What's new in regional and urban economics?

The journal that I edit, the Australasian Journal of Regional Studies, is about to release its latest issue (more on that in a future post). That makes it timely to think about what's new in regional and urban economics. Actually, it's probably always a good time to think about what's new, but this is a particularly useful time because we can rely on this new NBER Working Paper (ungated here) by Ran Abramitzky (Stanford University), Leah Boustan (Princeton University), and Adam Storeygard (Tufts University).

The paper mainly covers new data sources that have come into more regular use in recent years, and provides a good survey of the literature that has developed using each source. Abramitzky et al. also identify some new use cases for some of the data sources, which points to new research directions or extensions of existing work. For new (or experienced) researchers looking for inspiration for their next research project in regional and urban economics, this paper is a good one to read.

To save you a little bit of time though, here are some of the key data sources that Abramitzky et al. discuss (some of which I have grouped together differently than they do). The first is historical (US) Census records:

The US Census is far from a “new” data source, having provided the backbone of empirical research in urban economics and other applied fields for decades. Yet advances in record linkage have allowed researchers to convert (historical) census data into large panel datasets that follow individuals over time. This longitudinal data opens up a set of new research questions on spatial topics, including the determinants of geographic mobility, the long-run effect of childhood exposure to environmental conditions or economic shocks, and the causes and consequences of neighborhood change within cities.

Complete census records, including an individual’s name and detailed location information, becomes available to the public 72 years after the Census is taken; the 1950 Census was just released in 2022.

Sadly, this is not a data source that is available for many countries (including New Zealand, where Census unit records prior to 1966 were destroyed). However, the ability to link people over long periods of time (including between generations) has opened up a wealth of new research questions. Second, Abramitzky et al. discuss digitised historical maps and directories:

Digital spatial data in Geographic Information Systems (GIS) is indispensable for a variety of modern urban applications but, until recently, historical maps were not compatible with this tool. In recent years, economic historians and other social scientists have digitized a wide range of historical maps, including census geography, and environmental and land management maps. These efforts have opened up study of historical neighborhoods and the effects of proximity to relevant geographic features like administrative boundaries, industrial sites, religious and cultural institutions and the epicenters of natural disasters.

I have a project in progress (which is, unfortunately, somewhat stalled due to non-map-related data issues) that has made use of digitised boundary maps for the electoral boundaries from past New Zealand elections (more on that in a future post, if that project ever gets re-started). However, the key point is that there is a wealth of information stored in historical maps and archives that are currently underutilised. On a related note, Abramitzky et al. note that:

Beyond mapping the location of households or firms, GIS is also useful for reconstructing historical transportation infrastructure via waterways, roads or railroads.

Given that past infrastructure patterns, including transportation and other networks, affect the patterns observed today, these seem like important sources. Third, Abramitzky et al. talk about a range of remote sensing data, including night lights (from satellite imagery), and physical attributes like air quality, weather variables, and building footprints and heights. These data are often available at small spatial resolutions, allowing analysis at very fine-grained spatial levels. However, it is worth reading the paper (and the references) carefully, as they also identify issues to be aware of with remote sensing data.

Fourth, Abramitzky et al. very briefly discussed picture and video data, including Google Streetview, and CCTV camera data. There are definitely some interesting use cases for these atypical data sources, and you can expect to see a growing use of them in future research. Fifth, Abramitzky et al. discuss mobile phone or smartphone data, including data derived from particular smartphone apps:

Mobile phones provide information about the location of the people who use them, and sometimes the vehicles they drive. Broadly, there are two kinds of cell phone data. Call data records (CDRs), provided by network operators, report the location of the phone at the time a call was made or received, as triangulated from the network of cell towers. In some cases, the counterparty to the call can also be identified...

For research purposes, CDRs have been mostly replaced by data from smartphones, whose apps collect more accurate GPS-based locations at all times (regardless of whether a call is placed)...

Researchers have used location data from individual apps with which they have developed relationships. Most prominently, Uber has provided data on its trips to several groups of researchers...

Similarly, they discuss transportation data derived from vehicle location trackers, transit cards, or electronic tolling stations, or electronic payment systems for transit riders. All of these sources are useful for identifying transportation and commuter flows, which have high policy relevance.

Finally, Abramitzky et al. give a rapid-fire selection of other data sources that are only beginning to be used, including e-commerce and payment card transactions data, posted prices and listings (often scraped from websites), routinely collected administrative data (which in my experience will generally require a lot more data cleaning), and text as data.

Clearly there are lots of new and emerging data sources in use in regional and urban economics. However, Abramitzky et al. are clear that developing skills with these data sources and the appropriate methods for dealing with them is not feasible for everyone. They do, however, suggest a solution:

We encourage urban economists, both young and old, to familiarize themselves with these data sources and to become conversant in some of the methods needed to build new data from textual corpora, digital traces, and images and video of the world around us, including large language models and deep learning more broadly. We emphasize the word “conversant” because we do not think that all of us need to become experts in these techniques. Rather, we anticipate and encourage interdisciplinary collaboration with scholars around the university in data science, computational linguistics, computer science, geography and the natural sciences who know these methods well and can thus complement the research focus and conceptual framework specific to urban economics.

So, we should definitely expect the current trend for larger, more interdisciplinary, research teams to continue into the future.

[HT: Marginal Revolution]