Working Paper: NBER ID: w21778
Authors: Edward L. Glaeser; Scott Duke Kominers; Michael Luca; Nikhil Naik
Abstract: New, “big” data sources allow measurement of city characteristics and outcome variables higher frequencies and finer geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big data has the most value for the study of cities when it allows measurement of the previously opaque, or when it can be coupled with exogenous shocks to people or place. We describe a number of new urban data sources and illustrate how they can be used to improve the study and function of cities. We first show how Google Street View images can be used to predict income in New York City, suggesting that similar image data can be used to map wealth and poverty in previously unmeasured areas of the developing world. We then discuss how survey techniques can be improved to better measure willingness to pay for urban amenities. Finally, we explain how Internet data is being used to improve the quality of city services.
Keywords: big data; urban life; urban economics; city characteristics; social outcomes
JEL Codes: C18; C55; C80; C83; R10; R11; R23
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| urban development (R58) | economy (O51) |
| urban density (R11) | productivity (O49) |
| urban design (R58) | crime rates (K42) |
| urban design (R58) | public health outcomes (I14) |
| big data (C55) | valuation of urban amenities (R29) |
| big data (C55) | urban service provision (R53) |