, Ryan Blake
    Keywords: Marketing, Food Consumption/Nutrition/Food Safety, Institutional and Behavioral Economics
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335645&r=dcm
  35. By: Elisabeth Gsottbauer; Michael Kirchler; Christian König-Kersting
    Abstract: Climate change constitutes one of the major challenges to humankind in the 21st century. To address this crisis, it is necessary to transform the economy and reduce greenhouse gas emissions. The finance industry has the potential to play a central role in this transformation by implementing sustainable investment and financing policies.We document climate mitigation preferences and attitudes toward the climate crisis of finance professionals — the key protagonists on financial markets — and climate experts — the key protagonists providing scientific findings. We use an incentivized choice experiment to measure the willingness to forgo individual payout to curb greenhouse gas emissions and survey participants to elicit their attitudes and beliefs toward the climate crisis. To learn how well both groups understand each other, we also ask participants what they believe the other stakeholder group believes. Our results provide suggestive evidence that finance professionals have a lower willingness to curb greenhouse gas emissions, measured through incentivized indifference valuations of carbon offsets, and are also less concerned about climate change compared to climate experts. Additionally, we find that the motivations and priorities of the two groups in addressing the climate crisis differ, with finance professionals being more driven by economic and reputational considerations and climate experts prioritizing the ecological and social consequences of the crisis. Finally, we find that finance professionals are less supportive of a carbon tax. Our findings have implications for policy and communication efforts, highlighting the importance of financial incentives and reputational concerns in motivating finance professionals to address the climate crisis.
    Keywords: Climate Crisis, Financial Professionals, Climate Experts, Greenhouse Gas Emissions, Carbon Tax
    Date: 2023–06
    URL: http://d.repec.org/n?u=RePEc:inn:wpaper:2023-06&r=dcm
  36. By: Kang, Qi; Carpio, Carlos E.; Wang, Chenggang; Boonsaeng, Tullaya; Hudson, Michael Darren
    Keywords: Research Methods/Statistical Methods, Marketing, Food Consumption/Nutrition/Food Safety
    Date: 2023
    URL: http://d.repec.org/n?u=RePEc:ags:aaea22:335792&r=dcm
  37. By: Sejas Portillo, Rodolfo
    Abstract: I study the effects of weather conditions on the economic valuation of energy-efficiency (EE) in the UK housing market. The benefits of EE features depend directly on the expected weather over the ownership time frame (e.g. insulation for maintaining heat during cold periods). However, due to its notorious unpredictability, current weather conditions provide little to no additional information about future weather conditions (beyond common knowledge such as seasonal temperatures). Using transaction-level data of over 5 million residential property sales in England and Wales, I find that weather conditions on the month the buying decision is made can disproportionately influence the EE valuation of properties: During rough weather (i.e. cold and rainy) the EE rating of a property has a stronger influence on its sale price than during favourable weather (i.e. warm and dry). I show that these results are unlikely to be driven by energy-cost optimisation or self-selection behaviour. The consistency of the results with intuitive predictions (in the UK the benefits of EE are much higher during rough weather) highlights their importance: People understand the benefits of EE yet make biased intertemporal valuations. I model and discuss psychological biases as the most likely mechanisms and find that salience appears to have the stronger effect. I also present a novel extension to the regression-kink design (RDK) for identifying and estimating the treatment effect when the running variable also moderates the effect of another variable (via interaction). I conclude with policy recommendation.
    JEL: D91 R31 Q41
    Date: 2023–06–01
    URL: http://d.repec.org/n?u=RePEc:ehl:lserod:119358&r=dcm
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