During the past few years, interest in high-frequency price data has grown steadily. Recent major economic events - including the food crisis and the energy price surge – have increased the need for timely high-frequency data, openly available to all users. Standard survey methods lag behind in meeting this demand, due to the high cost of collecting detailed sub-national data, the time delay usually associated with publishing the results, and the limitations to publishing detailed data. For example, although national consumer price indices (CPIs) are published on a monthly basis in most countries, national statistical offices do not release the underlying price data.
About a month ago two colleagues (Greg Kisunko and Steve Knack) posted a blog on “The many faces of corruption in the Russian Federation”. Their post, based on the elegant analysis of the 2011/2012 Russian BEEPS, underscores a point that many practitioners and researchers are now beginning to appreciate because of the availability of new, disaggregated data: corruption is not a homogenous phenomenon, but rather a term that encompasses many diverse phenomena that can have profoundly different impact on the growth and the development of a country. If we delve deeper into this disaggregated data, we observe that within the same country can coexist significantly different sub-national realities when it comes to the phenomenon we label “corruption”.
So I have blogged in the past about the potential and the use of gender disaggregated data, but my work this past week in Ghana made me realize (again and in new ways) how complicated it can get in practice.
The March 2011 issue of the Harvard Business Review has “a step-by-step guide to smart business experiments” by Eric Anderson and Duncan Simester, two marketing professors who have done a number of experiments with large firms in the U.S. Their bottom line message for businesses is:
We know that technology is not a panacea, that gadgetry and software are not always the right solutions for our transport problems. But how do we know – really know -- when technology is truly the wrong way to go – when, say, using an old-fashioned compass is genuinely better than a GPS?
Thanks to blogger Sebastiao Ferreira, writing for MIT’s CoLab Radio, I have learned about an intriguing phenomenon in Lima, where entrepreneur data collectors, named dateros, stand with clipboards along frequented informal microbus routes, collecting data on headways, passenger counts, and vehicle occupancy levels. The microbus drivers pay dateros about 10-cents per instant update, and they use the information to adjust their driving speed. For example, if there is a full bus only a minute ahead of the driver’s vehicle, the driver will slow down, hoping to collect more passengers further down the route. In informal transit systems, where drivers’ incomes are directly tied to passenger counts, paying dateros is a good investment (Photo from MIT CoLab Radio).
If you think about it, use of dateros could be more efficient than traditional schedule or GPS-based dispatch, because the headways are dynamically and continuously updated to optimize the number of passengers transported at any given time of day. According to Jeff Warren (a DIY cartography pioneer), the dateros have been praised as the “natural database, an ‘informal bank’ of transportation optimization data.”
Does this little-known practice call into question our traditional prescription for high-tech solutions to bus dispatch?
The other day, my colleague Roger Gorham, a transport economist working in Africa, shared with me an interesting story. He was in Lagos, meeting with stakeholders about setting up public-private partnerships for transport initiatives. One meeting revealed that, in an effort to improve service, a private entity had invested in new taxis for Lagos and in each had installed a GPS unit. This little revelation may not seem interesting, but it was very exciting to Roger, who also learned that the company has amassed more than 3 years of GPS tracking data for these taxis (which, incidentally, troll the city like perfect probes, nearly 24 hours a day, 7 days a week) and that this data could be made available to him, if he thought he might make some use of it.
Now, if you are reading this blog, chances are that you realize that with this kind of data and a little analysis, we can quickly and easily reveal powerful insights about a city’s transport network – when and where congestion occurs, average traffic volumes, key traffic generators (from taxi pick-up point data), occurrence of accidents and traffic blockages in real time, and even the estimated effects of congestion and drive cycle on fuel efficiency.
As Roger said, “They are sitting on a gold mine and don’t even know it….”
During a recent World Bank retreat, my colleagues and I visited Baltimore, a city that has developed some interesting, low-cost, innovative strategies to improve governance and increase transparency in policymaking. These strategies could be applied in many of the developing cities where we work, and, I will admit, stumbling across this initiative was akin to finding hidden treasure.