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Florida State University 
Department of Urban and Regional Planning
Planning Methods III: Forecasting 





Topic
Summary
LQ Calculation
LQ
Interpretation
Calculating
Basic
Employment
A LQ Caveat
on Geography
Examples
Pairing the LQ
and Assumption
Techniques
Key Concepts
Lessons to
be Learned
Discussion
Questions
References
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LOCATION QUOTIENT TECHNIQUE

Topic Summary

The Location Quotient Technique is the most commonly utilized economic base analysis method. It was developed in part to offer a slightly more complex model to the variety of analytical tools available to economic base analysts. This technique compares the local economy to a reference economy, in the process attempting to identify specializations in the local economy. The location quotient technique is based upon a calculated ratio between the local economy and the economy of some reference unit. This ratio, called an industry "location quotient" gives this technique its name.
Unlike the Assumption Technique, the Location Quotient Technique does not assume that ALL employment in each industry is Basic or Non-Basic. Instead, location quotients are calculated for all industries to determine whether or not the local economy has a greater share of each industry than expected when compared to a reference economy. If an industry has a greater share than expected of a given industry, then that "extra" industry employment is assumed to be Basic because those jobs are above what a local economy should have to serve local needs.
For example, suppose a local economy has 5% of its workforce in computer manufacturing and the national economy has only 0.05% of its workforce in computer manufacturing. This technique assumes that the local economy would have that same percentage of its workers in the computer manufacturing industry to serve its local needs for computers. Any employment over and above the expected percentage (in this case 0.05%) is therefore considered to consist of basic sector jobs because these workers are assumed to be exporting their goods and services to non-local areas. If the percentages had been identical or if the local percentage had been lees than the reference percentage, then the analyst would conclude that the local area has no basic sector employment for that industry as the area can only, at best, meet their local demand and not export these goods and services.

Location Quotient Calculation

To calculate any location quotient the following formula is applied. Note that in this formula we are comparing the Regional Economy (often a county) to the National Economy. Location quotients may also be calculated that compare the county to a state.
Location Quotient=
Regional Employment in
Industry I in Year T
/
National Employment in
Industry I in Year T
Total Regional Employment
in Year T
Total National Employment
in Year T
Examining this formula more closely, we see that to allocate employment to the basic and non-basic sectors, location quotients are calculated for each industry. Simply stated, the location quotient method compares Local Employment to National Employment. The LQ provides evidence for the existence of basic employment in a given industry.

Interpreting Calculated Location Quotients

Interpreting the Location Quotient is very simple. Only three general outcomes are possible when calculating location quotients. These outcomes are as follows:
LQ < 1.0
LQ = 1.0
LQ > 1.0
LQ < 1.0 = All Employment is Non-Basic
A LQ that is less than zero suggests that local employment is less than was expected for a given industry. Therefore, that industry is not even meeting local demand for a given good or service. Therefore all of this employment is considered non-basic by definition.
A LQ = 1.0 = All Employment is Non-Basic
A LQ that is equal to zero suggests that the local employment is exactly sufficient to meet the local demand for a given good or service. Therefore, all of this employment is also considered non-basic because none of these goods or services are exported to non-local areas.
A LQ > 1.0 = Some Employment is Basic
A LQ that is greater than zero provides evidence of basic employment for a given industry. When an LQ > 1.0, the analyst concludes that local employment is greater than expected and it is therefore assumed that this "extra" employment is basic. These extra jobs then must export their goods and services to non-local areas which, by definition, makes them Basic sector employment.

Calculating the Level of Basic Employment

When the LQ is calculated to be greater than 1.0, it has been determined that some of that industry's employment is Basic. However, it is must be emphasized that a LQ > 1.0 does not mean that all that industry's employment is basic in nature. Recall that it is assumed that any employment "below" an LQ of 1.0 is Non-Basic; those jobs serve local demand. Only those jobs over and above what was expected for the region can be identified as Basic sector jobs.
Because of the assumptions of the Location Quotient approach, a second formula must be applied to determine the number of Basic sector jobs when the LQ is greater than 1.0. This formula is as follows:
Basic Sector
Employment
=
Regional
Employment
Industry I
-
Total
Regional Employment
X
National
Employment
Industry I
National
Employment
Industry I
Total
National
Employment

Examples

For an example of the Location Quotient technique applied to a single industrial sector, visit the King County-WA State LQ Example Page. To see the technique applied with the United States as a comparison region, visit the King County-US LQ Example page.

