Reclaiming public space: The economic, environmental, and social impacts of Bogotá's transformation

Over the course of a few short years, the city of Bogotá (Colombia) has dramatically transformed the quality of its public space. This session presents primary and secondary data documenting the economic, environmental, and social benefits of improved public space and urban mobility. Bogotá has benefited from a series of political leaders with a highly progressive view on the importance of urban space. This high degree of political will contributed to dramatic changes in several areas, including: 1. Reclamation of public space; 2. Improvement of public transport; 3. Promotion of non-motorised transport; and, 4. Implementation of auto restriction measures. The near simultaneous application of these policies has produced quantifiable benefits to the quality of life of city residents. The research shows that property values in areas with urban upgrades have appreciated considerably when compared to a control group of similar properties. Additionally, the research shows employment benefits from the city’s Sunday “ciclovía” (closing of streets to motorised vehicles) is significantly greater than week-day auto-related employment along the same corridors. Air quality monitoring shows emission reductions by as much as 40 per cent for some pollutants. Social indicators related to accidents, crime levels, and equity are also quite positive. Traffic deaths have been reduced from over 1,300 in 1995 to less than 700 in 2002. Bogotá’s transformation has attracted visits by city officials from over 50 nations. The replicability of Bogotá’s successes will depend upon local circumstances, and especially upon levels of local political will. Further documentation of the economic, environmental, and social benefits stemming from Bogotá’s efforts will help instil the confidence of city officials to move ahead with urban transformations of their own

A unified framework for monetary theory and policy analysis

Search-theoretic models of monetary exchange are based on explicit descriptions of the frictions that make money essential. However, tractable versions of these models typically need strong assumptions that make them ill-suited for studying monetary policy. We propose a framework based on explicit micro foundations within which macro policy can be analyzed. The model is both analytically tractable and amenable to quantitative analysis. We demonstrate this by using it to estimate the welfare cost of inflation. We find much higher costs than the previous literature: our model predicts that going from 10% to 0% inflation can be worth between 3% and 5% of consumption.Monetary policy ; Money

Dynamics, cycles and sunspot equilibria in "genuinely dynamic, fundamentally disaggregative" models of money

This paper pursues a line of Cass and Shell, who advocate monetary models that are "genuinely dynamic and fundamentally disaggregative" and that incorporate "diversity among households and variety among commodities." Recent search-theoretic models fit this description. The authors show that, like overlapping generations models, search models generate interesting dynamic equilibria, including cycles, chaos, and sunspot equilibria. This helps explain how alternative models are related and lends support to the notion that endogenous dynamics and uncertainty matter, perhaps especially in monetary economies. Th authors also suggest that such equilibria in search models may be more empirically relevant than in some other models.Monetary theory

Crime, Inequality, and Unemployment, Second Version

There is much discussion of the relationships between crime, inequality, and unemployment. We construct a model where all three are endogenous. We find that introducing crime into otherwise standard models of labor markets has several interesting implications. For example, it can lead to wage inequality among homogeneous workers. Also, it can generate multiple equilibria in natural but previously unexplored ways; hence two identical neighborhoods can end up with different levels of crime, inequality, and unemployment. We discuss the effects of anti-crime policies like changing jail sentences, as well as more traditional labor market policies like changing unemployment insurance.Crime, Inequality, Unemployment, Search

An On-the-Job Search Model of Crime, Inequality, and Unemployment

We extend simple search-theoretic models of crime, unemployment and inequality to incorporate on-the-job search. This is valuable because, although the simple models can be used to illustrate some important points concerning the economics of crime, on-the-job search models are more relevant empirically as well as more interesting in terms of the types of equilibria they generate. We characterize crime decisions, unemployment, and the equilibrium wage distribution. We use quantitative methods to illustrate key results, including a multiplicity of equilibria with different unemployment and crime rates, and to discuss the effects of changes in labor market and anti-crime policies.Crime, Inequality, Unemployment, Search, Turnov

Alimentación, estado nutricional y micronutrientes en niños celíacos

La Enfermedad Celíaca es una patología multisistémica, que requiere de la supresión de por vida de los alimentos que contengan TACC para mejorar los síntomas. Dicha supresión alimentaria, podría alejar de los patrones de requerimientos nutricionales, alterando el adecuado crecimiento y desarrollo de los niños.Área: Ciencias Biológicas, Ambiente y Salu

A Remote Sensing-Based Tool for Assessing Rainfall-Driven Hazards

RainyDay is a Python-based platform that couples rainfall remote sensing data with Stochastic Storm Transposition (SST) for modeling rainfall-driven hazards such as floods and landslides. SST effectively lengthens the extreme rainfall record through temporal resampling and spatial transposition of observed storms from the surrounding region to create many extreme rainfall scenarios. Intensity-Duration-Frequency (IDF) curves are often used for hazard modeling but require long records to describe the distribution of rainfall depth and duration and do not provide information regarding rainfall space-time structure, limiting their usefulness to small scales. In contrast, Rainy Day can be used for many hazard applications with 1-2 decades of data, and output rainfall scenarios incorporate detailed space-time structure from remote sensing. Thanks to global satellite coverage, Rainy Day can be used in inaccessible areas and developing countries lacking ground measurements, though results are impacted by remote sensing errors. Rainy Day can be useful for hazard modeling under nonstationary conditions