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on Discrete Choice Models |
| By: | Joshua Lanier; Jeremy Large; John Quah |
| Abstract: | We present a discrete choice, random utility model and a new estimation technique for analyzing consumer demand for large numbers of products. We allow the consumer to purchase multiple units of any product and to purchase multiple products at once (think of a consumer selecting a bundle of goods in a supermarket). In our model each product has an associated unobservable vector of attributes from which the consumer derives utility. Our model allows for heterogeneous utility functions across consumers, complex patterns of substitution and complementarity across products, and nonlinear price effects. The dimension of the attribute space is, by assumption, much smaller than the number of products, which effectively reduces the size of the consumption space and simplifies estimation. Nonetheless, because the number of bundles available is massive, a new estimation technique, which is based on the practice of negative sampling in machine learning, is needed to sidestep an intractable likelihood function. We prove consistency of our estimator, validate the consistency result through simulation exercises, and estimate our model using supermarket scanner data. |
| Date: | 2022–06–27 |
| URL: | http://d.repec.org/n?u=RePEc:oxf:wpaper:998&r=dcm |
| By: | Ksenia Panidi; Yaroslava Grebenschikova; Vasily Klucharev |
| Abstract: | In the present study, we use an experimental setting to explore the effects of sugar-free labels on the willingness to pay for food products. In our experiment, participants placed bids for sugar-containing and analogous sugar-free products in a Becker-deGroot-Marschak auction to determine the willingness to pay. Additionally, they rated each product on the level of perceived healthiness, sweetness, tastiness and familiarity with the product. We then used structural equation modelling to estimate the direct, indirect and total effect of the label on the willingness to pay. The results suggest that sugar-free labels significantly increase the willingness to pay due to the perception of sugar-free products as healthier than sugar-containing ones. However, this positive effect is overridden by a significant decrease in perceived sweetness (and hence, tastiness) of products labelled as sugar-free compared to sugar-containing products. As in our sample, healthiness and tastiness are positively related, while healthiness and sweetness are related negatively, these results suggest that it is health-sweetness rather than health-tastiness tradeoff that decreases the efficiency of the sugar-free labelling in nudging consumers towards healthier options. |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2311.09885&r=dcm |
| By: | Payró, Clara; Taherzadeh, Oliver; van Oorschot, Mark; Koch, Julia; Koch, Julia; Marselis, Suzanne |
| Abstract: | The environmental gains of dietary change are often assessed in relation to average national diets, overlooking differences in individual consumption habits and preferences. As a result, we ignore the roles and impacts of different consumer groups in a sustainable dietary transition. This study combines micro data on food intake and consumer behaviour to elicit the likely environmental gains of dietary shifts. We focus on the Netherlands owing to the county’s ambition to halve its dietary footprint by 2050. Linking food recall survey data from a cross-section of the population (n=4, 313), life cycle inventory analysis for 220 food products, and behavioural survey data (n=1, 233), we estimate the dietary footprints of consumer groups across water, land, biodiversity and greenhouse gas footprints. We find that meat and dairy significantly contribute to the dietary greenhouse gas (GHG) footprint (59%), land footprint (55%), and biodiversity footprint (57%) of all consumer groups, and that male consumers impose a 30-32% greater burden than women across these impact areas. Our scenario analysis reveals that simply replacing cow milk with soy milk could reduce the GHG, land and biodiversity footprints of food consumption by ±8% if widely adopted by the Dutch adult population. These impacts could be further reduced by ±20% from a full adoption of a sustainable diet, as recommended by the EAT-Lancet Commission, but would significantly increase the blue water footprint of Dutch food consumption. While the EAT-Lancet recommended diet is preferred in terms of impacts and nutrition, it would necessitate a complete overhaul of individual dietary habits, whereas shifting to soy milk is a simple single product substitution and a more accessible choice for consumers. However, when incorporating gender- and age-specific willingness for meat and dairy consumption reduction, the environmental gains resulting from partial adoption of the EAT diet and No-Milk diet diminish to a mere ±4.5% and ±0.8%, respectively. Consequently, consumer motivation alone is insufficient to realise the significant environmental gains often promised by dietary change. Our findings highlight that specific and targeted policies are needed to overcome the barriers that consumers face to adopting a more sustainable diet. |
