Bikinis Benchmarking and Location Quotients

1. BikinisBenchmarking and Location Quotients Urban and Regional Economic Development September 25, 2006

2. Statistics are like a bikini – what they reveal is suggestive; what theyconceal is vital

3. Agenda • Firms vs. establishments • Non-governmental data • Combining data – extracting meaning • Access to work • Geography issues • Urban vs. Rural • Municipalities, Counties, MSAs, etc. • Benchmarking • LOCATION QUOTIENTS

4. What is a firm? • What is an establishment? • a single physical location at which business is conducted or where services or industrial operations are performed. • Establishment <> a company or enterprise, which may consist of one or many establishments. • All activities carried on at a location generally are grouped together and classified on the basis of the major reported activity, and all data for the establishment are included in that classification. • Establishments with paid employees include all locations with paid employees any time during the year. • Nonemployer establishments, provides the number of establishments without paid employees, mostly self-employed individuals. • Reporting Unit: sometimes when have multiple establishments that are small, may have one establishment that is actually doing the reporting – that’s the ‘Reporting Unit’ • Cautions about commercial or unofficial data….

5. Example: Firm/Establishment • Design – original firm • Viz – first a project, then a division, then a separate company • Group – holding company • 3 corporate entities • 1 or 2 establishments

6. Combining Data • Labor Force: • restricted to those more than 16 years old; doesn’t include military or prison population; etc. • Access to work • Jobs-to-the-labor force (jobs/LF) • why not Jobs-to-Population • Dependency • Resident Employees-to-Population • how many residents are working to support the population

7. Geography Issues • Urban • All territory, population, and housing units located within an urbanized area (UA) or an urban cluster (UC). • core census block groups or blocks that have a population density of at least 1,000 people per square mile and • surrounding census blocks that have an overall density of at least 500 people per square mile • In addition, under certain conditions, less densely settled territory may be part of each UA or UC. • Rural • All territory, population, and housing units located outside of UAs and UCs.

8. Urban / Rural / Suburban • Places, counties, metropolitan areas are often split between urban and rural territory.  • For example, St. Mary's County, MD, is a predominantly rural county which contains a substantial urban population.  • So what is a suburb? • Is it urban or rural or neither?

9. States and counties typically don’t change • District, municipal, metropolitan and regional boundaries change over time • www.ask.census.gov for information about geographic boundaries • Metropolitan areas • The New England exception: NECTAs & NECMAs • If doing historical or trend analysis, MSA definitions may change – make sure you’re talking about same consistent area over a specific time period • Different kinds of MSAs • MSAs (metropolitan statistical area); • CSA (combined statistical area) • Macropolitan Statistical Area • Micropolitan Statistical Area • FIPS State & County codes; also CBSA codes – both really helpful in ensuring that you’re pulling data from the same sources

10. Basic benchmarking can be helpful

11. Basic benchmarking can be helpful

16. Measures of Growth • Linear Measure of Growth • Compound Measure of Growth • Percent Change in Population Growth between 1990 and 2000 • 2000-1990 / 1990 • Average Annual Change • 2000-1990 / 1990; then that number divided by number of years (how many years?) • Compound Average Growth Rate (CAGR) • Compute annual change for every year and then take the average. More tedious computations. • If have smooth procession of growth, Avg Annual vs CAGR will be pretty close. But, if have major ‘dip’ or ‘growth’ in one particular year, will get more precise number calculating CAGR vs Avg Annual

17. Adjusting data • Ways to normalize data • Adjusting for inflation • How do you adjust numbers for inflation? National Science Foundation can adjust its numbers for inflation if you want or can give you it in CURRENT dollars; CONSTANT dollars is adjusted for inflation. • When doing something for wages – might want to adjust based on consumer price index (need to use a conversion factor).

18. Region Industry Total Nation Industry Total Location Quotients Interpretation Formula

19. Using LQs • The Export Flaw • Global production • Intermediate goods and end users • Assumption Approach • Local = Government, Banking (is this still true?), • Utilities • Retail (The Amazon effect) • Minimum Requirements Approach • The least a region needs to have • Can use location quotient to get a sense of how many specialization industries a region has • Specialization – • location quotients about 1.0 or 1.5 • High LQ may be difficult to increase employment • Diversification. • If all location quotients near or at a 1.0, will see the region mimicking the national economy.

20. LQ quirks • Sensitive to the size of the region and base • Sensitive to the level of industry

21. Interpreting ratios • Changes in Location Quotients – what does it mean? (compare LQs for Flint, Michigan and Albuquerque, New Mexico) • Changes in Ratios – what does it mean? • Different ways to measure growth over time – population, employment, income • Concept of multipliers • Different ways to calculate growth rate

22. Finding Key Industries High LQ Low LQ Low Growth High Growth

23. The Policy Map Brooks and Krugman Part 2