Decomposing the effects of time on the social acceptability of biotechnology using age-period-cohort-country models

Rousselière, Damien; Rousselière, Samira

doi:10.1177/0963662515622394

Cited by 8 publications

(11 citation statements)

References 76 publications

(119 reference statements)

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“…[ 34 ]. In effect, we underline a more important age effect for cisgenesis than for transgenesis, with increasingly weaker support as age increase (as in Rousselière & Rousselière [ 47 ]. Consumers can balance out the risk with expected benefits for technology, but this connection is plural [ 34 ], depending on the social and cognitive resources available that may influence their perception of biotechnologies.…”

Section: Discussionmentioning

confidence: 72%

“…Therefore, it is as if there is a convergence between European countries. Unfortunately our model is not flexible enough to include random effects, as in Rousselière & Rousselière [ 47 ] where divergence between European countries is largely explain by the strategy of national political parties. However, if we compare transgenesis and cisgenesis, there is still a high difference between countries about social acceptability.…”

Section: Discussionmentioning

confidence: 99%

“…This point is confirmed by meta-analysis of experimental economic studies underlining greater resistance from the European consumers than from American or Asian ones [ 45 , 46 ]. European studies [ 1 , 47 ] or comparative studies between countries [ 43 ] show that beyond the average European citizen there is a great diversity in national configurations. There is a convergence between European countries on the general attitude towards biotechnologies, with the caveat that recent members of the European Union show an increase in the number of citizens who are, ex ante , more favorable to them [ 48 , 49 ].…”

Section: Literature Review: Four Proposals Regarding the Acceptabilitmentioning

confidence: 99%

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Is biotechnology (more) acceptable when it enables a reduction in phytosanitary treatments? A European comparison of the acceptability of transgenesis and cisgenesis

Rousselière¹,

Rousselière²

2017

PLoS ONE

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Reduced pesticide use is one of the reasons given by Europeans for accepting new genetic engineering techniques. According to the advocates of these techniques, consumers are likely to embrace the application of cisgenesis to apple trees. In order to verify the acceptability of these techniques, we estimate a Bayesian multilevel structural equation model, which takes into account the multidimensional nature of acceptability and individual, national, and European effects, using data from the Eurobarometer 2010 73.1 on science. The results underline the persistence of clear differences between European countries and whilst showing considerable defiance, a relatively wider acceptability of vertical gene transfer as a means of reducing phytosanitary treatments, compared to horizontal transfer.

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Section: Discussionmentioning

confidence: 72%

Section: Discussionmentioning

confidence: 99%

Section: Literature Review: Four Proposals Regarding the Acceptabilitmentioning

confidence: 99%

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Is biotechnology (more) acceptable when it enables a reduction in phytosanitary treatments? A European comparison of the acceptability of transgenesis and cisgenesis

Rousselière¹,

Rousselière²

2017

PLoS ONE

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“…Model (1) is omnipresent in a wide array of fields often arising from repeated cross-sectional studies. Recent contributions among many others include aging (Yang, 2011), blood pressure (Tu et al, 2011), health inequalities (Jeon et al, 2016), social capital (Schwadel and Stout, 2012), social acceptability of biotechnology (Rousselière and Rousselière, 2017), household savings (Fukuda, 2006) and obesity epidemic (Reither et al, 2009). 25 Nested within the age-period-cohort model is the simpler age-cohort model which arises for γ ≡ 0, meaning that there is no period effect: log µ ik = α i + β k .…”

