Les objectifs de résultats assignés, par le pouvoir politique, à l’équipe de France pour les Jeux olympiques (JO) de Paris 2024 sont comparés aux prévisions obtenues avec un modèle macroéconométrique déjà éprouvé puisqu’il a prévu 95 % des résultats des JO de Tokyo 2021. Ses variables sont pour chaque pays : population, PIB par habitant, nombre d’athlètes alignés et nombre de médailles remportées aux JO précédents net des disqualifications pour dopage ainsi que des indicatrices pour le pays hôte, le régime politique, la spécialisation sportive, le fait d’être le pays hôte des prochains Jeux, et le fait d’avoir été l’hôte des JO précédents. Dans toutes les variantes du modèle, estimées en Tobit et en Hurdle, les quatre nations récoltant le plus de médailles sont dans l’ordre : États-Unis, Chine, athlètes russes, Grande-Bretagne. Le résultat le plus probable de la France est 47-48 médailles dans des intervalles de confiance allant, aux extrêmes, de 43 à 60 médailles. L’équipe de France se classe 5 e ou 6 e au nombre de médailles selon les variantes. Ce qui nous écarte de l’objectif politique initial de 70 à 80 médailles. Selon le modèle de prédiction, l’objectif initial en nombre de médailles est hors d’atteinte et statistiquement improbable mais l’objectif révisé de la 5 e place est atteignable.

ObjectiveThis article aims at explaining national medal totals at the 1992–2016 Summer Olympic Games (n = 1,289 observations) and forecasting them in 2016 (based on 1992–2012 data) and 2020 with a set of variables similar to previous studies, as well as a regional (subcontinents) variable not tested previously in the literature in English.MethodEconometric testing not only resorts to a Tobit model as usual but also to a Hurdle model.ResultsMost variables have a significant impact on national team medal totals; it appears to be negative for most regions other than North America except Western Europe and Oceania (not significant). Then, two models (Tobit and Hurdle) are implemented to forecast national medal totals at the 2016 and 2020 Summer Olympics.ConclusionBoth models are complementary for the 2016 forecast. The 2020 forecast is consistent with Olympic Medals Predictions, although some striking differences are found.

Multinational companies (MNCs) based in 26 post-communist transition economies (PTEs) emerged during the 1990s. Their outward foreign direct investment (OFDI) boomed dramatically from 2000 to 2007 in these countries, and then muddled through the financial crisis and great recession at difference paces on different paths. This difference is revealed in a sample of 15 PTEs for which data are available from 2000 to 2015. Most of these economies appear to be on the brink of moving from the second to the third stage of Dunning's investment development path. The geographical distribution of their OFDI favors host countries located in other PTEs, developed market economies, and tax havens while their industrial structure is more concentrated on services rather than on manufacturing and the primary sector. PTE-based MNCs primarily adopt a strategy of market-seeking OFDI. Econometric testing shows that push factors are major determinants of OFDI. The results demonstrate that OFDI is determined by the home country's level of economic development, the size of its home market, and its rate of growth as well as technological variables: OFDI decreases with an increase in the number of scientists in the home economy and with an increase in the share of high-tech products in overall exports, exhibiting a negative technological gap. A lagged relationship between OFDI and previous inward FDI suggests that Mathews’ linkage-leverage-learning theory is relevant in the case of PTEs.

A model used for successfully predicting Beijing Olympics’ medal wins is adapted to check whether economic variables could be good predictors of soccer World Cup outcomes. Some ‘footballistic’ variables must be added with regard to predicting the outcome of a single sport discipline contest. The model does not perform as well with the soccer World Cup as with the Olympics. This is owing to surprising sporting outcomes, a notion not previously analysed. The chapter elaborates on such notion and suggests a simple metrics, then concludes that economic predictions of sporting performances must be treated with caution.

This paper uses forecasting techniques to predict outcomes at the 2014 Winter Olympics using economic variables.

This article addresses the following research question: would a model based on population and GDP per capita as determinants perform as well in explaining the football World Cup outcomes as it performed with medal wins at Olympic Games? What has been observed with the prediction of the football World Cup semi-finalists paves the way for a new avenue for research which would consist in defining and explaining more carefully so- called surprising sporting outcomes.

To the best of our knowledge nobody has attempted to elaborate on an economic model for predicting medal wins at the Winter Olympics so far. This contrasts with Summer Olympics for which about thirty studies have estimated economic determinants of sporting performances (among which Andreff, 2001; Ball, 1972; Clarke, 2000; Grimes et al., 1974; Jiang & Xu, 2005; Levine, 1974; Nevill et al., 2002; Novikov & Maximenko, 1972; Pfau, 2006). Some publications have even provided predictions about medal wins at the next Olympic Games (Bernard, 2008; Bernard & Busse, 2004; Hawksworth, 2008; Johnson & Ali, 2004; Johnson and Ali, 2008; Maennig & Wellebrock, 2008; Wang & Jiang, 2008). Our own model has exactly predicted 70% of medal wins at the 2008 Beijing Olympics and correctly (with a small error margin) 88% of the sporting outcomes at these Games (Andreff et al., 2008 & Andreff, 2010). In this paper, we would take stake of the good predictions achieved with our model for Summer Olympics to adapt it in view of forecasting the distribution of medal wins per nation at the 2014 Sochi Winter Games.

