Publications
In this paper we investigate if government balanced-budget rules together with endogenous taxation may lead to aggregate instability in an endogenous growth framework. After highlighting the differences with the exogenous growth framework, we prove that under counter-cyclical consumption taxes, while there exists a unique balanced growth path, sunspot equilibria based on self-fulfilling expectations occur through a form of global indeterminacy. In addition, we argue that this result is empirically plausible for a large set of OECD countries and that it may also emerge with endogenous income taxes.
An acceleration index is proposed as a novel indicator to track the dynamics of COVID-19 in real-time. Using data on cases and tests in France for the period between the first and second lock-downs—May 13 to October 25, 2020—our acceleration index shows that the pandemic resurgence can be dated to begin around July 7. It uncovers that the pandemic acceleration was stronger than national average for the [59–68] and especially the 69 and older age groups since early September, the latter being associated with the strongest acceleration index, as of October 25. In contrast, acceleration among the [19–28] age group was the lowest and is about half that of the [69–78]. In addition, we propose an algorithm to allocate tests among French “départements” (roughly counties), based on both the acceleration index and the feedback effect of testing. Our acceleration-based allocation differs from the actual distribution over French territories, which is population-based. We argue that both our acceleration index and our allocation algorithm are useful tools to guide public health policies as France might possibly enter a third lock-down period with indeterminate duration.
Workers' propensity to migrate to another local labor market varies a lot by occupation. We use the model developed by Schmutz and Sidibé (2019) to quantify the impact of mobility costs and search frictions on this mobility gap. We estimate the model on a matched employer-employee panel dataset describing labor market transitions within and between the 30 largest French cities for two groups at both ends of the occupational spectrum and find that: (i) mobility costs are very comparable in the two groups, so they are three times higher for blue-collar workers relative to their respective expected income; (ii) Depending on employment status, spatial frictions are between 2 and 3 times higher for blue-collar workers; (iii) Moving subsidies have little (and possibly negative) impact on the mobility gap, contrary to policies targeting spatial frictions; (iv) Mobility-enhancing policies have almost no impact on the unemployment gap.
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Les difficultés de la décision administrative durant la crise de la Covid-19 sont mises en perspective par les approches de l’École autrichienne et de l’analyse du Choix public. Cette crise et l’encadrement de l’économie par les mesures de confinement ajoutent une page originale au débat sur le calcul économique dans une économie planifiée. L’article propose une réflexion sur le niveau de la prise de décision (centralisation/décentralisation) et de la nécessaire implication des acteurs dans le processus. La diffusion de la connaissance est envisagée selon le point de vue hayékien, ce qui permet d’expliquer que les interactions humaines sont essentielles au fonctionnement des sociétés. Le retour partiel à l’autarcie provoqué par les mesures de confinement heurte de manière frontale l’organisation des sociétés ouvertes avec des conséquences émergentes préoccupantes sans nécessairement avoir des résultats probants sur le plan sanitaire.
BACKGROUND:
In high-dimensional data analysis, the complexity of predictive models can be reduced by selecting the most relevant features, which is crucial to reduce data noise and increase model accuracy and interpretability. Thus, in the field of clinical decision making, only the most relevant features from a set of medical descriptors should be considered when determining whether a patient is healthy or not. This statistical approach known as feature selection can be performed through regression or classification, in a supervised or unsupervised manner. Several feature selection approaches using different mathematical concepts have been described in the literature. In the field of classification, a new approach has recently been proposed that uses the y-metric, an index measuring separability between different classes in heart rhythm characterization. The present study proposes a filter approach for feature selection in classification using this y-metric, and evaluates its application to automatic atrial fibrillation detection.
METHODS:
The stability and prediction performance of the [Formula: see text]-metric feature selection approach was evaluated using the support vector machine model on two heart rhythm datasets, one extracted from the PhysioNet database and the other from the database of Marseille University Hospital Center, France (Timone Hospital). Both datasets contained electrocardiogram recordings grouped into two classes: normal sinus rhythm and atrial fibrillation. The performance of this feature selection approach was compared to that of three other approaches, with the first two based on the Random Forest technique and the other on receiver operating characteristic curve analysis.
