bikinis benchmarking and location quotients urban and regional economic development september 25,...
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BikinisBenchmarking
and Location Quotients
Urban and Regional Economic Development
September 25, 2006
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
• 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….
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
MAYA Group
MAYA VizMAYA Design
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
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.
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?
• 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
Basic benchmarking can be helpfulAnnual Employment Growth, 1969-2000
-6.0%
-4.0%
-2.0%
0.0%
2.0%
4.0%
6.0%
1969
-197
0
1970
-197
1
1971
-197
2
1972
-197
3
1973
-197
4
1974
-197
5
1975
-197
6
1976
-197
7
1977
-197
8
1978
-197
9
1979
-198
0
1980
-198
1
1981
-198
2
1982
-198
3
1983
-198
4
1984
-198
5
1985
-198
6
1986
-198
7
1987
-198
8
1988
-198
9
1989
-199
0
1990
-199
1
1991
-199
2
1992
-199
3
1993
-199
4
1994
-199
5
1995
-199
6
1996
-199
7
1997
-199
8
1998
-199
9
1999
-200
0
Pittsburgh United States
Basic benchmarking can be helpfulEmployment Change, 1970-1993
0
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
San Diego Boise Tucson Fresno Memphis New Orleans Toledo Pittsburgh
The Scale Problem
-
20,000,000
40,000,000
60,000,000
80,000,000
100,000,000
120,000,000
140,000,000
160,000,000
180,000,000
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
United States United States Metropolitan Portion
Pennsylvania Metropolitan Portion Pittsburgh, PA (MSA)
Total Percent Change, Pittsburgh in Context, 1990-1994
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
United StatesMetropolitan Portion
PennsylvaniaMetropolitan Portion
PennsylvaniaNonmetropolitan
Portion
State College, PA(MSA)
Altoona, PA (MSA) Pittsburgh, PA (MSA)
Detail <> Clarification
-2.0%
-1.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
1990-1991
1991-1992
1992-1993
1993-1994
1994-1995
1995-1996
1996-1997
1997-1998
1998-1999
1999-2000
2000-2001
2001-2002
2002-2003
2003-2004
An
nya
l Per
cen
t C
han
ge
United States Metropolitan Portion
Pennsylvania Metropolitan Portion
Pennsylvania Nonmetropolitan Portion
State College, PA (MSA)
Altoona, PA (MSA)
Pittsburgh, PA (MSA)
Pittsburgh in Context - Different Averages
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
Avg Pct Change 1 Avg Pct Change 2 AAGR
United States Metropolitan Portion
Pennsylvania Metropolitan Portion
Pennsylvania Nonmetropolitan Portion
State College, PA (MSA)
Altoona, PA (MSA)
Pittsburgh, PA (MSA)
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
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).
Location Quotients
Loc
atio
n Q
uot
ien
t
Employment Growth
Important industries that may require attention
High
High
Low
Low
Important growth industries
Industries of little promise to local economy
Potential emerging industries
Total
Total
Industry
Industry
Region
Natio
n
Formula Interpretation
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.
LQ quirks
• Sensitive to the size of the region and base• Sensitive to the level of industry
Industry Georgia -- Statewide
Atlanta-, GA MSA
NAICS 311 Food manufacturing 1.47 0.88
NAICS 31193 Flavoring syrup and concentrate manufacturing
ND ND
NAICS 3121 Beverage manufacturing 0.65 ND
NAICS 312111 Soft drink manufacturing 0.43 0.57
NAICS 484 Truck transportation 1.12
NAICS 484122 General freight trucking, long-distance
2.43
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
Finding Key Industries
Military (small & not growing industry)
StLGov (large, Ind. Decline – but does that matter, is this desirable)
Farm (small, ind. decline
Mfg (small, Ind decline)
Svc (lg, growth industry)
HighLQ
LowLQ
Low Growth High Growth