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The Power of Data in
Decision-Making
APRIL 7, 2015
Using Data to Make Smarter Decisions
Key Data Sources
What are your data issues?
The Power of Data in
Decision-Making: Outline
How Not to Use Data
Keep It Simple
Sometimes, you really don’t need a
chart
“There’s a 50%
chance of rain
on Saturday.”
“Therefore, there’s a 100%
chance of rain this weekend!”
“There’s also a
50% chance
of rain on
Sunday.”
“If you can’t explain it simply, you don’t understand it well enough”
Albert Einstein
Regional Prosperity
Prosperity at a Crossroads Targeting Drivers of Economic Growth for Greater Kansas City
State of the Regional Economy The region outperformed the nation in output, jobs and wages in the 1990s, but that trend has reversed.
Growth in Output, Jobs and Wages
Sources: REMI and Moody’s Analytics
Traded Sectors
Growth in Employment, 1990-2011
Source: REMI
Only professional services and manufacturing outperformed the nation in employment growth.
Traded Sectors
Growth in Economic Output, 1990-2011
Source: REMI
Only professional services and manufacturing outperformed the nation in economic output.
Traded Sectors Only one sector, Professional, Scientific and Technical Services is firing on all cylinders.
Regional Economic Competitiveness Trends by Sector
Source: REMI
0% 10% 20% 30% 40% 50%
Las Vegas
Orlando
San Antonio
New Orleans
Louisville
Virginia Beach
Jacksonville
Memphis
Tampa
San Diego
Charlotte
Indianapolis
Milwaukee
Nashville
Pittsburgh
Kansas City
Oklahoma City
Cleveland
St. Louis
Providence
Portland
Columbus
Sacramento
Richmond
Cincinnati
Minneapolis
Denver
Baltimore
Austin
Seattle
San Jose
Percent of Jobs that are “Good Jobs”
Workforce
Educational Attainment
60%
47.6%
-
100,000
200,000
300,000
400,000
500,000
600,000
700,000
800,000
900,000
1,000,000
Associates's Degree + Projections
Needed Current Trend
Source: U.S. Census Bureau , MARC Projections
Educational Attainment (Population 25 and Over)
8.8%
27.0%
23.1%
7.7%
21.4%
12.1%
KC
13.6%
28.0%
21.3%
8.0%
18.2%
10.9%
U.S.
Less than High School
High School (includes
GED)
Some College, no
degree
Associate's degree
Bachelor's degree
Graduate degree
Source: U.S. Census Bureau , American Community
Survey
Workforce Data
Workforce Data
IT Jobs by
Occupati
on
5
11
26
141
161
201
623
650
719
1,137
1,188
1,370
1,690
1,734
1,769
2,474
2,922
4,421
4,962
5,048
5,528
6,257
- 1,000 2,000 3,000 4,000 5,000 6,000 7,000
Mathematical Technicians
Mathematical Science Occupations, All…
Mathematicians
Computer Hardware Engineers
Computer and Information Research…
Statisticians
Actuaries
Operations Research Analysts
Information Security Analysts
Computer, Automated Teller, and Office…
Database Administrators
Computer Network Support Specialists
Computer Occupations, All Other
Computer Network Architects
Web Developers
Software Developers, Systems Software
Computer and Information Systems…
Computer Programmers
Network and Computer Systems…
Computer Systems Analysts
Software Developers, Applications
Computer User Support Specialists
2015 Jobs
Source: EMSI
The opportunities created
by an aging population
-100,000-50,000 0 50,000 100,000
Under age 5
10-14
20-24
30-34
40-44
50-54
60-64
70-74
80-84
2030
M
F
-100,000-50,000 0 50,000 100,000
Under age 5
10-14
20-24
30-34
40-44
50-54
60-64
70-74
80-84
2010
M
F
-100,000-50,000 0 50,000 100,000
Under age 5
10-14
20-24
30-34
40-44
50-54
60-64
70-74
80-84
1990
M
F
Metropolitan Kansas
City’s population is
becoming more and
more a region of all ages
– where all groups are
represented relatively
equally
As its population ages, 58 percent of metropolitan
Kansas City’s population growth over the next two
decades will come from seniors.
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
0-4 5-9 10-14 15-19 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79 80-84 85+
2010-2030 Population Change by Age Group
First home
Largest home
Same home or downsize
Older households spend about as
much as younger households, on
average.
$51,100
$30,373
$48,087
$58,784 $60,524 $55,892
$46,757
$34,382
$-
$10,000
$20,000
$30,000
$40,000
$50,000
$60,000
$70,000
Total Under 25 25-34 35-44 45-54 55-64 65-74 over 75
Average Annual Expenditures by Age Group 2013
With each generation we create
greater human capital. We need
to retain it.
