5. Analyzing Local Economics

To gain a better understanding of the trade area, it is important to recognize existing trends and conditions of the local and regional economy.  Accordingly, this section provides methods and data sources for examining a variety of economic information that describes, income and employment trends, sales trends, and other important statistics.

When analyzing local economic data is important to keep some key points in mind.  First, data analysis is not a means to an end, but rather is intended to stimulate discussion, to reaffirm current thinking about the local economy or to challenge local perceptions.  The data are used to identify strengths in the local economy that can be built upon or weaknesses that need to be addressed.  The data analysis is also intended to identify potential threats to the economy as well as opportunities.  If for example, the data analysis reveals that the community has a strong presence in a certain type of manufacturing, but at the national level that industry demonstrates stagnation and even decline, is this a threat or opportunity?  Data analysis cannot answer this question, but rather helps refine and focus discussions.  In the end, data analysis will help you better understand what is happening with the local, regional and national economies.

Second, when analyzing the data, it is important to keep in mind that one is not necessarily looking for a single answer but is rather looking for patterns in the data.  There is a "story to be told" about the local economy and the data helps uncover and tell that story.  To accomplish this one must make comparisons between and within other local, regional and national economies.  One must also look for trends over time.  If a locally important industry is stagnating or declining, is this a local phenomenon or is it a reflection of larger regional and national trends.  One is also looking for surprises or to have their perspectives challenged by the data.  Local knowledge of the economy is important in analyzing the data and is vital to reinforcing and/or challenging prior beliefs. 

Finally, it is important to keep in mind that in analyzing the local economy one is attempting to gain insights into local strengths, weakness and trends and not necessarily precision.  Users of this guidebook will discover that we are teeming with data and methods to analyze that data.  Without due care, one could spend days, weeks, even months collecting and analyzing data worrying about the value of the fifth decimal place.  The power of modern spreadsheet programs allows analysts to twist and turn the data in countless ways.  Remember, one is looking for insights, not necessarily precision.

Income and Employment Trends

Personal Income Trends

Personal income trends provide an important measure of economic activity for a local area over time. Personal income consists of the income that is received by persons from participation in production, from government and business transfer payments, and from government interest.  When compared to state and national trends, it provides an indication of how well the local area's economy is performing.

The Bureau of Economic Analysis (BEA) prepares regional economic accounts for the United States. These accounts provide estimates of State and local area personal income. The BEA prepares quarterly and annual estimates of personal income by type of income and place-of-residence and estimates of labor and proprietors' earnings by major industry by place-of-work for all States and the District of Columbia.

BEA prepares the only detailed, broadly inclusive economic time series for local areas (counties, metropolitan areas, and BEA economic areas) that is available annually. Estimates of total and per capita personal income, beginning with 1969, are available for each of the 3,110 counties and county equivalents, the 335 metropolitan areas, and the 172 BEA economic areas in the United States.  Furthermore, these estimates are released 17 months after the end of the year. Detailed annual estimates of earnings and employment by industry, transfer payments by major program, and farm gross income and expenses by major category are available as well. These estimates, along with a description of the methodology used to prepare the estimates, are available on the Regional Economic Information System (REIS) CD-ROM and on the Internet at www.bea.doc.gov/bea/regional/reis/.  Exhibit 6.1 shows an example of how personal income trends can be depicted. 

Exhibit 6.1 provides sample trend data to describe Jefferson County, Wisconsin.  An index of Growth was added to show how different areas have grown since 1990 (i.e. an index of 161% indicates that the personal income has grown 61% since 1990).  In this example, Jefferson County has slightly exceeded the U.S. growth in personal income.  This provides one indicator of the relative economic health of this region.

Earnings Mix

While personal income trends relate a community's relative economic health as a whole, the earnings in various sectors provide a snapshot of the industry mix in an area.  Earnings include wage and salary disbursements, other labor income and proprietor's income (both farm and non-farm).  These numbers can be related as individual numbers, or as a percentage to provide an understanding of the industry distributions.  Furthermore, comparing the numbers for a local area to those of a larger area, such as an entire state, point to differences in the local economy that may be useful in subsequent market analysis steps.  As with personal income trends, these figures are also available through the Bureau of Economic Analysis at: www.bea.doc.gov/bea/regional/reis/.