The analysis of international trade in sports goods is still in its infancy. In order to alleviate the sports economics ignorance in this area, an entirely new dataset is built up by extracting Comtrade data at the most disaggregated level (6 digits). The dataset covers 41 countries, 36 different sports goods, and 94-96% of global sports goods trade (1994-2004). The country sample is divided into five regional areas: North American Free Trade Area (NAFTA), EU + Switzerland, Eastern Europe, Asia and other emerging countries. A detailed snapshot of global trade in sports goods and its distribution by major areas, countries and products provides first empirical evidence about how much industrialisation in emerging countries and de-industrialisation in developed market economies have affected international specialisation, and indirectly tests multinational companies outsourcing and production relocation strategies in low unit cost countries in the sports goods industry.Then, studying export/import ratios and country's position in the global market, it appears that major trading areas are Asia, Europe and NAFTA. Major exporters are China, Hong Kong, the USA and France, and major importers are the USA, Japan, Germany, France, the UK and Italy. The biggest market shares are in sportswear, anoraks and gymnastic equipment trade. Asia, Eastern Europe and emerging countries have an excess balance in sports goods trade, whereas NAFTA and Europe are in deficit. Three indexes assess a country's comparative advantages and disadvantages and competitiveness, and describe international specialisation. NAFTA and Europe are specialised in equipment-intensive sports goods, while Asia, Eastern Europe and emerging countries are specialised in trite sports goods and some less equipment-intensive sports goods. NAFTA is not competitive in any sport good, Europe is competitive in skis, emerging countries and Eastern Europe in sportswear and anoraks, and Asia in sportswear, anoraks, rackets, balls, skates and gymnastic equipment. Such an international specialisation pattern fits with both assumptions of industrialisation/de-industrialisation and firms outsourcing strategies. A principal component analysis with hierarchical ascendant classification groups trite sports goods as opposed to intensive-equipment sports goods in global trade and shows that production relocation influences international trade specialisation.Major policy implications are that developed economies and multinational companies should continue investing in R&D in order to keep their comparative advantages in equipment-intensive sports goods, while Asian and emerging countries should more tightly supervise working conditions and child labour in their subcontracting producers that work for foreign multinational companies.

The analysis of economic determinants of Olympic performance was for a long time a macroeconomics of the cold war in which the number of medals won by a nation was explained by its endowment with economic and human resources, then its political regime and a host country effect. We adopt a post-cold war view of the Games which provides an additional explanation closer to the Olympic ideals since it takes into account individual athlete performances, culture, and sporting disciplines. Various econometric estimations that translate this renewed view are achieved under the constraint of limited available data, over 1976-2004. In a first specification inspired from the macroeconomic approach by Bernard and Busse (2004), with a more detailed country classification, GDP per capita, population, the political regime and the host country effect determine the number of medals won. A second specification adds a variable that captures cultural differences across various regions in the world, which improves the previous estimation. A third estimation relies on a new individual data base, introduces an economic classification of sports as a dependent variables and enables estimating, for an athlete from a given country, his/her chances of participating to an Olympic final and of winning a medal. Finally, regarding the prediction of the would-be medal wins in Peking 2008, an inertial variable is introduced in the macroeconomic model in order to capture an ‘Olympic worship’ in those nations which are used to win a number of medals, a variable that differentiates them from other participating nations. Another prevision is based on individual data without any inertia.

The analysis of economic determinants of Olympic performance was for a long time a macroeconomics of the cold war in which the number of medals won by a nation was explained by its endowment with economic and human resources, then its political regime and a host country effect. We adopt a post-cold war view of the Games which provides an additional explanation closer to the Olympic ideals since it takes into account individual athlete performances, culture, and sporting disciplines. Various econometric estimations that translate this renewed view are achieved under the constraint of limited available data, over 1976-2004. In a first specification inspired from the macroeconomic approach by Bernard and Busse (2004), with a more detailed country classification, GDP per capita, population, the political regime and the host country effect determine the number of medals won. A second specification adds a variable that captures cultural differences across various regions in the world, which improves the previous estimation. A third estimation relies on a new individual data base, introduces an economic classification of sports as a dependent variable and enables estimating, for an athlete from a given country his/her chances of participating to an Olympic final and of winning a medal. Finally, regarding the prediction of the would-be medal wins in Peking 2008, an inertial variable is introduced in the macroeconomic model in order to capture an 'Olympic worship' in those nations which are used to win a number of medals, a variable that differentiates them from other participating nations. Another prevision is based on individual data without any inertia.

