ByChristopher H. Wheeler
Economic activity within metropolitan areas in the United States tends to be distributed unevenly. Within nearly any city, there are neighborhoods that grow-attracting businesses that provide jobs, goods and services-and there are those that do not. Why are some neighborhoods more conducive to economic development than others?
Between 1994 and 2002, for example, the St. Louis metropolitan area saw its total private sector employment grow by 12 percent, or nearly 130,000 jobs. During this same period, the number of business establishments grew by nearly 3,500, or roughly 5.3 percent.
What these aggregate figures fail to reveal, however, is a substantial difference in the experiences of neighborhoods throughout the metro area. Across St. Louis' 226 ZIP codes, employment growth varied between -93 percent and 1,100 percent, while business establishment growth ranged between -50 percent and 200 percent.
Why does economic activity vary so much across a metropolitan area? This article attempts to provide some semblance of an answer to these questions by looking at the recent experiences of a collection of approximately 15,000 neighborhoods (defined by ZIP codes) across a sample of 361 metropolitan areas in the United States. To do so, we studied the pattern of development, quantified by changes in the total number of business establishments, and identified some basic neighborhood characteristics that are associated with different levels of this development.
An obvious question that comes to mind when one begins to consider the issue of neighborhood growth concerns whether or not it really matters. That is, as long as there is job growth somewhere in a metro area, does it really matter if jobs do not grow in certain neighborhoods? Although it is certainly possible, and indeed likely, that the residents of a particular neighborhood would gain from business growth elsewhere within the metropolitan area, empirical evidence suggests that growth within one's ZIP code does indeed matter.
To begin, significant financial gains accrue to the residents of growing neighborhoods. Larger increases in the number of business establishments in a ZIP code, for example, are associated with significantly higher rates of growth in two common measures of financial well-being: median household income and per capita income. Increasing the number of newly created businesses by 100, for instance, corresponds to a three- to four-percentage point increase in the rate of growth in these two income series.
In addition, as ZIP codes accumulate more businesses, they tend to see their unemployment rates decline. The data indicate that, as the number of newly created establishments rises by 100, a neighborhood's unemployment rate drops by more than 0.2 percentage points (e.g., 5.1 percent to 4.9 percent). This relationship may reflect the fact that, for some proportion of individuals in a neighborhood, finding and holding a job in another part of a metro area may be prohibitively difficult. As the extent of economic activity taking place locally increases, therefore, their chances of finding employment rises.
Increases in the number of business establishments also correlate significantly with the level of education among the local resident population.
In particular, as the rate of entrepreneurial growth rises, the fraction of a ZIP code's resident population with a bachelor's degree or higher also tends to rise significantly. An additional 100 businesses are associated with a 1.6 percentage point rise (e.g., 20 percent to 21.6 percent) in the fraction of a ZIP code's adult population with a bachelor's degree or more. This result may be due to the desire among highly educated individuals for proximity to economic activity.
The benefits of a highly educated population, of course, are well known. Greater levels of education among a population have been shown to provide benefits in the forms of higher labor earnings, higher productivity, lower crime and better government.
When deciding where in a metro area to locate, employers take into account a number of considerations, all of which bear upon the expected profitability of the enterprise. Although not meant to be complete, this study examined several basic neighborhood characteristics in an attempt to identify which ones tend to draw (or deter) business activity.
More than anything else, businesses rely on people to accomplish their daily tasks. People, of course, provide both the labor required for the production of goods and services and the ultimate demand for those products. It is, therefore, plausible that businesses would want to set up near concentrations of people to allow easy access for both workers and consumers.
On the other hand, with highly populated, dense areas comes congestion and higher prices for land. In addition, neighborhoods with large populations may be primarily residential, which may place restrictions (legal or otherwise) on the extent to which business activity can grow. The relationship between population and economic activity, therefore, is theoretically ambiguous.
Examining data from more than 15,000 ZIP codes between the years 1998 and 2002 reveals that more populous neighborhoods tend to see larger increases in the number of businesses than do less populous ones. As population roughly doubles in a cross section (e.g., comparing a ZIP code with 1,000 residents to one with 2,000 residents), the number of newly created business establishments rises by nearly six, on average.
At the same time, greater population density tends to be inversely associated with business growth. The data show that a doubling of the number of residents per square mile in a ZIP code is associated with one fewer business establishment created between 1998 and 2002. These results suggest that, while populated areas may tend to attract entrepreneurial activity, densely populated areas seem to deter it.
While population and density may influence the location decisions of businesses, the characteristics of those populations may also be important.
This study examined the relationship between the number of newly created businesses and the following four basic features of the local neighborhood: per capita income, percent with a bachelor's degree or higher, fraction between ages 25 and 44, and unemployment rate. The results indicate that each of these characteristics is strongly associated with the extent of business establishment growth.