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confidence: 99%

A comparison of in-sample forecasting methods

Bischofberger

Hiabu

Mammen

et al. 2019

Computational Statistics & Data Analysis

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In-sample forecasting is a recent continuous modification of well-known forecasting methods based on aggregated data. These aggregated methods are known as age-cohort methods in demography, economics, epidemiology and sociology and as chain ladder in non-life insurance. Data is organized in a two-way table with age and cohort as indices, but without measures of exposure. It has recently been established that such structured forecasting methods based on aggregated data can be interpreted as structured histogram estimators. Continuous in-sample forecasting transfers these classical forecasting models into a modern statistical world including smoothing methodology that is more efficient than smoothing via histograms. All in-sample forecasting estimators are collected and their performance is compared via a finite sample simulation study.All methods are extended via multiplicative bias correction. Asymptotic theory is being developed for the histogram-type method of sieves and for the multiplicatively corrected estimators. The multiplicative bias corrected estimators improve all other known in-sample forecasters in the simulation study. The density projection approach seems to have the best performance with forecasting based on survival densities being the runner-up. not reported) claims is often solved via model (2): For each past claim, one considers the date (cohort i) when the accident had happened and the delay (age k) there was until the claim was reported to the insurer.Hence, cohort and age satisfy i + k − 1 ≤ today; given a certain year-wise aggregation. This information is then used to estimate the number of future claims µ ik , i + k − 1 > today, for accidents in the past, i ≤ today.Under model (2), the parameters α i and β k for each cohort i and age k can be estimated from past data. 50Assuming a maximum delay (usually 7 to 10 years in practice, depending on the business line), the estimates of the parameters can be used to forecast the number of future claims with i + k − 1 > today. More details of this age-cohort-reserving example are given in the recent contribution Harnau and Nielsen (2018) and are also included in the highly-cited overview paper of actuarial reserving (England and Verrall, 2002).Other examples where no significant period effect has been found include among many others cancer stud-55 ies (Leung et al., 2002;Remontet et al., 2003), returns due to education (Duraisamy, 2002), unemployment numbers (Wilke, 2017), mesothelioma mortality (Peto et al., 1995;Martínez-Miranda et al., 2014).Given the importance of age-period-cohort models and age-cohort models, it is surprising that continuous versions have not been considered much in the literature. Continuous modeling avoids inefficient pre-smoothing and is in line with recent trends around big data and the drive of modeling and understanding 60 every individual separately. Modeling every individual separately, possibly with additional covariates, results in the estimation of a large number of parameters. An increase of dimension means that ...

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“…In this case confidence in the best-fitting model should be reduced because other models can be selected in different samples from the same population. We focused on this scenario because researchers might be especially tempted to base inference only on the best-fitting model when AIC or BIC differences are large, and disregard the effects of model selection uncertainty on inference (Preacher & Merkle, 2012; Rousseliere & Rousseliere, 2016; Song & Lee, 2002). We investigated whether bootstrap selection rates can be indicative of model selection uncertainty and therefore serve as a protection against undue confidence.…”

Section: Discussionmentioning

confidence: 99%

Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update

Lubke

Campbell

McArtor

et al. 2016

Structural Equation Modeling: A Multidisciplinary Journal

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Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indices such as AIC or BIC, and inference is done based on the selected best-fitting model. This practice does not account for the possibility that due to sampling variability, a different model might be selected as the preferred model in a new sample from the same population. A previous study illustrated a bootstrap approach to gauge this model selection uncertainty using two empirical examples. The current study consists of a series of simulations to assess the utility of the proposed bootstrap approach in multi-group and mixture model comparisons. These simulations show that bootstrap selection rates can provide additional information over and above simply relying on the size of AIC and BIC differences in a given sample.

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Decomposing the effects of time on the social acceptability of biotechnology using age-period-cohort-country models

Cited by 8 publications

References 76 publications

Is biotechnology (more) acceptable when it enables a reduction in phytosanitary treatments? A European comparison of the acceptability of transgenesis and cisgenesis

Is biotechnology (more) acceptable when it enables a reduction in phytosanitary treatments? A European comparison of the acceptability of transgenesis and cisgenesis

A comparison of in-sample forecasting methods

Assessing Model Selection Uncertainty Using a Bootstrap Approach: An Update

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