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on Discrete Choice Models |
| By: | Margaux Lapierre (US ODR - Observatoire des Programmes Communautaires de Développement Rural - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement); Gwenolé Le Velly (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier); Douadia Bougherara (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier); Raphaële Préget (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier); Alexandre Sauquet (CEE-M - Centre d'Economie de l'Environnement - Montpellier - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement - Institut Agro Montpellier - Institut Agro - Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement - UM - Université de Montpellier) |
| Abstract: | Agri-environmental schemes (AES) are part of the main tools used by decision makers to trigger a transition in agricultural practices but one of the factors that discourages farmers from enrolling is the uncertainty of the costs and benefits associated with the adoption of the new practices. In this study, we distinguish between the "internal uncertainty" that is related to the characteristics of the farmer and his/her parcels and "external uncertainty", which is related to the occurrence of external events. We propose three innovations to better account for uncertainty in AES design: the possibility to suspend the conditions of the contract for one year, an opt-out option after three years and the opportunity for farmers to share their experience in peer-groups. We test their attractiveness through a choice experiment and analyze our results using a mixed logit model. We find that proposing AES that allow suspending the conditions of the contract for one year enhances participation. |
| Keywords: | Agri-environmental Measures,Uncertainty,Flexibility,Choice Experiment,Pesticides |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03811624&r=dcm |
| By: | Aeppli, Clem; Ruedin, Didier (University of Neuchâtel) |
| Abstract: | Different measures exist to capture agreement, consensus, concentration, dispersion, and polarization in ordinal data. We compare consensus scores across specific situations for a better understanding of how different measures work in practice: constructed cases, simulated data where we know the underlying distribution, and empirical data. Although researchers have solved the ‘problem’ of measuring agreement, consensus, and polarization several times, we highlight similarities and equivalence across some existing approaches, while others differ substantially. The choice of method can lead to substantively different conclusions, and we recommend that researchers use a combination of measures and use graphics to examine the distribution qualitatively. |
| Date: | 2022–10–24 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:syzbr&r=dcm |
| By: | Michelle Acampora (University of Zurich); Francesco Capozza (Erasmus University of Rotterdam); Vahid Moghani (Erasmus University of Rotterdam) |
| Abstract: | This paper assesses the impact of a mental health literacy intervention on the demand for mental health support among university students. We run a field experiment with 2,978 university students from one of the largest Dutch universities. The literacy intervention provides information on the benefits of care-seeking and its potential returns in terms of academic performance. The intervention increases the willingness-to-pay for a mental health app among male respondents. Moreover, the information increases (decreases) the demand for information about coaching (psychological) services. We document that this substitution is concentrated among students with low to moderate anxiety/depressive symptoms, while the students with severe symptoms increase their demand for coaching without reducing their demand for psychological services. An increased perceived effectiveness of low-intensity therapy is likely to be the mechanisms. In a follow-up survey three weeks later, we find that the treated female respondents have improved their mental health. Finally, a model of mental health investment decisions in the presence of (self-)image concerns rationalizes the results. |
| Keywords: | Mental Health Literacy, Demand for Mental Health Support, Beliefs, Stigma, Survey Experiment |
| JEL: | C93 D83 D91 I12 I31 |
| Date: | 2022–11–13 |
| URL: | http://d.repec.org/n?u=RePEc:tin:wpaper:20220079&r=dcm |
| By: | James, Amity; Crowe, Adam; Tually, Selina; Sharam, Andrea; Faulkner, Debbie; Cebulla, Andreas; Hodgson, Helen; Webb, Eileen; Coram, Veronica; Singh, Ranjodh |
| Abstract: | This research investigates lower income older households’ preferences for a range of alternative housing models and examines which of these would best meet their needs, as well as identifying ways to support households deciding their housing options. The findings of this project provide key evidence for consideration in developing a market for alternative housing options. Seven alternative housing models were presented to a nationally representative sample of older people. These composite models—each with a unique combination of tenure, construction, location, social composition, shared space and technology characteristics—included a mixed use apartment building option; a cooperative housing option; a communal housing option; a transportable home option; a shared equity home ownership option; a dual key property option; and a village-style housing option. The shared equity home ownership model; cooperative housing model; and transportable home model were substantially preferred by lower income housed older Australians. All three alternative housing models met the short and long-term housing needs of the respondents and would also deliver benefits in terms of people’s non-shelter aspirations for home including independence, privacy, security of tenure, ability to have companion animals, and room for friends, family or a carer to stay. Survey respondents expressed a strong liking for rights of ownership (84%)—through the dual key housing option and the shared equity housing option—and a long lease option (83%). Housing options that included other tenure arrangements, such as shared governance and management (59%) and land owned and retained by government (68%), were considered less desirable. |
| Date: | 2022–11–09 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:2t6xs&r=dcm |
| By: | Gregory Cox |
| Abstract: | This paper describes how to reparameterize low-dimensional factor models to fit the weak identification theory developed for generalized method of moments (GMM) models. Identification conditions in low-dimensional factor models can be close to failing in a similar way to identification conditions in instrumental variables or GMM models. Weak identification estimation theory requires a reparameterization to separate the weakly identified parameters from the strongly identified parameters. Furthermore, identification-robust hypothesis tests benefit from a reparameterization that makes the nuisance parameters strongly identified. We describe such a reparameterization in low-dimensional factor models with one or two factors. Simulations show that identification-robust hypothesis tests that require the reparameterization are less conservative than identification-robust hypothesis tests that use the original parameterization. The simulations also show that estimates of the number of factors frequently include weakly identified factors. An empirical application to a factor model of parental investments in children is included. |