Higher per capita income is associated with greater numbers of businesses. As per capita income doubles, for instance, the estimates suggest that an additional 45 establishments are created.
More educated neighborhoods also tend to see more sizable increases in their business activity. A 10 percentage point increase (e.g., 10 percent to 20 percent) in the proportion of a ZIP code's population with a bachelor's degree or more tends to be accompanied by an additional 11 businesses generated during this time period.
ZIP codes with higher fractions of individuals between the ages of 25 and 44 also tend to gain economic activity in larger quantities. A 10 percentage point increase in the fraction of a ZIP code's population between the ages of 25 and 44 tends to be accompanied by 28 more business establishments created over five years.
Higher rates of unemployment, on the other hand, tend to be negatively associated with the expansion of business activity. A one percentage point increase in a ZIP code's unemployment rate (e.g., 5 percent to 6 percent) is associated with nearly three fewer business startups. To be sure, part of this association may stem from the fact that growing areas tend to see reductions in their unemployment rates. Nevertheless, it also suggests that neighborhoods with high rates of joblessness may find themselves in a vicious cycle where high unemployment deters new business startups, thus reinforcing high rates of unemployment over time.
There are a number of reasons to suspect that employers may also want to situate themselves near other employers.
To begin, there may be benefits to clustering in areas where customers do their shopping (e.g., malls) or where large numbers of workers already go to work (e.g., office parks). Second, employers may want to be close to the companies with which they do business because proximity reduces the cost of transporting goods and people. Third, there may be a variety of advantages associated with seeing the operations of other businesses. Proximity allows businesses to learn from and keep tabs on competitors. It may also allow businesses to develop new products or enhance their day-to-day operations by observing a wide array of economic activity.
All of these reasons suggest that new business startups may be especially prevalent in neighborhoods with large numbers of existing businesses. The data, as it turns out, strongly bear out this conclusion. As the number of establishments in a ZIP code doubles, the number of newly created businesses tends to increase by more than 10. Growth of business activity, interestingly, also tends to be positively related to a ZIP code's total employment. As employment doubles, establishment growth rises by more than seven businesses.
Not all business establishments, of course, are engaged in the same line of work. Retail outlets tend to be concerned with very different activities from those in manufacturing. The environment that each type of business seeks may, therefore, differ significantly from one industry to another.
Some of these differences are apparent from the Table, which summarizes the correlations between six basic ZIP code characteristics and the number of new business establishments created between 1998 and 2002 in each of 13 broad industries.
Several of the industries, including manufacturing and wholesale trade, tend to expand the most in ZIP codes with low population densities and small numbers of existing business establishments (either belonging to the same industry or total). Wholesale trade, construction, and transportation and warehousing all tend to grow more in ZIP codes with fewer college-educated residents.
On the other hand, industries that tend to employ relatively highly educated workers and pay relatively high wages—such as real estate, finance and insurance, and professional, scientific, technical and health services—all tend to locate in densely populated neighborhoods. Employers in these sectors also tend to locate in ZIP codes with large numbers of established businesses (either of the same industry or overall) and larger numbers of college-educated individuals.
Interestingly, it is not simply "white collar" sectors that gravitate toward such environments. Employers in the accommodation and food services sector also tend to be drawn to neighborhoods with high levels of density, education and business activity. This pattern is very likely the product of a demand effect: Neighborhoods with high levels of business activity and educated residents may also have a particularly strong demand for restaurants and coffee shops, for example.
The characteristic that has the most uniform association with the growth of business establishments of all types is the unemployment rate, which exhibits a strong, negative association in nearly every instance. Evidently, employers of all types tend to stay away from neighborhoods with high rates of joblessness.
Identifying the reasons why businesses settle where they do is crucial for any neighborhood development program. This article has offered a brief look at this issue, documenting the types of characteristics that are associated with the expansion of business activity in a collection of ZIP codes in the United States.
One fundamental result from this study is that different types of employers tend to seek different environments. The types of businesses that do well in densely populated or highly educated neighborhoods, for example, tend to be quite different from those that seek areas with less activity and lower levels of education. As such, a plan to target development for, say, a traditional downtown area should involve a completely different set of employers than a plan to develop a suburban neighborhood.
|Industry||Population Density||Own Industry
|Percent of Population with College Degrees||Per Capita Income||Unemploy-
|Transportation and Warehousing||-||-||+||-||0||-|
|Finance and Insurance||+||+||+||+||+||-|
|Professional, Scientific and Technical Services||+||+||+||+||+||-|
|Arts, Entertainment and Recreation||0||0||+||+||+||0|
|Accommodation and Food Services||+||+||+||+||+||-|
Note: Significantly positive and negative associations are denoted, respectively, by a "+" and "-". The "0" implies no significant correlation.