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on Discrete Choice Models |
| By: | Stavroula Tsigou (AUA); Stathis Klonaris (AUA) |
| Abstract: | Salt stress noxiously shocks agricultural yield all over the world affecting production either it is for subsistence or economic outcomes. However, the market for anti-salinity products is still developing and little is known about the willingness to pay for agricultural supplies, these have been largely focused on using stated preference methods only. This paper applied both Contingent and Inferred Valuation Method for the purpose of examining the determinants of farmers’ willingness to pay for two packages of an innovative anti-salinity fertilizer, which does not yet exist in the market, in the regions of southwest Greece. With the aid of questionnaire, primary data were obtained from 150 farmers. Willingness to pay for the two packages of liquid fertilizer was measured through dichotomous choice. For the econometric analysis, interval regression model was used. The results revealed that farmers’ willingness to pay for the specific anti-salinity product is influenced by a host of factors. Especially, the regression showed that the size of cultivated land, the level of education, the knowledge scale about salinity, the package of liquid fertilizer that farmers usually buy and the consequentiality script have a positive effect on willingness to pay, whilst hypothetical bias and inferred valuation method have a negative effect. Also, we examined a methodological issue concerning the order that the package of fertilizer was appeared in the willingness to pay question which has a positive effect on willingness to pay. The implication is that taking these factors into account while large companies are looking for new and profitable products by investing for research and development enables companies’ managers to come up with projects that win acceptance from the farmers. |
| Keywords: | salinity; willingness to pay; contingent valuation; inferred valuation; dichotomous choice |
| JEL: | C21 M31 Q31 Q16 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:aua:wpaper:2018-3&r=dcm |
| By: | Andor, Mark Andreas; Frondel, Manuel; Sommer, Stephan |
| Abstract: | The production of electricity on the basis of renewable energy technologies is a classic example of an impure public good. It is often discriminatively financed by industrial and household consumers, such as in Germany, where the energy-intensive sector benefits from a far-reaching exemption rule, while all other electricity consumers are forced to bear a higher burden. Based on randomized information treatments in a stated-choice experiment among about 11,000 German households, we explore whether this coercive payment rule affects households' willingness-to-pay (WTP) for green electricity. Our central result is that reducing inequity by abolishing the exemption for the energyintensive industry raises households' WTP substantially. |
| Keywords: | stated-choice experiment,behavioral economics,fairness |
| JEL: | D03 D12 H41 Q20 Q50 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:rwirep:759&r=dcm |
| By: | Stefano DellaVigna |
| Abstract: | What is the role of structural estimation in behavioral economics? I discuss advantages, and limitations, of the work in Structural Behavioral Economics. I also cover common modeling choices and how to get started. Among the advantages, I argue that structural estimation builds on, and expands, a classical behavioral tool, simple calibrations, and that it benefits from the presence of a few parsimonious behavioral models which can be taken to the data. Estimation is also well suited for experimental work, common in behavioral economics, as it can lead to improvements in the experimental design. In addition, at a time where policy implications of behavioral work are increasingly discussed, it is important to ground these policy implications in (estimated) models. Structural work, however, has important limitations, which are relevant to its behavioral applications. Estimation takes much longer and the extra degree of complexity can make it difficult to know which of a series of assumptions is driving the results. For related reasons, it is also easy to over-reach with the welfare implications. Taking this into account, I provide a partial how-to guide to structural behavioral economics, covering: (i) the choice of estimation method; (ii) the modeling of heterogeneity; (iii) identification and sensitivity. Finally, I discuss common issues for the estimation of leading behavioral models. I illustrate this discussion with selected coverage of existing work in the literature. |
| JEL: | C1 C9 D03 D9 |
| Date: | 2018–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24797&r=dcm |