The analysis of economic determinants of Olympic performance was for a long time a macroeconomics of the cold war in which the number of medals won by a nation was explained by its endowment with economic and human resources, then its political regime and a host country effect. We adopt a post-cold war view of the Games which provides an additional explanation closer to the Olympic ideals since it takes into account individual athlete performances, culture, and sporting disciplines. Various econometric estimations that translate this renewed view are achieved under the constraint of limited available data, over 1976-2004. In a first specification inspired from the macroeconomic approach by Bernard and Busse (2004), with a more detailed country classification, GDP per capita, population, the political regime and the host country effect determine the number of medals won. A second specification adds a variable that captures cultural differences across various regions in the world, which improves the previous estimation. A third estimation relies on a new individual data base, introduces an economi c classification of sports as a dependent variables and enables estimating, for an athlete from a given country, his/her chances of participating to an Olympic final and of winning a medal. Finally, regarding the prediction of the would-be medal wins in Peking 2008, an inertial variable is introduced in the macroeconomic model in order to capture an 'Olympic worship' in those nations which are used to win a number of medals, a variable that differentiates them from other participating nations. Another prevision is based on individual data without any inertia.

The driving training sector in France is subjected to an increasing interest from the French government. The regulator notably wonders about the performance of the training given by services companies. He plans to use more widely the driving test success rate indicator by enterprise to guide the public action. This article explores how the driving examination rate depends on a set of socio-economic variables. With a factor analysis and an analysis of variance and a CART method, we highlight that the companies' success rate depends more on the characteristics of the regional demand than to their inner characteristics in terms of provision and supply. We can thus deduce that the control through this indicator proves to be ineffective and that it is necessary to appeal to a more multidimensional definition of the performance of the companies of this sector.

L’analyse des déterminants de la performance olympique fut longtemps une macroéconomie de guerre froide où le nombre de médailles gagnées par une nation est expliqué par ses ressources économiques et humaines, puis par son régime politique et le fait d’être le pays hôte des Jeux. On propose une vision des Jeux d’après guerre froide, recherchant un complément d’explication plus proche de l’idéal olympique qui tiendrait compte des performances individuelles, de la culture et des disciplines sportives. Les estimations économétriques correspondant à cette vision renouvelée sont limitées par la faible disponibilité des données, portant sur la période 1976-2004. Dans une première spécification inspirée de l’article de référence de la macroéconomie des médailles par nations, avec une classification plus précise des pays, le PIB par tête, la population, le régime politique et l’effet pays hôte déterminent le nombre de médailles gagnées. Une deuxième spécification ajoute une variable capturant des différences culturelles par régions du monde qui améliore l’estimation précédente. Une troisième estimation utilise une nouvelle base de données individuelles et introduit une classification économique des disciplines sportives parmi les variables explicatives. Elle permet d’estimer les chances, pour un athlète d’une nation donnée, d’atteindre une finale olympique ainsi que ses chances de gagner une médaille. Pour la prévision des médailles gagnées à Pékin 2008, on introduit une variable inertielle dans le modèle macroéconomique, captant le culte de l’olympisme des nations habituées à gagner des médailles et les différenciant des autres nations participantes. On prévoit aussi ces gains à partir de données individuelles, sans inertie.

The analysis of economic determinants of Olympic performance was for a long time a macroeconomics of the cold war in which the number of medals won by a nation was explained by its endowment with economic and human resources, then its political regime and a host country effect. We adopt a post-cold war view of the Games which provides an additional explanation closer to the Olympic ideals since it takes into account individual athlete performances, culture, and sporting disciplines. Various econometric estimations that translate this renewed view are achieved under the constraint of limited available data, over 1976-2004. In a first specification inspired from the macroeconomic approach by Bernard and Busse (2004), with a more detailed country classification, GDP per capita, population, the political regime and the host country effect determine the number of medals won. A second specification adds a variable that captures cultural differences across various regions in the world, which improves the previous estimation. A third estimation relies on a new individual data base, introduces an economic classification of sports as a dependent variable and enables estimating, for an athlete from a given country, his/her chances of participating to an Olympic final and of winning a medal. Finally, regarding the prediction of the would-be medal wins in Peking 2008, an inertial variable is introduced in the macroeconomic model in order to capture an 'Olympic worship' in those nations which are used to win a number of medals, a variable that differentiates them from other participating nations. Another prevision is based on individual data without any inertia.

La competencia de los paises de Europa central y oriental (PECO) frente a los 15 paises de la Union Europea (UE-15) para atraer las inversiones extranjeras directas (IED) se ha acentuado entre 1993 y 2002, con independencia del criterio de evaluacion adoptado. Los PECO han triplicado su cuota del mercado europeo de la IED entrante, reduciendo asi la cuota de mercado de los antiguos miembros de la UE.