Local Commerce is comprised of local, or "placed-based", expenditures on everyday goods and services.
- Place-Based: Expenditures are local because each transaction is geographically tied to the metro area in question. For transactions to be included in our data assets, they must either 1) occur at a merchant located in one of our metropolitan areas, or 2) be executed by a consumer that lives in one of our metropolitan areas.
- Everyday Goods/Services: Everyday goods and services are those that are most frequently purchased from retailers (e.g. groceries, restaurants, or clothing) and are often made with credit or debit cards. They would not include purchases of goods likely to be financed (e.g. cars). Because the billions of financial transactions we observe have all been executed vis-à-vis one of our consumer card products, we are capturing the everyday financial behaviors of consumers.
To the extent that the locality of commerce is driven by either the location of the merchant, or the location of the consumer, there are two LC views: the merchant and consumer views. To the extent that the majority of purchases made by consumers occur at merchants located in their own metropolitan areas, these views overlap significantly. However, the nuanced distinction between the two can provide powerful insight into the evolving nature of local economies.
Table 1 – Inclusion criteria for transactions based upon the location of the consumer and the merchant
The merchant view of LC is comprised of transactions that occur at merchants inside one of the metro areas. In this frame, the location of the consumer's residence (within the US) does not influence inclusion of a transaction into the asset. It is the view that we have provided since the launch of the first LC report: Profiles of Local Consumer Commerce: Insights from 12 Billion Transactions in 15 US Metro Areas. Now that we have launched the consumer view of LC, it is useful to draw the distinction. In the merchant view, we see a greater variety of consumers shopping at a geographically contained set of merchants.
By focusing on transactions made at merchants in a metro area, the merchant view sheds light on the impact of an evolving marketplace on local merchants. It provides local public officials and nonprofit officers with information about local fiscal dynamics and geographic disparities in access, while local business owners can better understand the local market.
The consumer view of LC, by contrast, captures spending by consumers from the perspective of their residence. For example, if we are discussing the consumer view in Detroit, consumers must live in the Detroit metro area. In this frame, inclusion of transactions is not driven by the location of the merchant (so long as they are domestic). The consumer view provides much wider variety in purchases for a geographically contained set of consumers.
By focusing on transactions made by residents of a metro area, the consumer lens speaks to the evolving set of commercial options available to the consumer. It can shed light on the extent to which consumers choose to make purchases from local or remote retailers, and the likelihood of said purchases being made online. Importantly, it shows how these choices change over time. In so doing, the consumer view provides a powerful complement to merchant view in our ongoing exploration of local economies.
Table 1 provides a graphical representation of geographic coverage. Note that the cell that captures the intersection of non-resident merchants and non-resident consumers is not covered by either view. To the extent that neither the merchant nor the consumer is tied to a specific metro area, commerce ceases to be local to the metro area in question.
Both place-based views of LC can measure local economic vibrancy and speak to local fiscal dynamics. These lenses provide valuable information for a variety of stakeholders, including but not limited to the following:
- Local elected officials
- Local fiscal and economic development officials
- Business owners, local or otherwise
- Academic researchers concerned with household and firm behavior
- Public policy researchers focused on local economic and social dynamics
That said, each lens allows stakeholders to ask different questions. The merchant view of LC is better suited to understanding economic activity contained entirely within a given jurisdiction, while the consumer view of LC is better suited to understanding how the locus of economic activity is changing over time. To the extent that consumers tend to spend most of their money within the metro area in which they reside, these views overlap significantly. However, the ways in which they differ provide us with a richer understanding of local economies. The difference in geographic bucketing of economic activity allows for targeted inquiries, including, but not limited to the questions raised in Table 2.
The LC lens is a powerful complement to public data sources because it is administratively collected, it is high volume, and it can avoid a tradeoff between geographic granularity and reporting frequency:
- Most public data sources, like the Survey of Consumer Finances (SCF), rely on respondents to provide historical information from personal recollection. The LC lens is constructed on credit and debit card transactions that are administratively collected in the normal course of operations for JPMC.
- Surveys tend to capture relatively small samples of the broader population. The Current Population Survey (CPS), for example, relies on a sample of about 60,000 households. By contrast, the administratively collected data underlying the LC assets rely on over 64 (merchant view) and over 87 (consumer view) million consumers, respectively.
- Local decision makers need information about local activity to better understand the local economy and the impact of policy interventions. The American Community Survey (ACS) 1-year estimate will report locally-relevant estimates (e.g. county or metropolitan area), but the reporting frequency is annual. On the other end of the spectrum, the Monthly Retail Trade Survey (MRTS) reports on retail activity each month, but at the national level. The LC lens has both geographic granularity and high reporting frequency, insofar as it provides metro-level views of consumer behavior in every month.
To better understand the contributions of the LC lens, it is useful to place it within the context of existing data sources. The most commonly used indicator of US economic health is gross domestic product (GDP), which is the total value of goods produced and services provided by firm assets located in the country. Broadly speaking, GDP is comprised of consumer spending, gross investment, government spending, and the net value of exports.
Figure 1 – Components of GDP
The first category—consumer spending—makes up more than two-thirds of GDP. A better understanding of this key source of economic activity can improve our understanding of the drivers behind economic growth. The consumer portion of GDP is defined as personal consumption expenditures (PCE), which is the amount of goods and services purchased by households and nonprofit institutions serving households who are resident in the United States. In other words, these are purchases by end users.
The LC and PCE lenses contain data that can be used to better understand final consumption in the US, but there are some differences. First, while there is considerable overlap, there are some kinds of purchases that are included in both the merchant and consumer views, but not PCE. The converse is also true.