Exhibit 6.2 presents an example that compares two counties with the State of Wisconsin.  These numbers relate percentages of total earnings in a variety of industry sectors.  An analysis of these numbers show that the local area has a large manufacturing base when compared that of the State.  Furthermore, both the retail and service sectors show a lower percentage of earnings.  As a market analysis often focuses on these two sectors, this example points to important trends that must be recognized.

Labor Force and Unemployment

Labor force and unemployment data provide important information on the size and stability of a local economy.  State employment departments in cooperation with the U.S. Bureau of Labor Statistics make this data available on a timely basis.  In Wisconsin, the Department of Workforce Development offers labor market information online for counties and certain cities.  This information is available at http://www.dwd.state.wi.us/lmi/default.htm.

Local Firm Employment

An analysis of local employers provides insight into the types of larger businesses in the area that may provide drawing power.  Furthermore, identifying these employers, their number of employees, and their locations may help in later analysis, as they aid in determining daytime employee populations for the trade area.   Employer statistics are often available through the local chamber of commerce.  However, these numbers are also available on the Internet through state employment departments.  For an example, see the Wisconsin Department of Workforce Development's County Profiles.  These profiles contain information on the largest industries and employers as well as county population, industry change, commuting patterns, and wages and income.

One common measure used to assess the region's performance in capturing local markets as well as assess the level of relative dependence on a particular industry is the Population/Employment Ratio.  An example of this simple ratio is provided in Exhibit 6.3 using data for a handful of Wisconsin communities. Note that in addition to the community of interest, in this example, Platteville, a number of "comparable" communities and the state of Wisconsin are included as reference points.

The Ratio is computed for the region of interest along with the state or the nation for comparison with subscripts identifying region (r), industry (i) and year (t):

P:E_rit = POP_rit/EMP_rit.

Where

The P:E Ratio is traditionally used to assess the region's performance in capturing local markets.  If population serves as a surrogate for regional demand for a particular industry and employment measures the region's ability to supply the industry's product, then the P:E Ratio represents a simple measure of regional demand and supply.  If the P:E Ratio is equal to some critical value, then demand is said to be equal to supply and the region is capturing local markets.  If the P:E Ratio is larger than some critical value, then demand is said to be greater than supply and the region is not capturing local markets and expansion potential is said to be present.  If the P:E Ratio is less than some critical value, then demand is said to be less than supply and the region is not only capturing local demand but is importing demand for surrounding regions.  For the analysis presented here, one critical values of the P:E Ratio are provided for comparison, Wisconsin, as well as a selection of comparable places.

A second interpretation of the P:E Ratio is that it provides a measure of relative dependence on specific industries.  A P:E Ratio that is relatively small (i.e.,  high levels of employment given the region's population) is an indication of higher levels of dependence.  Conversely, a P:E Ratio that is relatively large (i.e., low levels of employment given the region's population) is an indication of lower levels of dependents.  As an example, the critical value of the P:E Ration for eating and drinking places in 29 suggesting that it takes one employee in this sector to satisfy the demands of 29 residents.  Note that this value is significantly smaller than the P:E Ratio for furniture stores that have a critical value of 263 suggesting that it takes fewer employees in this sector to satisfy local demand.

Another widely used tool used to analysis local economic strengths and weaknesses is the Location Quotient.  Originally developed by regional economist to aid in the construction of economic multipliers, analysts have found that the Location Quotient (LQ) can be a powerful indicator by itself.  Using either employment or income data for local sectors this tool compare local shares, such as those reported in Exhibit 2, to a national or state average.  An example of Location Quotients for the City of Platteville and a handful of comparative places are provided in Exhibit 6.4.

 The LQ is computed simply as the percentage of employment, or income, in a certain sector divided by the percentage of economic activity in the same sector for either the state as a whole or the nation:

LQ_ij = Share EMP_ij / Share EMP_in

Where

Much like the Population:Employment ratio the Location Quotient is a measure of the ability of the local market to capture local economic activity.  The Location Quotient's critical value is one; if the computed LQ is greater than one for any sector then that sector is said to be a strength of that community, if the LQ is less than one, then the community is weak in that sector.  For our example community Platteville, the LQ for general merchandise stores is 2.11, which is significantly greater than one indicating that this sector is strength for Platteville. For apparel and accessory stores, the LQ for Platteville is only .56, suggesting that this sector is not a strong point for Platteville.  But, if one compares the LQ for Platteville across similar places, one sees that apparel and accessory stores are a weakness for most communities of Platteville's size.  The reason for this poor performance is due to the fact that apparel and accessory stores tend to cluster in larger communities, particularly those with shopping malls.  For LQ there is the critical value of one, but the results must also be viewed in light of what is typical for other similar size communities.

