Stadium gentrification Data
from “The Attrition of the common fan”

Below you will find links to the spreadsheets, codebooks, Stata files, raw data sources (primary sources) used to complete the data analysis for “The Attrition of the Common Fan: Class, Spectatorship, and Major League Stadiums in Postwar America,” my piece for Social Science History on the gentrification of stadiums in post-World War II America:

Premium Seating Dataset (.xlsx)- This original dataset contains data on premium seating capacity (luxury boxes and club sections) and non-premium seating capacity for Major League Baseball, National Basketball Association, and National Hockey League venues as of 2014, as well as for the facilities that they replaced. This data thus allows for comparing the seating arrangements at two successive generations of arenas/stadiums.

Premium Seating Data Set (.dta)- This is the same dataset as above already converted to .dta format for Stata users.

Data Codebook and Bibliographic Notes (.pdf)- This file includes descriptions of all dataset variables, as well as variable-by-variable information on the provenance of the data.

Statistical Coding (.do)- Those interested in replicating or more closely scrutinizing the statistical analysis underlying the article can do so using this Stata .do file. If you are planning to execute the .do file in Stata on your computer, you may prefer a copy of the file without “log” and “use” coding specific to my file system.

Association of Luxury Suite Directors (ALSD) Manuals, 2011-2014 (.xls)- These spreadsheets contain raw data on luxury suite numbers at major-league venues compiled by the ALSD, and served as an important source for the Premium Seating Data Set linked to above.

Team-Specific Data Sources (.pdf)- This folder contains PDFs of individual sources containing data on seating capacities not covered in the ALSD spreadsheets, as well as on venue square footage. PDF files pertaining to individual franchises in the “Team Stadium Data” folder often contain scans of multiple sources. For example, if the square footage data and seating data for a specific team came from different sources, the individual PDF for that franchise contains scans of both sources.