Économie comportementale : retrouve-t-on un effet d’ancrage dans la LNH ?

De par leur nature scientifique, les sciences économiques visent, entre autre, à observer, qualifier, ainsi que quantifier des phénomènes économiques afin de pouvoir en dégager diverses prévisions. Ce mémoire se penche sur ces prévisions et, plus particulièrement, sur les facteurs pouvant biaiser les prévisionnistes au niveau comportemental en référant à l’effet d’ancrage, un biais propre à l’économie comportementale – une sous-discipline des sciences économiques. Il sera donc question de comprendre, par une analyse selon la discipline que représente l’économie comportementale, ce qui peut les affecter, avec un accent mis sur l’effet d’ancrage plus précisément. L’idée générale de ce dernier est qu’un agent peut être biaisé inconsciemment par la simple connaissance d’une valeur précédente lorsqu’il est demandé de faire une estimation ultérieure. De cette façon, une analyse des salaires des joueurs de la Ligne Nationale de Hockey (NHL) selon leurs performances passées et leurs caractéristiques personnelles, de 2007 à 2016, a été réalisée dans ce travail afin d’en dégager de possibles effets d’ancrage. Il est alors possible de constater que les directeurs généraux des équipes de la ligue agissent généralement de façon sensible et rationnelle lorsque vient le temps d’octroyer des contrats à des joueurs mais, néanmoins, une anomalie persiste lorsqu’on porte attention au rang auquel un joueur a été repêché. Dans un tel contexte, il semble pertinent de se référer à l’économie comportementale afin d’expliquer pourquoi le rang au repêchage reste une variable significative huit ans après l’entrée d’un joueur dans la NHL et qu’elle se comporte à l’inverse de ce que prévoit la théorie à ce sujet.

From a traditionally open access fishery to modern restrictions: Portuguese anglers' perceptions about newly implemented recreational fishing regulations

Saltwater recreational fishing (SRF) in Portugal was for a long time an open-access activity, without restrictions of any kind. Restrictions to control the recreational harvest were first implemented in 2006 and were highly criticized by the angler community, for being highly restrictive and lacking scientific support. The present study aimed to obtain socio-economic data on the recreational shore anglers and gauge their perceptions about recreational fishing regulations and the newly implemented restrictions in Portugal. Roving creel surveys were conducted along the south and south-west coasts of Portugal, during pre and post regulation periods (2006-2007). A total of 1298 valid face-to-face interviews were conducted. Logit models were fitted to identify which characteristics influence anglers' perceptions about recreational fishing regulations. The majority of the interviewed anglers was aware and agreed with the existence of recreational fishing regulations. However, most were against the recreational fishing regulations currently in place. The logit models estimates revealed that Portuguese anglers with a higher level of formal education and income are more likely to agree with the existence of recreational fishing regulations. In contrast, anglers who perceive that more limitations and a better enforcement of commercial fishing would improve fishing in the area are less likely to agree with the existence of SRF regulations. The findings from this study will contribute to inform decision-makers about anglers' potential behaviour towards the new and future regulations. Although the existence of fishing regulations is a good starting point for effective management, the lack of acceptance and detailed knowledge of the regulations in place by fishers may result in lack of compliance, and ultimately hinder the success of recreational fishing regulations in Portugal. (C) 2013 Elsevier Ltd. All rights reserved.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.

Health literacy [Document électronique] : an economic perspective and data for Switzerland. Part 2, A review of health literacy measures and a cost assessment of limited health literacy

Summary : 1. Measuring health literacy in Switzerland: a review of six surveys: 1.1 Comparison of questionnaires - 1.2 Measures of health literacy in Switzerland - 1.3 Discussion of Swiss data on HL - 1.4 Description of the six surveys: 1.4.1 Current health trends and health literacy in the Swiss population (gfs-UNIVOX), 1.4.2 Nutrition, physical exercise and body weight : opinions and perceptions of the Swiss population (USI), 1.4.3 Health Literacy in Switzerland (ISPMZ), 1.4.4 Swiss Health Survey (SHS), 1.4.5 Survey of Health, Ageing and Retirement in Europe (SHARE), 1.4.6 Adult literacy and life skills survey (ALL). - 2 . Economic costs of low health literacy in Switzerland: a rough calculation. Appendix: Screenshots cost model

Determinants of economic growth: Will data tell?

Many factors inhibiting and facilitating economic growth havebeen suggested. Can agnostics rely on international incomedata to tell them which matter? We find that agnostic priorslead to conclusions that are sensitive to differences acrossavailable income estimates. For example, the PWT 6.2 revisionof the 1960-96 income estimates in the PWT 6.1 leads tosubstantial changes regarding the role of government,international trade, demography, and geography. We concludethat margins of error in international income estimates appeartoo large for agnostic growth empirics.

Data protection at the cost of economic growth? ECRI Commentary No. 11, 16 November 2012

The Data Protection Regulation proposed by the European Commission contains important elements to facilitate and secure personal data flows within the Single Market. A harmonised level of protection of individual data is an important objective and all stakeholders have generally welcomed this basic principle. However, when putting the regulation proposal in the complex context in which it is to be implemented, some important issues are revealed. The proposal dictates how data is to be used, regardless of the operational context. It is generally thought to have been influenced by concerns over social networking. This approach implies protection of data rather than protection of privacy and can hardly lead to more flexible instruments for global data flows.

Natural disasters, economic growth and sustainable development in China: an empirical study using provincial panel data

Using a newly developed integrated indicator system with entropy weighting, we analyzed the panel data of 577 recorded disasters in 30 provinces of China from 1985–2011 to identify their links with the subsequent economic growth. Meteorological disasters promote economic growth through human capital instead of physical capital. Geological disasters did not trigger local economic growth from 1999–2011. Generally, natural disasters overall had no significant impact on economic growth from 1985–1998. Thus, human capital reinvestment should be the aim in managing recoveries, and it should be used to regenerate the local economy based on long-term sustainable development.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the (feasible) bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular, it is asymptotically equivalent to the conditional expectation, i.e., has an optimal limiting mean-squared error. We also develop a zeromean test for the average bias and discuss the forecast-combination puzzle in small and large samples. Monte-Carlo simulations are conducted to evaluate the performance of the feasible bias-corrected average forecast in finite samples. An empirical exercise based upon data from a well known survey is also presented. Overall, theoretical and empirical results show promise for the feasible bias-corrected average forecast.

A panel data approach to economic forecasting: the bias-corrected average forecast

In this paper, we propose a novel approach to econometric forecasting of stationary and ergodic time series within a panel-data framework. Our key element is to employ the bias-corrected average forecast. Using panel-data sequential asymptotics we show that it is potentially superior to other techniques in several contexts. In particular it delivers a zero-limiting mean-squared error if the number of forecasts and the number of post-sample time periods is sufficiently large. We also develop a zero-mean test for the average bias. Monte-Carlo simulations are conducted to evaluate the performance of this new technique in finite samples. An empirical exercise, based upon data from well known surveys is also presented. Overall, these results show promise for the bias-corrected average forecast.