Today is the 7th Anniversary of Urban Demographics. I hope the blog has been a valuable source of procrastination information for you as much as it has been for me. Thanks all the readers for the support \o/
Here are some stats that show a summary of the blog over the past year. Please, feel free to drop me a line with suggestions on how to improve the blog. If you have any criticisms, please direct them to this other blog here.
This picture comes from the Earth View, an extension for Chrome that displays some really beautiful satellite images from Google Earth every time you open a new tab.
ps. and some people tell me I procrastinate too much, yeah right.
It just came to my knowledge today that Raymond Florax passed away a couple of months ago (in memorian). Prof. Florax was very influential in the field of spatial econometrics. In one his latest papers, he co-authored with Delgado and proposed a difference-in-differences method for spatial data, controlling for spatial dependence. Here is the paper.
We consider treatment effect estimation via a difference-in-difference approach for spatial data with local spatial interaction such that the potential outcome of observed units depends on their own treatment as well as on the treatment status of proximate neighbors. We show that under standard assumptions (common trend and ignorability) a straightforward spatially explicit version of the benchmark difference-in-differences regression is capable of identifying both direct and indirect treatment effects. We demonstrate the finite sample performance of our spatial estimator via Monte Carlo simulations.
Early this year, a paper in PNAS using a computer model estimated that car sharing services like Uber and Lyft could reduce the number of taxi vehicles on roads by ~76% without significantly impacting travel time. As Joe Cortright has said, the authors are overly optimistic.
There is another study from last year that analyzed what actually happened to congestion levels when Uber entered the market in some US cities (abstract below). The results of this study are not really comparable to the the paper in PNAS, though. The methods are sound but I have the impression the authors pay too much attention to the statistical significance of the results and do not really discuss the magnitude of the effects of Uber entry on congestion. In any case, it's a good read.
Li, Z., Hong, Y., & Zhang, Z. (2016). Do Ride-Sharing Services Affect Traffic Congestion? An Empirical Study of Uber Entry. Available at SSRN: https://ssrn.com/abstract=2838043
Abstract:
Sharing economy platform, which leverages information technology (IT) to re-distribute unused or underutilized assets to people who are willing to pay for the services, has received tremendous attention in the last few years. Its creative business models have disrupted many traditional industries (e.g., transportation, hotel) by fundamentally changing the mechanism to match demand with supply in real time. In this research, we investigate how Uber, a peer-to-peer mobile ride-sharing platform, affects traffic congestion and environment (carbon emissions) in the urban areas of the United States. Leveraging a unique data set combining data from Uber and the Urban Mobility Report, we examine whether the entry of Uber car services affects traffic congestion using a difference-in-difference framework. Our findings provide empirical evidence that ride-sharing services such as Uber significantly decrease the traffic congestion after entering an urban area. We perform further analysis including the use of instrumental variables, alternative measures, a relative time model using more granular data to assess the robustness of the results. A few plausible underlining mechanisms are discussed to help explain our findings.
A good-looking video of the computer simulation model of the PNAS paper.
"... communities have proved more durable than borders. The counties with the highest concentration of Mexicans (as defined by ethnicity, rather than citizenship) overlap closely with the area that belonged to Mexico before the great gringo land-grab of 1848."
For the most part, Mexicans didn’t cross the US border. The border crossed them.
This week we have crossed the milestone of 5000 followers on Twitter. If anything, this means there are a lot of procrastinators out there. If you're not on Twitter but you would like to procrastinate receive automatic updates when there is a new blog post, there are two options:
We investigate the effect of lane kilometers of roads on vehicle-kilometers traveled (VKT) in US cities. VKT increases proportionately to roadway lane kilometers for interstate highways and probably slightly less rapidly for other types of roads. The sources for this extra VKT are increases in driving by current residents, increases in commercial traffic, and migration. Increasing lane kilometers for one type of road diverts little traffic from other types of road. We find no evidence that the provision of public transportation affects VKT. We conclude that increased provision of roads or public transit is unlikely to relieve congestion
It was a great experience and I learned a lot from the speakers but also from the process of co-organizing the event. I would like to share here four papers that I've read back then and that I would recommend to anyone who wants to start a research on smart cities. These are quite influential papers so some of you might have read them already. Also, feel free to suggest in the comments some other publications you think have strongly contributed to the literature.
