Search results for " quantitative method"
showing 10 items of 111 documents
RENT CREATION AND RENT SHARING: NEW MEASURES AND IMPACTS ON TOTAL FACTOR PRODUCTIVITY
2019
International audience; This analysis proposes new measures of rent creation and rent sharing and assesses their impact on productivity on cross-country-industry panel data. We find first that: (1) anticompetitive product market regulations positively affect rent creation and (2) employment protection legislation boosts hourly wages, particularly for low-skill workers. However, we find no significant impact of this employment legislation on rent sharing, as the hourly wage increases are offset by a negative impact on hours worked. Second, using regulation indicators as instruments, we find that rent creation and rent sharing both have a substantial negative impact on total factor productivi…
"Facta non verba" : an experiment on pledging and giving
2015
International audience; We design an experiment to investigate whether asking people to state how much they will donate to a charity (i.e., to pledge) increases their actual donation. Individuals’ endowment is either certain or a random variable. We study different types of pledges, namely, private, public and irrevocable, which differ in terms of the cost to the individual for not keeping the promise. We show that in absence of endowment uncertainty, private and public pledges are associated with lower donations as compared to donations in the no-pledge case: private pledges slightly reduce donations and public pledges reduce them more significantly. Donations increase with uncertainty (in…
Environmental expenditure interactions among OECD countries, 1995-2017
2021
International audience; How do countries respond to other countries when setting the level of their environmental expenditures? Using data from 1995-2017 on a sample of 28 OECD countries, we examine the nature and extent of strategic interactions in environmental expenditures among OECD countries using a spatial Durbin model including economic and political control variables and both economic and spatial weight matrices reflecting several interaction mechanisms. The results show the existence of significant positive spatial dependence in environmental spending suggesting that OECD countries consider their neighbors' behavior when making policy choices related to environmental expenditures. …
Two Half-Truths Make a Whole? On Bias in Self-Reports and Tracking Data
2019
The pervasive use of mobile information technologies brings new patterns of media usage, but also challenges to the measurement of media exposure. Researchers wishing to, for example, understand the nature of selective exposure on algorithmically driven platforms need to precisely attribute individuals’ exposure to specific content. Prior research has used tracking data to show that survey-based self-reports of media exposure are critically unreliable. So far, however, little effort has been invested into assessing the specific biases of tracking methods themselves. Using data from a multimethod study, we show that tracking data from mobile devices is linked to systematic distortions in sel…
Finding optimal finite biological sequences over finite alphabets: the OptiFin toolbox
2017
International audience; In this paper, we present a toolbox for a specific optimization problem that frequently arises in bioinformatics or genomics. In this specific optimisation problem, the state space is a set of words of specified length over a finite alphabet. To each word is associated a score. The overall objective is to find the words which have the lowest possible score. This type of general optimization problem is encountered in e.g 3D conformation optimisation for protein structure prediction, or largest core genes subset discovery based on best supported phylogenetic tree for a set of species. In order to solve this problem, we propose a toolbox that can be easily launched usin…
Selectivity in Probabilistic Causality: Drawing Arrows from Inputs to Stochastic Outputs
2011
Given a set of several inputs into a system (e.g., independent variables characterizing stimuli) and a set of several stochastically non-independent outputs (e.g., random variables describing different aspects of responses), how can one determine, for each of the outputs, which of the inputs it is influenced by? The problem has applications ranging from modeling pairwise comparisons to reconstructing mental processing architectures to conjoint testing. A necessary and sufficient condition for a given pattern of selective influences is provided by the Joint Distribution Criterion, according to which the problem of "what influences what" is equivalent to that of the existence of a joint distr…
Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes
2018
In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer.
Retrieval of aboveground crop nitrogen content with a hybrid machine learning method
2020
Abstract Hyperspectral acquisitions have proven to be the most informative Earth observation data source for the estimation of nitrogen (N) content, which is the main limiting nutrient for plant growth and thus agricultural production. In the past, empirical algorithms have been widely employed to retrieve information on this biochemical plant component from canopy reflectance. However, these approaches do not seek for a cause-effect relationship based on physical laws. Moreover, most studies solely relied on the correlation of chlorophyll content with nitrogen, and thus neglected the fact that most N is bound in proteins. Our study presents a hybrid retrieval method using a physically-base…
Human experts vs. machines in taxa recognition
2020
The step of expert taxa recognition currently slows down the response time of many bioassessments. Shifting to quicker and cheaper state-of-the-art machine learning approaches is still met with expert scepticism towards the ability and logic of machines. In our study, we investigate both the differences in accuracy and in the identification logic of taxonomic experts and machines. We propose a systematic approach utilizing deep Convolutional Neural Nets with the transfer learning paradigm and extensively evaluate it over a multi-pose taxonomic dataset with hierarchical labels specifically created for this comparison. We also study the prediction accuracy on different ranks of taxonomic hier…
Machinery Failure Approach and Spectral Analysis to study the Reaction Time Dynamics over Consecutive Visual Stimuli
2020
The reaction times of individuals over consecutive visual stimuli have been studied using spectral analysis and a failure machinery approach. The used tools include the fast Fourier transform and a spectral entropy analysis. The results indicate that the reaction times produced by the independently responding individuals to visual stimuli appear to be correlated. The spectral analysis and the entropy of the spectrum yield that there are features of similarity in the response times of each participant and among them. Furthermore, the analysis of the mistakes made by the participants during the reaction time experiments concluded that they follow a behavior which is consistent with the MTBF (…