There are two other potential ways to interpret the LQ.  One is to think in terms of specialization of the local economy.  For sectors with LQ greater than one, we could say that the local community specializes in that particular sector and is considered a strength.  If the LQ is less than one, then we simply say that this particular sector is not an area of specialization; an interpretation not quite as strong as concluding that the sector is a weakness.  The final interpretation focuses on the exporting potential of the sector. If the LQ is greater than one, the sector can be said to be exporting sales outside of the community.  Conversely, if the LQ is less than one, the sector is a non-exporter and importing must satisfy local consumption of the goods.  As seen below, this latter interpretation closely follows that of a Pull Factor.

Sales Tax Trends

Understanding economic development trends entails an analysis of the strengths and weaknesses of the existing retail market. By understanding the performance of the local retail market, local leaders and development practitioners can foster a more conducive environment for retail business development. This also becomes a base for further market analysis that will help current and future business operators make more informed business decisions.

To achieve this end, numerous research tools have been developed and refined over the years to help identify county strengths and weaknesses.  In this article we review the tools of Trade Area Analysis as development by Ken Stone and Jim McConnon at Iowa State University and later refined by Ron Hustedde, Ron Shaffer and Glen Pulver at the University of Wisconsin. The tools of Trade Area Analysis allow the analyst to estimate net inflows ("surpluses") and outflows ("leakages") of retail dollars.

While a wide range of data are available, such as the U.S. Census of Retail Trade and a number of private data firms, one of the best sources of information is generally drawn from sales tax receipts.  Given that Wisconsin law allows counties to adopt a local option sales tax, detailed and timely data for a number of Wisconsin counties are available for analysis.  This study uses these sales tax data for those counties that have elected to implement the tax.

Other states have more detailed sales tax data available at the city or town level.  If available, the Trade Area Analysis techniques described in this article can be applied at a more local (and useful) level of study.

The most important component of Trade Area Analysis is the estimation of a retail market's potential. While there are several complex methods that may be used to estimate market potential, the method used here is perhaps the simplest.  It should be kept in mind that Trade Area Analysis is based on averages.  Many times there are mitigating circumstances, such as proximity to large population centers, interstate highways, or regional shopping centers, that will cause market potential to deviate substantially from actual market conditions.  Hence, these tools should be viewed as only one means to examining local retail markets.

The following steps and example for Marathon County, Wisconsin describe the calculations.  While the example uses total taxable retail sales, it can also be performed using more specific store groupings (building and materials, general merchandise, food stores, auto dealers & service stations, apparel & accessory, furniture & home furnishing, eating & drinking places, and miscellaneous retail stores).

Step 1:  Estimate State Per Capita Expenditures

Per capita expenditures are calculated by dividing the state's actual level of retail sales by the state's population.  In this case, data for only the 52 Wisconsin counties with county sales tax were used.  Total taxable sales is available from the Wisconsin Department of Revenue, County Sales Tax Reports on the web at http://www.dor.state.wi.us/ra/05cosatx.pdf .  The 52 counties had a combined population of 3,410,000.

Step 2:  Calculate Index of Income

Index of income is a proxy measure for the relative wealth of a county compared to the state (state being defined here as the 52 counties with a county sales tax).  It is reasonable to expect that wealthier counties have a higher expenditure rate than the state average.  Similarly, poorer counties may have lower expenditure rates. The index of income is a simple measure to adjust county expenditure rates and is simply the ratio of county to the state per capita income.  A county by county index of income used in this analysis is available from the author.  A sample calculation for Marathon County is presented below:

Step 3:  Calculate Trade Area Captured

Trade area captured is defined as the number of full-time customer equivalents being serviced in a particular county. Trade area captured is calculated by dividing the county's actual retail sales by state's (52 counties with county sales tax) per capita sales adjusted for income differences as measured by the index of income.  A sample calculation based on total taxable retail sales for Marathon County is presented below:

Step 4:  Calculate Pull Factor

The pull factor, or index of pulling power, is a proxy measure of the relative strength of the county's retail market. The pull factor is calculated by comparing the trade area captured for the county to its population. The pull factor is calculated by dividing the trade area captured by the population.  Pull factors greater than one may result from drawing tourists or customers from surrounding counties. A pull factor less than one indicates the county is losing customers to other retail markets.  A sample calculation based on total taxable retail sales for Marathon County is presented below:

Step 5:  Potential Sales

Potential sales are an estimate of the sales level that a county should achieve if it were performing on par with a statewide (52 counties with county sales tax) average, after adjusting for income.  A county's potential sales are calculated by multiplying state per capita sales by the county's population and an index of the county's buying power.  Here the county's buying power is the ratio of its per capita income to the state's per capita income.  A sample calculation based on total taxable retail sales for Marathon County is presented below:

Step 6:  Surplus or Leakage

By comparing the potential sales of the county with the actual sales realized a measure of retail surplus or leakage can be estimated.  If actual sales are greater than potential sales, the county can be said to have a retail trade surplus.  If potential sales are greater than actual sales, the county is said to have a retail trade leakage.  Alternatively, the surplus and leakage measures places a dollar value on the relative size of the pull factor where retail surpluses are associated with pull factors greater than one and leakages are associated with pull factors less than one.

Analyzing the Results

Because actual sales are greater than potential sales in this example, Marathon County is said to have a $163,000 surplus in the retail market.  In other words, the dollar value of the pull factor being greater than one is approximately $163,000. 

When analyzing the findings, one must ask what market forces may be causing the results in the data.  In the example, is it possible that neighboring counties have fewer retailers that would pose direct competition?  Alternatively, are the existing businesses in the county doing an especially good job at penetrating the regional market?  On the other hand, if the county had a significant leakage of dollars, could existing businesses change their mode of operation to recover the observed leakage?  

In addition to the direct use of these tools for business development, strengths and/or weaknesses in retail categories groups can point to the underlying structure of county markets.  For example, strength in eating and drink establishments, miscellaneous retail and to some extent gasoline and service stations often point to strong tourist economies.  Areas with strong sales in building materials can point to areas experiencing overall growth as measured through construction activities.  Thus, certain commodity groups can be used as indicators of particular sectors of the economy beyond the broad retail markets.

When interpreting these estimates of market strengths and weaknesses one must keep in mind the nature of the particular commodity group.  Some goods are often labeled "convenient" because of the frequency in purchasing patterns.  These goods, like milk and bread, gasoline, and hardware items, are purchased on such a regular basis that people will tend to make their purchases as close to their residence as possible.  People are usually unwilling to travel great distances to purchase convenient goods.  Hence, nearly every county has a grocery store, hardware store and gasoline station.  For these categories, one would generally expect the pull factor to be close to one indicating that county businesses are satisfying county demands.  Weak performance in these types of commodity groups generally point to opportunities while strengths may indicate a strong tourism sector.  Generally, those commodity groups with low population threshold estimates are considered convenient goods.

Conversely, larger ticket items that are purchased on a much less frequent basis, such as furniture and automobiles, people are often willing to travel great distances in pursuit of a "good deal" or just the right item.  Note that in casual observation, car dealership, appliance stores, and furniture stores tend to cluster together in larger urban markets.

County-level trade area analysis provides important background information to help understand the current competitive situation (at the county level).  In short, it describes a whether a county is capturing its fair-share of sales and sales tax receipts.  However, these tools are suggestive and should not be used as the sole means of understanding county economic trends.  For example, research suggests that for larger urban markets the tools of county-level trade area analysis may be inappropriate.  Similarly, the user must remember that market areas rarely follow the boundaries of a county (see Section 5 for help in defining your market area).  Finally, they do not provide sufficient detail to gauge market support for specific business expansion or development opportunities.

Other Local Economic Trends

Transit and Traffic Patterns

Public transit and traffic patterns provide a good indicator of the movement of people to and through your community.  Data on the number of public transit riders and vehicle traffic volume provide information on amount, time of and location of travel.  Activity generators in a community such as industries, colleges, convention centers and hospitals often are the major determinants in local travel.  Communities may also accommodate state and interstate highway travelers going through town.

Street and highway traffic volume provides an important indicator of travel to a downtown area.  Retailers typically seek locations on major arteries and often require minimum average daily traffic counts to survive.  More specifically, businesses such as gasoline stations, convenience stores and fast food restaurants are located based on traffic volume and the access to and visibility from high traffic streets and highways.  Subsequently, examining the counts aid in determining the feasibility of these types of businesses.  Conversely, while high traffic counts are desirable, extreme traffic congestion can be a deterrent to shoppers.  That is, high traffic may hinder visibility, parking and pedestrian friendliness. 