RESULTS:
The [Formula: see text]-metric approach showed satisfactory results, especially for models with a smaller number of features. For the training dataset, all prediction indicators were higher for our approach (accuracy greater than 99% for models with 5 to 17 features), as was stability (greater than 0.925 regardless of the number of features included in the model). For the validation dataset, the features selected with the y-metric approach differed from those selected with the other approaches; sensitivity was higher for our approach, but other indicators were similar.
CONCLUSION:
This filter approach for feature selection in classification opens up new methodological avenues for atrial fibrillation detection using short electrocardiogram recordings.
Preferences elicitation can be a challenging exercise for citizens participating in assessment surveys. It is even more challenging when it comes to complex and unfamiliar ecosystems and the threatened ecosystem services they provide. Making people aware of the characteristics of the ecosystem services being valued is determinant for the assessment process. We investigated the impact of familiarity and academic information supply on people's preferences for twenty selected ecosystem services of French Mediterranean coastal lagoons. The results show that regardless of familiarity and information supply, there is a strong consensus about the highest importance of regulation and maintenance ecosystem services as well as environmental education and research opportunity ecosystem services. By contrast, nine of the cultural ecosystem services, together with two provisioning ecosystem services showed heterogeneous preferences among the different citizen groups. Using a combination of descriptive and inferential statistics these eleven ecosystem services split up into three clusters characterized as (i) contemplative leisure, (ii) heritage, and (iii) consumptive activities. Familiarity and academic information supply had a strong impact on the preferences for these three clusters of ecosystem services.
Land is back. The increase in wealth in the second half of 20th century arose from housing and land. It should be taxed. We introduce land and housing structures in Judd’s standard setup: first best optimal taxation is achieved with a property tax on land and requires no tax on capital. With positive taxes on housing rents, a first best is still possible but with subsidies to rental housing investments, and either with differential land tax rates or with a tax on imputed rents. It can be taxed. Even absent land taxes, one can tax it indirectly and reach a Ramsey-second best still with no tax on capital and positive housing rent taxes in the steady-state. This result extends to the dynamics under restrictions on parameters.
We study price personalization in a two period duopoly with vertically differentiated products. In the second period, a firm not only knows the purchase history of all customers, as in standard Behavior Based Price Discrimination models, but it also collects detailed information on its old customers, using it to engage in price personalization. The analysis reveals that there exists a natural market for each firm, defined as the set of customers that cannot be poached by the rival in the second period. The equilibrium is unique, except when firms are ex-ante almost identical. In equilibrium, only the firm with the largest natural market poaches customers from the rival. This firm has highest profits but not necessarily the largest market share. Aggregate profits are lower than under uniform pricing. All consumers gain, total welfare is higher herein than under uniform pricing if firms’ natural markets are sufficiently asymmetric. The low quality firm chooses the minimal quality level and a quality differential arises, though the exact choice for the high quality depends upon the cost specification.
Background:
Asthma affects over 330 million people worldwide. Timing of an asthma event is extremely important and lack of identification of asthma increases the risk of death. A major challenge for health systems is the length of time between symptom onset and care seeking, which could result in delayed treatment initiation and worsening of symptoms.
Objective:
This study evaluates the utility of the internet search query data for the identification of the onset of asthma symptoms.
Methods:
Pearson correlation coefficients between the time series of hospital admissions and Google searches were computed at lag times from 4 weeks before hospital admission to 4 weeks after hospital admission. An autoregressive integrated moving average (ARIMAX) model with an autoregressive process at lags of 1 and 2 and Google searches at weeks –1 and –2 as exogenous variables were conducted to validate our correlation results.
Results:
Google search volume for asthma had the highest correlation at 2 weeks before hospital admission. The ARIMAX model using an autoregressive process showed that the relative searches from Google about asthma were significant at lags 1 (P<.001) and 2 (P=.04).
Conclusions:
Our findings demonstrate that internet search queries may provide a real-time signal for asthma events and may be useful to measure the timing of symptom onset.
