The existence of homeowner preferences-specifically homeowner preferences for neighborsis fundamental to economic models of sorting. This paper investigates whether or not the terrorist attacks of September 11, 2001 (9/11) impacted local preferences for Arab neighbors. We test for changes in preferences using a differences-indifferences approach in a hedonic pricing model. Relative to sales before 9/11, we find properties within 0.1 miles of an Arab homeowner sold at a 1.4% discount in the 180 days after 9/11. The results are robust to a number of specifications including time horizon, event date, distance, time, alternative ethnic groups, and the presence of nearby mosques. Previous research has shown price effects at neighborhood levels but has not identified effects at the micro or individual property level, and for good reason: most transaction level data sets do not include ethnic identifiers. Applying methods from the machine learning and biostatistics literature, we develop a binomial classifier using a supervised learning algorithm and identify Arab homeowners based on the name of the buyer. We train the binomial classifier using names from Summer Olympic Rosters for 221 countries during the years 1948-2012. We demonstrate the flexibility of our methodology and perform an interesting counterfactual by identifying Hispanic and Asian homeowners in the data; unlike the statistically significant results for Arab homeowners, we find no meaningful results for Hispanic and Asian homeowners following 9/11.
We evaluate the impact of a half century of nontransportation Appalachian Regional Commission (ARC) investments on its constituent counties using quasi-experimental methods. We apply a set of propensity score methods and select the most appropriate matching algorithm for use in identifying the effects of policy implementation. The results of the analyses indicate that counties that received ARC funding grew faster than the control counties. The long-run per capita income growth rate in ARC investment counties was an average of 5.5 percent higher than in the control counties. Employment also grew significantly faster in these ARC counties than in the control counties for most of the study period. The average difference in the long-run employment growth rates between the counties that received ARC investments and those counties that did not was approximately 4.2 percent.
El presente artículo examina cómo la comunicación gubernamental en Twitter®, en el contexto de crisis por Covid-19, ha producido diferentes sentimientos en la ciudadanía. Este tipo de interacción tiene efectos en la percepción sobre los gobernantes y el acatamiento de las restricciones. Utilizando el paquete tidytext de R se analizaron un total de 28.344 trinos relacionados con el Covid entre enero 2020 y marzo 2021 de diez usuarios en Twitter® (Alcaldes y Alcaldías de Barranquilla, Bogotá, Cali y Medellín, Presidente y Presidencia). El artículo busca generar un aporte al campo de la comunicación gubernamental durante la crisis del Covid-19 y los sentimientos que la interacción entre ciudadanía y gobernantes suscita para el período seleccionado.
The existence of homeowner preferences-specifically homeowner preferences for neighborsis fundamental to economic models of sorting. This paper investigates whether or not the terrorist attacks of September 11, 2001 (9/11) impacted local preferences for Arab neighbors. We test for changes in preferences using a differences-indifferences approach in a hedonic pricing model. Relative to sales before 9/11, we find properties within 0.1 miles of an Arab homeowner sold at a 1.4% discount in the 180 days after 9/11. The results are robust to a number of specifications including time horizon, event date, distance, time, alternative ethnic groups, and the presence of nearby mosques. Previous research has shown price effects at neighborhood levels but has not identified effects at the micro or individual property level, and for good reason: most transaction level data sets do not include ethnic identifiers. Applying methods from the machine learning and biostatistics literature, we develop a binomial classifier using a supervised learning algorithm and identify Arab homeowners based on the name of the buyer. We train the binomial classifier using names from Summer Olympic Rosters for 221 countries during the years 1948-2012. We demonstrate the flexibility of our methodology and perform an interesting counterfactual by identifying Hispanic and Asian homeowners in the data; unlike the statistically significant results for Arab homeowners, we find no meaningful results for Hispanic and Asian homeowners following 9/11.
En los últimos años, las redes sociales se han convertido en una herramienta fundamental en las campañas electorales. Específicamente, Twitter® se ha vuelto un canal de comunicación de campaña de vital importancia, ya que permite generar opinión, dar a conocer propuestas y posicionar candidaturas. El presente trabajo investiga cómo las y los once precandidatos con mayor intención de voto a la presidencia de Colombia llevaron a cabo la comunicación en Twitter® durante el Paro Nacional de abril de 2021. En aquella comunicación entre las y los precandidatos a la presidencia y las y los potenciales votantes, los sentimientos que se generan como respuesta a esa interacción tienen un impacto sobre las emociones. Con el paquete tidytext de R se analizaron un total de 18.093 tuits para las once cuentas verificadas de las y los precandidatos a la presidencia seleccionadas en Twitter® y, específicamente, los 2.700 tuits que están relacionados con temas de coyuntura general sobre el paro nacional. Esto permitió establecer la relevancia de aquellos temas en la comunicación de las y los precandidatos a la presidencia. Además, se pudo entender qué sentimientos y emociones generaron esas comunicaciones en las y los potenciales votantes.
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