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on Discrete Choice Models |
| By: | Nabila Arfaoui (University Catholic of Lyon, ESDES); Amandine Gnonlonfin (Université de Nice/IMREDD) |
| Abstract: | The study collects original monetary estimates for Nature Based Solutions (NBS) and benefits, with restoration approach in a basin context. A database of 187 monetary estimates is constructed to perform the first meta-analysis, which will assess how individuals value the NBS restoration measures and their benefits. We find that individuals value, in particular, global climate regulation, local environmental regulation, recreational activities, and habitat and biodiversity benefits. We find also that NBS measures aimed at floodplains and river streams are more highly valued. The results of this study suggest that the Willingness-to-pay (WTP) is weakly influenced by the methodological variables. While the contingent valuation method affects the WTP compared to studies using choice experiments, the payment and econometric method means have only a marginal effect. Survey modes are never significant. Finally, studies on the US and Europe country contexts show higher WTP than those conducted in Asia. |
| Keywords: | Nature Based Solution (NBS), Meta-Analysis, Ecosystem services, Willingness To Pay |
| JEL: | Q51 Q57 O13 |
| Date: | 2019–04 |
| URL: | http://d.repec.org/n?u=RePEc:fae:ppaper:2019.02&r=all |
| By: | Lechner, Michael; Okasa, Gabriel |
| Abstract: | In econometrics so-called ordered choice models are popular when interest is in the estimation of the probabilities of particular values of categorical outcome variables with an inherent ordering, conditional on covariates. In this paper we develop a new machine learning estimator based on the random forest algorithm for such models without imposing any distributional assumptions. The proposed Ordered Forest estimator provides a flexible estimation method of the conditional choice probabilities that can naturally deal with nonlinearities in the data, while taking the ordering information explicitly into account. In addition to common machine learning estimators, it enables the estimation of marginal effects as well as conducting inference thereof and thus providing the same output as classical econometric estimators based on ordered logit or probit models. An extensive simulation study examines the finite sample properties of the Ordered Forest and reveals its good predictive performance, particularly in settings with multicollinearity among the predictors and nonlinear functional forms. An empirical application further illustrates the estimation of the marginal effects and their standard errors and demonstrates the advantages of the flexible estimation compared to a parametric benchmark model. |
| Keywords: | Ordered choice models, random forests, probabilities, marginal effects, machine learning |
| JEL: | C14 C25 C40 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:usg:econwp:2019:08&r=all |
| By: | Nikhil Datta |
| Abstract: | This paper investigates the extent to which labour supply preferences are responsible for the marked rise in atypical work arrangements in the UK and US. By employing vignettes in a discrete job choice experiment in a representative survey, I estimate the distribution for preferences and willingness-to-pay over various job attributes. The list of attributes includes key distinguishing factors of typical and atypical work arrangements, such as security, work-related benefits, flexibility, autonomy and taxation implications. The results are indicative that the majority of the population prefer characteristics associated with traditional employee-employer relationships, and this preference holds even when analysing just the sub-sample of those in atypical work arrangements. Additionally, preferences across the UK and US are very similar, despite differences in labour market regulations. Rather than suggesting that labour supply preferences have contributed to the increase in atypical work arrangements, I find that the changing nature of work is likely to have significant negative welfare implications for many workers. |
| Keywords: | atypical work, self-employment, willingness-to-pay, experiment, labour supply preferences |
| JEL: | J22 J24 J32 J81 |
| Date: | 2019–07 |
| URL: | http://d.repec.org/n?u=RePEc:cep:cepdps:dp1632&r=all |