Share this:

rachel-meltzer-80x108Local neighborhoods seldom remain static – but why do some have a greater turnover of retailers than others? And can excessive retail churn be a bad thing? Using New York business data, Rachel Meltzer investigates these questions, finding that bigger households and higher shares of white residents are most strongly associated with less retail churn, as are having more restaurants, more necessity services (like grocery stores or drug stores) and a greater diversity of businesses. Population growth, on the other hand, is the strongest predictor of greater retail turnover. She writes that local government and nonprofits can use this information to encourage more businesses that will benefit the community, such as independents and those which provide needed services. 

Urban neighborhoods are defined as much by their commercial character as their residential.   Retail services, particularly in mixed-use settings, not only provide material needs for those living nearby, but less-tangible social and cultural capital as well.  Jane Jacobs famously argued that local small businesses are not only good for services and access to jobs, but are critical to the vitality of community life.  Therefore, the stability of a neighborhood can be threatened when these businesses experience excessive churn.  While neighborhood change can bring in new amenities, filling much-needed gaps in local services, it can also introduce unpredictability in what and how these new businesses will serve the community. What kind of neighborhoods have more retail churn?  What factors matter most in explaining this churn?  Is the turnover exacerbated under conditions of gentrification?

With respect to neighborhood dynamics, there are two main reasons why businesses turn over: changes in the characteristics of the consumer pool and changes in the costs of operation.  Both the density and the composition of the consumer pool can change, reflecting not only consumption preferences, but also less-tangible cultural identities and biases.  The explicit costs of running the business, such as rent or space, can also shift.  In addition, information about the risks associated with operating in a particular neighborhood can become more accessible over time.  For example, increases in activity from other establishments can signal a more hospitable business environment (especially in markets that are otherwise hard to read), lowering the entry risk for new businesses.  And all of these drivers can be amplified by government incentives.

But all retail is not created equal, and certain kinds of businesses might be particularly vulnerable to churn.  Chain businesses may be able to better withstand shocks to the market and exhibit more stability.  Services that are more frequently consumed, especially by those in the immediate neighborhood, could be more vulnerable to shifts in the local consumer base.  Businesses that provide critical, necessity goods (versus more discretionary, “luxury” ones) could be more resilient, since their consumption may not be as sensitive to changes in discretionary income or demographic and cultural composition.

In our research, we test all of these questions for the universe of neighborhoods in New York City, an impressively dense and retail-rich city that has also undergone dramatic socioeconomic changes over the past few decades.  Because of the fine-grained nature of our business data (sourced from the National Establishment Time Series Dataset), we are able to capture the nuance of retail churn and break it down into five distinct components: staying in place, entries, exits, births, and deaths.  And, our findings are similarly nuanced.

First, consumer-related characteristics better explain retail churn than those related to the local commercial environment.  For example, bigger households and higher shares of white residents are most strongly associated with less retail churn and population growth is the strongest predictor of more turnover. This is in contrast to a negligible effect on overall churn from available commercial space or nearby retail density.  Consumer characteristics are especially predictive of first-time entries of chains into the market; relocations of businesses, on the other hand, are driven more by characteristics of the commercial environment, i.e. moving towards more/better space.  Moreover, there are not elevated rates of churn (or any of its components) in neighborhoods undergoing gentrification, after accounting for other neighborhood socioeconomic differences.


Second, the type of business matters.  Food establishments (i.e. restaurants) tend to be a more stabilizing presence in neighborhoods over time, and businesses that provide more frequently consumed necessity goods and services are more likely to stay in place.  Chain establishments are less likely to open up brand new establishments in the city, and, when they do open, are more likely to enter neighborhoods with more commercial space, lower vacancy rates, more affluent households, and fewer owner-occupied and college-educated households.  Overall, neighborhoods with less (and a more diverse) general retail concentration, as well as bigger businesses, are more stable.

Third, the nature of retail turnover matters—it is an incomplete metric to solely look at net changes in retail (which is what most public, aggregated data make available).  Our findings show that instances of increased retail churn are more often than not driven by more births or entries from other neighborhoods in the city (rather than deaths or exits).  And, lower churn is accompanied by higher shares of businesses that stay in place.   This potentially sheds a more positive light on retail turnover, if it indeed brings in new services that were previously underprovided.   While we do not observe here the exact services and goods provided by those new businesses, they are not overwhelmingly emerging at the expense of other incumbent businesses.

So, is retail stability always beneficial for a neighborhood?  Or can churn bring much-needed vitality and services?  We cannot say anything definitive about the welfare-enhancing (or demeaning) effects of retail churn; indeed the answers to these questions should be very context-specific.  However, we have been able to document the underlying factors that coincide with retail turnover (and stability), and therefore can provide insight into which strategies either mitigate against or encourage churn.

The fact that retail churn is most strongly related to local consumer demographics might suggest a “leave-it-to-the-market” approach.  However, this assumes that the market is functioning properly, with accurate information freely flowing. Rather, it is more likely the case that information about the local demographics and demand for goods and services is hard to pin down, especially for neighborhoods in flux.  And any reliable fine-grained information is rarely in the hands of small business owners or community development organizations.  In addition, the actual value of a local business may extend beyond the market transaction of the good or service it provides.  There are broader social benefits to having certain services nearby and an array of street-level activities.  And these assets are often not valued accurately, either due to the information constraints described above or coordination problems at the neighborhood level.

Here is where government, and the local nonprofits it supports, becomes important—to help the neighborhoods think holistically (and accurately) about their assets, deficits and risks.  If commercial infrastructure matters, not only in terms of physically appropriate spaces, but also economically developed retail markets, then local governments can, through zoning ordinances, allow, incentivize or even mandate the build-out of commercial spaces.  They can go further and think about what kinds of businesses they hope to attract to those spaces.  Independently owned establishments might be more likely to have ties with (and redistribute benefits to) the community; chain retailers could bring more selection and possibly lower prices and, according to our analysis, pose no significant threat of increased turnover; businesses that provide necessity services exhibit more stability and also meet more immediate needs; but a diversity of services also helps with stability.  They can work with local community organizations and business improvement districts on the ground who can better speak to the quality and patronage of the services that come and go.  Local governments could assist in the dissemination of accurate data on local consumer dynamics.  Indeed, providing businesses with more complete information and refined tools to read local markets could better inform start-up business decisions, support in-place business sustainability, and ultimately better satisfy local service needs.

This article is based on the paper, ‘Neighbourhood differences in retail turnover: Evidence from New York City’, in Urban Studies.

Featured image credit: Eric Konon (Flickr, CC-BY-NC-SA-2.0)

Please read our comments policy before commenting.           

Note:  This article gives the views of the author, and not the position of USAPP – American Politics and Policy, nor the London School of Economics.

Shortened URL for this post:


About the author             

rachel-meltzer-80x108Rachel MeltzerThe New School
Rachel Meltzer is an Assistant Professor of Urban Policy at the Milano School of International Affairs, Management, and Urban Policy. Her research centers on issues related to housing, land use, economic development and local public finance, and how public policies in these areas affect individuals, neighborhoods and cities. Current projects look at how and why retail and commercial services change in neighborhoods undergoing economic and racial transitions and how Hurricane Sandy impacted small businesses in New York City.  Dr. Meltzer is also interested in the private provision of public goods, and she has explored a number of questions related to Business Improvement Districts (BIDs) and Homeowners Associations (HOAs). She tweets @ProfRachelM.