This research project explores the economic impacts of bicycle and pedestrian street improvements in the United States using multiple data sources and analytical approaches. Building on studies in New York City and San Francisco, researchers examined before-and-after data for street improvements on 14 corridors in six cities: Indianapolis, Memphis, Minneapolis, Portland (OR), San Francisco, and Seattle.
WHY DO THIS STUDY
To make cities more livable and in response to growing concerns over climate change and rising social inequality, cities across the country are promoting active transportation, and advocates are arguing for robust bicycle and pedestrian infrastructure. While studies have shown how such upgrades improve traffic safety, quality of life, and mobility for city residents, the question remains how such infrastructure improvements affect economic outcomes. A frequent argument against bicycle and pedestrian infrastructure improvements is the concern that bike lanes could discourage customers and reduce revenues. In 2013, the New York City Department of Transportation commissioned a first-of-its-kind study prepared by Bennett Midland, using sales tax data to evaluate the economic effects of street improvements, examining 7 improved corridors and 19 control corridors in three boroughs.1 The study found “convincing evidence that improved accessibility and a more welcoming street environment created by these projects generate increases in retail sales in the project areas.” In 2014, the San Francisco Municipal Transportation Agency used a similar methodology focused on sales tax data to conclude that “streetscape improvements are associated with improved economic performance for the locations studied.” This current study builds on past work by examining additional cities and incorporating new research methods and data sources.
WHAT DID WE STUDY
For the 14 corridors that received active transportation street improvements, as well as 14 control corridors for comparison, we looked at before-and-after metrics related to business activity. We examined data on retail and food sales, as well as retail and food service employment and wages. We excluded businesses that are specifically geared toward cars, such as gas stations, and concentrated rather on the types of business establishments that could be frequented by any type of road user.
WHAT DID WE LEARN
This study provides policymakers and planners with a robust analytical framework and evidence to support non-motorized transportation infrastructure investment. Overall, the study found very little evidence of active transportation street improvements having a negative impact on business or economic outcomes. In many cases, improved bicycle and pedestrian infrastructure was shown to have positive impacts on sales and employment in the retail and food service sectors. In general, we found:
--Street improvements had either positive or non-significant impacts on corridor employment and sales.
--The food service industry seems to benefit the most from the addition of active transportation infrastructure. Even in cases where a motor vehicle travel lane or parking was removed to make room for a bike lane, food sales and employment tended to go up.
--The retail industry benefits somewhat from the addition of active transportation infrastructure. In nine of our 14 case studies, retail sales and/or employment were positively impacted by the street improvements. Two case studies showed no impact, and three of the case studies generated mixed results, with some positive and some negative impacts on retail. Further study is needed to isolate causes and effects of these impacts.
OUR APPROACH
We used a variety of data sources and analysis methods to address the challenges of conducting research in this field. Each of our data sources includes variables that represent a different element of economic activity, such as wages, employment, and sales tax receipts from nearby businesses. These are all components of business activity, but they can shift or grow in different ways depending on the situation. For example, an increase in sales revenue could be interpreted in two different ways.
1. An increase in the number of people shopping and buying in the establishment, resulting in greater overall sales volume.
2. An increase in the price of each product sold, potentially due to rising lease costs, rather than increased consumer activity.
Similarly, while data on employment and wages is a strong indicator of economic activity in the long term, it is not an effective measure of immediate shifts in consumer activity, which can be affected by changes in travel patterns, like street improvements. By using several different data sources and analytic approaches, we sought to counteract these challenges and provide a holistic picture of the complex dynamics of business activity. For the same reason, we did our analysis in six cities rather than just one, in the hopes of uncovering some more or less “universal” trends among the complexity.
MULTIPLE DATA SOURCES:
We examined before-and-after effects of each street improvement using:
--LEHD (longitudinal employer-household dynamics) employment data
--QCEW (Quarterly Census of Employment and Wages) employment and wages data
--NETS (National Establishment Time Series) employment and sales data
--Retail sales tax data
MULTIPLE ANALYTIC METHODS:
We used three different methods of econometric analysis to understand the impacts of street improvements on retail and food service sales, employment, and neighborhood economics. For more detail on how these methods worked, refer to our final report. The three methods were:
--Aggregated trend analysis
--DID (Difference-in-difference) analysis
--ITS (Interrupted time series) analysis
Photo by Michael Lander