| Date: | 2023–11–16 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:m98kr&r=dcm |
| By: | Tijmen van Kempen; Sven Damen |
| Abstract: | Mandatory energy efficiency disclosure policies are increasingly being used by governments around the world to reduce information-driven market failures. We exploit two policy changes in Flanders to study the causal effect of mandatory energy efficiency disclosure policies on house prices. We find that the introduction of mandatory energy performance certificates in 2008 that include an energy efficiency score did not affect the association between energy efficiency and sales prices, indicating that the policy change did not reduce information frictions. However, the introduction of EPC labels in 2019 affected the willingness to pay for energy efficiency. |
| Keywords: | Energy Consumption; energy performance certificates; Information Asymmetry |
| JEL: | R3 |
| Date: | 2023–01–01 |
| URL: | http://d.repec.org/n?u=RePEc:arz:wpaper:eres2023_252&r=dcm |
| By: | Hannes Ullrich; Michael Allan Ribers |
| Abstract: | We analyze how machine learning predictions may improve antibiotic prescribing in the context of the global health policy challenge of increasing antibiotic resistance. Estimating a binary antibiotic treatment choice model, we find variation in the skill to diagnose bacterial urinary tract infections and in how general practitioners trade off the expected cost of resistance against antibiotic curative benefits. In counterfactual analyses we find that providing machine learning predictions of bacterial infections to physicians increases prescribing efficiency. However, to achieve the policy objective of reducing antibiotic prescribing, physicians must also be incentivized. Our results highlight the potential misalignment of social and heterogeneous individual objectives in utilizing machine learning for prediction policy problems. |
| Date: | 2023–11–13 |
| URL: | http://d.repec.org/n?u=RePEc:bdp:dpaper:0027&r=dcm |
| By: | Alain Hecq; Daniel Velasquez-Gaviria |
| Abstract: | This paper introduces new techniques for estimating, identifying and simulating mixed causal-noncausal invertible-noninvertible models. We propose a framework that integrates high-order cumulants, merging both the spectrum and bispectrum into a single estimation function. The model that most adequately represents the data under the assumption that the error term is i.i.d. is selected. Our Monte Carlo study reveals unbiased parameter estimates and a high frequency with which correct models are identified. We illustrate our strategy through an empirical analysis of returns from 24 Fama-French emerging market stock portfolios. The findings suggest that each portfolio displays noncausal dynamics, producing white noise residuals devoid of conditional heteroscedastic effects. |
| Date: | 2023–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2310.19543&r=dcm |
| By: | Costas Arkolakis; Fabian Eckert; Rowan Shi |
| Abstract: | We introduce a general quantifiable framework to study the location decisions of multinational firms. In the model, firms choose in which locations to pay the fixed costs of setting up production, taking into account potential complementarities among production locations. The firm’s location choice problem is combinatorial because the marginal value of an individual production location depends on its complete set of production sites. We develop a computational method to solve such problems and aggregate optimal decisions across heterogeneous firms. We use our calibrated model to study Brexit and the recent sanctions war with Russia. In both counterfactuals, changes in the location decisions of multinationals are driving real wage responses. |
| JEL: | F12 F21 F6 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:31877&r=dcm |
| By: | Dominique Desbois (AgroParisTech) |
| Abstract: | This paper presents the extension to the analysis of interval data of a statistical processing chain for the dual estimation of specific cost and gross margin distributions. |
| Keywords: | Interval data analysis, conditional quantile estimates, Farm accounting data network |
| Date: | 2023–11–02 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04271145&r=dcm |