Porter’s model and exports in Colombia: analysis of two profitable products

Tanto las industrias legales como ilegales tienen líderez y gerentes. Estas personas al mando deben hacer decisiones estrategicas con el fin de asegurar la rentabilidad de sus negocios. La manera en que ellos toman estas decisiones y las consecuencias de estas mismas son las preguntas que este documento pretende resolver. Haciendo una aplicación general del modelo de Michael Porter, este documento analiza y describe brevemente la configuración de ambos mercados (legal e illegal): es decir las maneras de hacer negocios, las tácticas utilizadas para negociar con los proveedores y compradores, las estrategias para resaltar los beneficios de sus productos frente a los sustitutos, y en general las acciones realizadas para competir, obtener un posicionamiento y porción en el mercado total. El objetivo de este documento no es exaltar las estrategias de los líderes en las industrias ilegales, sino resaltar aquello que los directivos en las industrias legales podrían hacer mejor para mejorar el nombre de los productos colombianos en el exterior.

Neighborhood characteristics and the spatial concentration of crime: Evidence for Bogotá

Using a unique neighborhood crime dataset for Bogotá in 2011, this study uses a spatial econometric approach and examines the role of socioeconomic and agglomeration variables in explaining the variance of crime. It uses two different types of crime, violent crime represented in homicides and property crime represented in residential burglaries. These two types of crime are then measured in non-standard crime statistics that are created as the area incidence for each crime in the neighborhood. The existence of crime hotspots in Bogotá has been shown in most of the literature, and using these non-standard crime statistics at this neighborhood level some hotspots arise again, thus validating the use of a spatial approach for these new crime statistics. The final specification includes socioeconomic, agglomeration, land-use and visual aspect variables that are then included in a SARAR model an estimated by the procedure devised by Kelejian and Prucha (2009). The resulting coefficients and marginal effects show the relevance of these crime hotspots which is similar with most previous studies. However, socioeconomic variables are significant and show the importance of age, and education. Agglomeration variables are significant and thus more densely populated areas are correlated with more crime. Interestingly, both types of crimes do not have the same significant covariates. Education and young male population have a different sign for homicide and residential burglaries. Inequality matters for homicides while higher real estate valuation matters for residential burglaries. Finally, density impacts positively both crimes.