| Date: | 2022–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2211.00329&r=dcm |
| By: | Boimah, M.; Gunarathne, A.; Behrendt, L. |
| Abstract: | Ghana imports more milk and other dairy products yearly than it produces. Even for what is processed domestically, almost all are exclusively made from imported milk powder. It is in this regard that this study was initiated to analyze the barriers to the local dairy sector’s competitiveness employing both primary and secondary data sources. For the collection of primary data, in-depth interviews were conducted with key informants. A total of 34 actors along the local fresh milk and milk powder value chains were sampled and interviewed and the data descriptively analyzed. Results show that the local milk value chain of Ghana is informal, not developed and with minimal value addition to fresh milk compared to the value chain of imported milk powder. Moreover, local products sold on the Ghanaian markets do not undergo any form of safety tests and have not been approved by the regulatory and standard authorities. Further, a host of challenges along the local milk value chain are identified as factors limiting its competitiveness. Nevertheless, a window of opportunity for developing the local milk value chain is presented considering the growing demand for fresh milk-based dairy products in Ghana as well as increasing international trade to integrate into the Global Value Chain. |
| Keywords: | International Relations/Trade, Livestock Production/Industries |
| Date: | 2021–06 |
| URL: | http://d.repec.org/n?u=RePEc:ags:aiea21:329291&r=dcm |
| By: | Wolff, Irenaeus |
| JEL: | C72 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:vfsc22:264072&r=dcm |
| By: | Xiaolin Sun |
| Abstract: | We propose a new estimator for heterogeneous treatment effects in a partially linear model (PLM) with many exogenous covariates and a possibly endogenous treatment variable. The PLM has a parametric part that includes the treatment and the interactions between the treatment and exogenous characteristics, and a nonparametric part that contains those characteristics and many other covariates. The new estimator is a combination of a Robinson transformation to partial out the nonparametric part of the model, the Smooth Minimum Distance (SMD) approach to exploit all the information of the conditional mean independence restriction, and a Neyman-Orthogonalized first-order condition (FOC). With the SMD method, our estimator using only one valid binary instrument identifies both parameters. With the sparsity assumption, using regularized machine learning methods (i.e., the Lasso method) allows us to choose a relatively small number of polynomials of covariates. The Neyman-Orthogonalized FOC reduces the effect of the bias associated with the regularization method on estimates of the parameters of interest. Our new estimator allows for many covariates and is less biased, consistent, and $\sqrt{n}$-asymptotically normal under standard regularity conditions. Our simulations show that our estimator behaves well with different sets of instruments, but the GMM type estimators do not. We estimate the heterogeneous treatment effects of Medicaid on individual outcome variables from the Oregon Health Insurance Experiment. We find using our new method with only one valid instrument produces more significant and more reliable results for heterogeneous treatment effects of health insurance programs on economic outcomes than using GMM type estimators. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.15829&r=dcm |
| By: | Lukas Gonon |
| Abstract: | This article studies deep neural network expression rates for optimal stopping problems of discrete-time Markov processes on high-dimensional state spaces. A general framework is established in which the value function and continuation value of an optimal stopping problem can be approximated with error at most $\varepsilon$ by a deep ReLU neural network of size at most $\kappa d^{\mathfrak{q}} \varepsilon^{-\mathfrak{r}}$. The constants $\kappa,\mathfrak{q},\mathfrak{r} \geq 0$ do not depend on the dimension $d$ of the state space or the approximation accuracy $\varepsilon$. This proves that deep neural networks do not suffer from the curse of dimensionality when employed to solve optimal stopping problems. The framework covers, for example, exponential L\'evy models, discrete diffusion processes and their running minima and maxima. These results mathematically justify the use of deep neural networks for numerically solving optimal stopping problems and pricing American options in high dimensions. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.10443&r=dcm |
| By: | Rub\'en Loaiza-Maya; Didier Nibbering |
| Abstract: | Variational Bayes methods are a scalable estimation approach for many complex state space models. However, existing methods exhibit a trade-off between accurate estimation and computational efficiency. This paper proposes a variational approximation that mitigates this trade-off. This approximation is based on importance densities that have been proposed in the context of efficient importance sampling. By directly conditioning on the observed data, the proposed method produces an accurate approximation to the exact posterior distribution. Because the steps required for its calibration are computationally efficient, the approach is faster than existing variational Bayes methods. The proposed method can be applied to any state space model that has a closed-form measurement density function and a state transition distribution that belongs to the exponential family of distributions. We illustrate the method in numerical experiments with stochastic volatility models and a macroeconomic empirical application using a high-dimensional state space model. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2210.11010&r=dcm |
| By: | Ismaël Rafaï (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur); Arthur Ribaillier (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - COMUE UCA - COMUE Université Côte d'Azur (2015-2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur); Dorian Jullien (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | The aim of this article is to better understand how judgements about nudge acceptability are formed and whether they can be manipulated. We conducted a randomized experiment with N = 171 participants to test whether acceptability judgements could be (1) more favourable when the decision to implement the nudges was made following a consultation with the targeted population and (2) influenced by the joint framing of the nudge's purpose and effectiveness (in terms of an increase in desirable behaviour versus decrease in undesirable behaviour). We tested these hypotheses on various nudge scenarios and obtained mixed results that do not clearly support our hypotheses for all nudge scenarios. A surprising result that calls for further work is that by mentioning that a nudge had been implemented through a consultation with the targeted population its acceptability could be lowered. |
| Keywords: | behavioural public policies,nudges,acceptability,framing,consultation |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03747844&r=dcm |