Suchergebnisse

The first chapter documents a key parameter to understand the housing market: the elasticity of housing supply in French urban areas. We show that this elasticity can be apprehended in two ways by considering the intensive and extensive supply of housing. Thanks to a large amount of new data collected and an original estimation strategy, this first chapter estimates and decomposes the two elasticities. The second chapter is devoted to the possibilities offered by Big Data for studying the French rental housing market. By using online data from December 2015 to June 2017 and comparing this data with traditional administrative data, we find that the Internet provides data to accurately track local real estate markets.The third chapter deals with the discrimination of women in politics. It exploits a natural experiment, the French departmental elections of 2015 during which, for the first time in the history of the French elections, the candidates had to present themselves in pairs of candidates necessarily mixed. Using the fact that the order of appearance of the candidates on a ballot was determined by the alphabetical order and showing that this rule does not seem to have been used strategically by the parties, we show, first, that the position of women on the ballot is random, and second, that the right-hand pairs for whom the name of the female candidate is in first position on the ballot receive on average 1.5 percentage points less than votes. ; Le premier chapitre documente un paramètre clé pour comprendre le marché du logement : l'élasticité de l'offre de logements des aires urbaines françaises. Nous montrons que cette élasticité peut être appréhendée de deux manières en considérant l'offre intensive et extensive de logements. Grâce à une quantité importante de nouvelles données collectées et une stratégie d'estimation originale, ce premier chapitre estime et décompose les deux élasticités. Le deuxième chapitre est consacré aux possibilités offertes par le Big Data pour étudier le marché de logement locatif français. En exploitant des données en ligne de décembre 2015 à juin 2017 et comparant ces données aux données administratives classiques, nous montons qu'internet fournit des données permettant de suivre avec exactitude les marchés immobiliers locaux. Le troisième chapitre porte sur la discrimination des femmes en politique. Il exploite une expérience naturelle, les élections départementales françaises de 2015 au cours desquelles, pour la première fois dans l'histoire des élections françaises, les candidats ont dû se présenter par paires de candidats obligatoirement mixtes. En utilisant le fait que l'ordre d'apparition des candidats sur un bulletin de vote était déterminé par l'ordre alphabétique et en montrant que cette règle ne semble pas avoir été utilisée de façon stratégique par les partis, nous montrons d'une part que la position des femmes sur le bulletin de vote est aléatoire, et d'autre part, que les binômes de droite pour qui le nom du candidat féminin est en première position sur le bulletin reçoivent en moyenne 1,5 point de pourcentage de moins de votes

The first chapter documents a key parameter to understand the housing market: the elasticity of housing supply in French urban areas. We show that this elasticity can be apprehended in two ways by considering the intensive and extensive supply of housing. Thanks to a large amount of new data collected and an original estimation strategy, this first chapter estimates and decomposes the two elasticities. The second chapter is devoted to the possibilities offered by Big Data for studying the French rental housing market. By using online data from December 2015 to June 2017 and comparing this data with traditional administrative data, we find that the Internet provides data to accurately track local real estate markets.The third chapter deals with the discrimination of women in politics. It exploits a natural experiment, the French departmental elections of 2015 during which, for the first time in the history of the French elections, the candidates had to present themselves in pairs of candidates necessarily mixed. Using the fact that the order of appearance of the candidates on a ballot was determined by the alphabetical order and showing that this rule does not seem to have been used strategically by the parties, we show, first, that the position of women on the ballot is random, and second, that the right-hand pairs for whom the name of the female candidate is in first position on the ballot receive on average 1.5 percentage points less than votes. ; Le premier chapitre documente un paramètre clé pour comprendre le marché du logement : l'élasticité de l'offre de logements des aires urbaines françaises. Nous montrons que cette élasticité peut être appréhendée de deux manières en considérant l'offre intensive et extensive de logements. Grâce à une quantité importante de nouvelles données collectées et une stratégie d'estimation originale, ce premier chapitre estime et décompose les deux élasticités. Le deuxième chapitre est consacré aux possibilités offertes par le Big Data pour étudier le marché de logement ...

AbstractWomen are underrepresented in politics. In this paper, we test one of the potential explanations for this situation: gender biases from voters. We use a natural experiment during French local elections in 2015: for the first time in this country, candidates had to run in pairs, which had to be gender-balanced. We argue that this reform confused some voters, who might have assumed that the first name on the ballot represented the 'main' candidate. Since the order of the candidates on the ballot was determined by their alphabetical order, the order of appearance of male and female candidates was as-good-as-random and this setting allows us to isolate gender biases from selection effects. Our main result is that there exists a negative gender bias affecting right-wing candidates, whose vote shares were lower by 1.5 percentage points when the female candidate appeared first on the ballot. The missing votes prevented some pairs of candidates from proceeding to the second round of voting. Using data on newspaper circulation and additional institutional features of the election—namely the fact that candidates can (but do not have to) report additional information about themselves on the ballot—we show that higher levels of information decrease discrimination. We argue that the discrimination we identify is, therefore, likely to be statistical.