Accordingly, it is necessary to examine average daily traffic counts in the downtown area.  While some private data providers sell traffic counts, the data is also available through city and state sources.  In Wisconsin, the state Department of Transportation (DOT) publishes  Wisconsin Highway Traffic Volume Data.  It provides detailed average daily traffic counts for 30,000 locations throughout the State.  Furthermore, historical data is available from the Wisconsin DOT that is useful in examining traffic trends.

Local Housing Construction

Trends in real estate development including housing construction provide another indicator of the economic health of your community.  This data is typically available through city and state sources.

In addition, the U.S. Census Bureau reports construction statistics by place and by county on new privately owned residential housing units authorized by building permits.  Data items include number of buildings, units, and construction cost for monthly, new, privately owned residential building permits. These data are updated monthly.  Place level data is provided for individual permit-issuing jurisdictions that report monthly within a given state.  County level data are totals provided for each county in which every permit office is requested to report monthly.   The U.S. Census Bureau's Building Permits data is available at http://tier2.census.gov/Bldgprmt/index.html.

Local Economics and GIS

Similar to other areas of market analysis, GIS can enhance the examination of local economics.  Perhaps the most common GIS application in local economics is its use in exploring the geographical relationships among economic data.  Identifying these relationships will be important in fully understanding the regional economy and ultimately, how they affect local market analysis efforts.  Accordingly, the following discussion demonstrates two GIS applications for analyzing local economic data.

Economic Data and Geographical Relationships

Traditionally, economic data is examined using tables or charts.  The numbers or graphs are compared side-by-side to show comparisons among regions.  While these formats are useful, they fail to illustrate how the data is related geographically. The same problem arises when examining economic data over time.  Changes can be compared using a graph or table, but cannot be examined spatially. For instance, consider a table showing an economic data set by county.  Unless the viewer has intimate knowledge of each county's location, no inferences can be made.

GIS can be used to overcome this deficiency.  Depicting economic data on a map allows the viewer to examine possible patterns in the data important to subsequent market analysis. Are similar types of conditions clustered or are the dispersed?  How is one county performing compared to its neighbors?  These types of questions can be quickly answered using a map.

As an example, examine the maps in Exhibit 6.5.  These maps of Wisconsin counties show both the total personal income for 2000 and the personal income index of growth between 1990 and 2000.  The map on the left shows distinct areas of high and low total income, while the map on the right shows how areas are growing in comparison to one another.  As an example of the viewer can use these maps, consider the areas around Vilas County (in the north) and Milwaukee County (in the southeast)

Vilas County and its neighbors have a low total personal income.  Nonetheless, the index of growth shows that these areas are growing, with Vilas County growing faster than its surrounding counties.  In contrast, Milwaukee County and its surrounding counties have a large total personal income.   However, Milwaukee County has shown smaller growth while its neighbors continue to grow much faster.  These types of regional comparisons will show the relative economic health of the area and will be important in understanding the market in subsequent analysis steps.

Exhibit 6.5 - Maps showing regional total personal income variations

Mapping Regional Market Conditions

Other sections in this toolbox explain how to map market conditions in the local trade area.  However, methods for examining the greater regional market are needed as well. Mapping the earning mix by sector or location quotients may point to strengths or weaknesses in different economic sectors.  Mapping unemployment may show the labor pool potential available in the area.  Additionally, mapping retail surplus and leakage across a larger region may show the strength of the trade area in terms of wider regional competition.

As an example showing regional market conditions, consider the map in Exhibit 6.6.  This map shows the retail surplus and leakage by county around Fort Dodge, Iowa.  The red shaded counties show those areas with a retail surplus, while the blue colored counties show areas leaking retail dollars.  The map shows that Fort Dodge is located in a county with a strong retail surplus.  However, the surrounding counties around are likely leaking dollars into Fort Dodge.  As a result, the map shows the relative strength of the retail sector in the Fort Dodge area.  This strength may have implications for future retail expansion. Furthermore, this map can be used to identify possible competing communities that may be contributing retail dollars to the Fort Dodge area. 

Exhibit 6.6 - Map showing retail surplus and leakage around Fort Dodge, Iowa