Figure 2 – Differences between LCC and PCE
Second, PCE is an aggregate measure of consumption for the entire US economy, which is simultaneously its greatest strength and weakness. As a guide for how consumers are doing across the entire country, PCE is invaluable, but it offers limited insight into how consumers are doing in different places within the country. A clearer view of variation within the country enhances our collective understanding of economic drivers, which is where the composite LC view offers an advantage. Since it is built on such a large set of geographically informed data, it can speak to economic activity in specific cities. This fine-grained view offers a more solid basis for tactical investment decisions by households and firms in their local markets.
About two-thirds of US Gross Domestic Product (GDP) is comprised of the purchase of goods and services by individuals and non-profits. Any analysis of the economy and where it is heading must have good measures of this activity. However, new purchasing channels are reshaping consumers' shopping behavior. For example, as the availability of online commerce expands, many merchants have less of a need to be physically close to consumers while other merchants are recognizing the need to be even closer just to compete. Alternatively, the proliferation of card and mobile payments creates new infrastructure needs for merchants, and lessens the extent to which they must handle cash.
Older merchants, large and small, are finding their operating strategies tested by these changes. In addition to shifting patterns in consumer spending, we are also seeing increases in expenditure on services relative to expenditure on goods. This evolving dynamic tests our ability to understand how the economy works with conventional statistical measures. Commonly relied upon data sources were developed based upon an older, different looking market.
To understand market behavior, we must understand the strategies employed and decisions made by consumers and merchants. Market demand is driven by consumer preferences, and it is the role of firms to satisfy demand. The way in which firms satisfy consumer demand (e.g. their chosen mix of labor and capital) determines how many people can be employed and what they get paid. The choices a firm makes, however, depend on which options bring the highest return on investment. If firms, as alluded to above, are making different choices about where they should be located, what implications does this have for who gets hired and where they live? To better understand questions like these, we need to know which populations drive local spending, and where they reside. Moreover, we need a better method of distinguishing which purchases are made from local merchants versus purchases from merchants that are located far from where consumers live. Both the merchant and consumer views of LC shed light on this distribution of purchasing activity.
As of December 2018, the merchant data asset is comprised of over 25 billion credit and debit transactions from over 64 million consumers in 14 US metro areas. (Since we are cumulatively building the data series, the number of transactions and consumers grows each month.) Credit and debit card transactions are particularly useful for this work because each transaction contains attributes of both the consumer and the merchant. The base unit of analysis is the transaction. In the merchant view, each contains the age and income of the consumer, the size and product type of the merchant, and the zip code of both the consumer and the merchant.
As of December 2018, the underlying consumer data asset is comprised of over 90 billion credit and debit transactions from over 87 million consumers across the US. In this view, each transaction
Figure 3 - The high volume of transactions provides a more granular view than other data sources
Currently, merchant view transactions come from merchants in 14 Core-Based Statistical Areas (CBSAs), or metropolitan areas: Atlanta, Chicago, Columbus, Dallas-Ft. Worth, Denver, Detroit, Houston, Los Angeles, Miami, New York, Phoenix, Portland (OR), San Diego, and San Francisco. The choice of these metro areas was based upon data coverage, geographic distribution, and a desire to vary the metro area size and industrial base in our set. In the consumer view, we do not restrict the locations in which consumers can reside. However, metro-specific analysis is limited to the 14 CBSAs included in the merchant view.
When analyzing LC data, we break up the 14 metro areas we track into three groups: small, mid-sized, and large. These groupings are useful for illustrating how local commerce can grow quite differently depending on the characteristics of the metro area. We focus on population size for this grouping. Specifically, we ranked each of the metro areas by their populations, and split them into three groups based upon that rank.
Despite some differences in the kinds of retail activity covered, both the local commerce data underlying the LC assets and the data used to generate estimates of the Personal Consumption Expenditures (PCE) component of GDP inform our view of consumption activity in the US. The source data for PCE estimates come, in large part, from the Annual and Monthly Retail Trade Surveys (ARTS and MRTS), produced by the US Census Bureau. These surveys seek to capture aggregate sales volumes at retail and food service stores, as well as inventories held by retail stores. This mail-out/mail-back survey of 12,500 merchants across the country remains the most conceptually comprehensive measure of retail activity in the US.
The LC and MRTS lenses differ in important ways. As vital as the MRTS is, it relies entirely on self-reported data from a relatively small number of firms. The sample size limits the ability of the MRTS to speak to local conditions, which is why the MRTS only reports national numbers. The LC lens, by contrast, offers measured data on realized transactions from millions of consumers. The sheer volume of data permits us to report local estimates with confidence.
The goal for both the LC and MRTS lenses is to capture purchases by end users. MRTS does this by asking a limited sample of firms about their sales, thereby avoiding business-to-business transactions. The LC lens, by contrast, uses the card choices of consumers. We exclude virtually all business-to-business purchases by excluding commercial cards from our transaction set, though it is possible for small business owners to make purchases of factor inputs with their personal cards. Moreover, the MRTS targets firms that have been in existence long enough to have acquired employees, and it is structurally biased towards the inclusion of larger firms. By using card spending, the LC lens can see spending at even short-lived merchants, as well as small service providers (e.g. hairdressers, small health providers, etc.) that are likely to be missed by MRTS. In short, neither source is perfect, but the limitations of one are often the strength of the other. As such, LC data provide a powerful and unprecedented complement to MRTS that is freely available to the public.