An Important Caveat: Carefully Choose Your Geographic Units

As with any of the Economic Base Methods, the choice of your data and, more importantly, your comparison area can greatly affect your results. As seen in the LQ example, the use of Washington State may compromise our Industry Code 37 Basic sector employment results. Like King County, it is likely that the entire State of Washington has a greater than average number of Transportation Equipment manufacturing jobs, mainly because of Boeing's existence in the State.
This greater than expected Transportation Manufacturing Employment in the State affects the calculation of the Location Quotient, which then has a direct effect upon the calculated number of Basic sector jobs for this industry. In short, we may have underestimated the number of Basic sector jobs for this industry by using the State of Washington as our reference region; the large number of Transportation Manufacturing jobs in King County does not appear to be that unique when compared to the State as a whole.

Pairing the Location Quotient and Assumption Techniques

It is also important to note that the Location Quotient Technique is often paired with the Assumption Technique to provide a more complete set of results. Some industries can clearly and correctly be identified as Basic and other can be identified as Non-Basic.
For example, SIC Code 70 (Hotels and Lodging) is generally assumed to be Basic sector employment, regardless of its calculated LQ, because this industry, by definition, largely serves a non-local demand. Similarly, Local Government employment is always assumed to be Non-basic because these jobs, again by definition,serve local demand. By pairing the assumptions of these different techniques a more reasonable and accurate assessment of the local economy is possible.

Key Concepts

         The underlying theory behind the location quotient
         The location quotient formula and the possible results of the LQ calculation
         Interpretation of calculated LQ's for each industry; Assigning employment to the Basic and Non-Basic sectors
         The calculation of Basic sector employment by industry
         Limitations of the Location Quotient method, especially in the choice of your reference region
         The use of the Assumption Technique and the Location Technique in tandem
         Possble refinements to the Location Quotient technique (from Klosterman reading)

Lessons to be Learned

         "The location quotient approach estimates the basic employment in each industry by relating an industry's local employment share to its national employment share." (Klostermann, p. 149)
         Planners often rely upon comparisons between a smaller local unit (like a county) and a larger geographic unit (like a State or the entire United States) to gain a better understanding of local conditions. This technique is very useful because it allows the planner to identify and interpret differences between local conditions and regional/national conditions. In our example here, the location quotient technique allows the local planner to identify the strengths (the specializations) within the local economy.
         To translate the comparison between a local unit and a larger unit, a simple ratio can be calculated. This can very quickly an easily summarize the local condition. The specific industry location quotients do this for the local economy.
         Because the "real world" can be very messy and distinct boundaries between regions, industries, and employment are difficult to draw, a combination of methods or approaches is often necessary. Therefore, a good planner is one that is willing to combine and utilize the different tools and methods at their disposal.

Discussion Questions

         What are the key assumptions of this technique?
         When a Location Quotient for an industry is determined to be greater than 1.0, is all of that industry's employment assumed to be Basic? Why or why not?
         Why is it often best to pair the Assumption and Location Quotient Techniques?
         Give two examples of industry's (other than Hotels and Government) that are best assessed by the Assumption Technique, regardless of their calculated Location Quotients.
         What refinements to the LQ technique does Klosterman indicate might be used by analysts? Why does he suggest that these refinements are necessary?

References

Isserman, Andrew M. 1977. "The Location Quotient Approach for Estimating Regional Economic Impacts." Journal of the American Institute of Planners 43: 33-41.
Klosterman, Richard E. 1990. Community and Analysis Planning Techniques. Rowmand and Littlefield Publishers, Inc. Savage, Maryland. See Chapter 10.
Klosterman, Richard E., Richard K. Brail, and Earl G. Bossard. 1993. Spreadsheet Models for Urban and Regional Analysis.

 

 

 

 

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