14%
38% 38%
33%
26%
0%
5%
10%
15%
20%
25%
30%
35%
40%
18-24 25-34 35-44 45-64 65+
Percent with a Bachelor's Degree or Above, 2013
Net Migration Inflow 2002-2011
On net, the region is losing population to the
sunbelt and other retirement locations
The region is currently more
attractive to children and younger
adults than older adults
(2,000)
(1,500)
(1,000)
(500)
-
500
1,000
1,500
1 to 4
years
5 to
17 years
18
and 19
years
20 to
24 years
25 to
29 years
30 to
34 years
35 to
39 years
40 to
44 years
45 to
49 years
50 to
54 years
55 to
59 years
60 to
64 years
65 to
69 years
70 to
74 years
75
years
and
over
Average Annual Net Domestic Migration by Age, 2007-2013
(7,807)
More seniors are moving out of the region
since the Great Recession, but the average
has been about 6,000 per year
-
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
2007 2008 2009 2010 2011 2012 2013
Gross Outmigration of the Senior Population, 2007-13
Retaining more seniors produces a cumulative effect on the
region’s economy, resulting in nearly 7,000 more people
and 2,600 more jobs if continued for 10 years.
2.553
6.892
0.0
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Tho
usa
nd
s
Increase in Total Employment
Total Employment Population
More people and jobs in the region raise annual incomes
by nearly half a billion dollars, and the value of goods and
services produced locally by nearly one-quarter billion.
$[VALUE]
$[VALUE]
0
50
100
150
200
250
300
350
400
450
500
2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023
Mill
ion
s
Increase in GDP and Income
Gross Domestic Product Real Disposable Personal Income
Residential and
workplace geographic
patterns
2011 Commuting Pattern
Where Lee’s Summit Residents
Work
Where Lee’s Summit Workers Live
Where Do New Residents Live?
Poverty
Distribution
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hospitals & medical facilities
®v <all other values>
Type
!( Safety Net Clinics
2010 Census Tracts
Poverty Rate
Less than 7.3%
7.3% to 15.1%
15.1% to 26%
26% to 41%
41% or Greater
Hazard Mitigation
Community Assessment of
Risk
Tornadoes Flooding
Severe
Thunderstorm
s
Severe
Winter
Weather
Cass 2.17 1.80 2.40 2.00
Clay 2.86 2.00 2.86 2.57
Jackson 3.00 1.83 2.83 2.67
Platte 2.44 2.06 2.31 2.25
Ray 3.00 2.00 3.00 2.50
Highest Risk
2nd Highest Risk
3rd Highest Risk
Health Insurance
Health Insurance Coverage
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
Total: Under 6 6 to 17 18 to 24 25 to 34 35 to 44 45 to 54 55 to 64
KC MSA - Percent Uninsured By Age
KC MSA
Health Insurance Coverage
23.9%
11.3%
16.9%
40.9% 41.4%
34.6%
25.0%
16.4%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
Total: Under 6 6 to 17 18 to 24 25 to 34 35 to 44 45 to 54 55 to 64
Percent Uninsured By Age
Wyandotte KC MSA
Health Insurance Coverage
7.8% 2.9% 3.1% 11.4%
18.4% 12.5%
7.7% 5.2% 0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
45.0%
Total: Under 6 6 to 17 18 to 24 25 to 34 35 to 44 45 to 54 55 to 64
Percent Uninsured By Age
Platte KC MSA
Health Insurance Coverage
9.3%
33.9%
19.9%
16.0% 15.1%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
40.0%
White, NH Hispanic Black alone Asian alone All other
Percent Uninsured by Race/Ethnicity
Health Insurance Coverage
12.9%
25.6%
20.9%
13.0%
6.5% 4.1%
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
30.0%
35.0%
Total Under
$25,000
$25,000 to
$49,999
$50,000 to
$74,999
$75,000 to
$99,999
$100,000
and over
Percent Uninsured by Income
Health Insurance Coverage
0%
10%
20%
30%
40%
50%
60%
Populations most likely to be uninsured
Percent uninsured MSA Average
Impact of the ACA on
Health Insurance Coverage
0
5
10
15
20
25
30
Percent Uninsured By County
2013 2014
Impact of the ACA on
Health Insurance Coverage
-8
-6
-5
-4
-3 -3
-2 -2 -2
-1
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
Pe
rce
nta
ge
Po
int
Ch
an
ge
Change in Percent Uninsured, 2013-14, by County
Potential Impact of the ACA
If Medicaid were expanded
-14
-12
-10
-8
-6
-4
-2
0
Estimated Impact of Medicaid Expansion on Percent
Insured by County, 2013-14
Actual w/Medicaid
Other important uses of data
to affect decision-making
The economics of sustainable development
Economic impact of creative industries and
occupations
Fair housing and equity analysis
Understanding the determinants of poverty and
prioritizing policy interventions.
Human Capital
Human Capital Gap – Educational Attainment
Source: US Census Bureau, American Community Survey
HOW? WHY?
PLANNED &
ONGOING
DEVELOPMENT
ALONG
STREETCAR
STARTER LINE
IMPACTS TO-DATE $256m, 860 units - directly attributable $311m, 817 units - impact decision
$214m, 574 units- benefit
WageIncomeBalance
Income_Balance
-1.00 to -0.20
-0.20 to -0.15
-0.15 to -0.10
-0.10 to -0.05
-0.05 to 0.00
0.0 to 0.05
0.05 to 0.10
0.10 to 0.15
0.15 to 0.20
0.20 to 1.00
Wage-Income Balance
Well Paying Jobs
/ Low Income Households
Low Wage Jobs
/ High Income Households
Simulation 1: The poor move
to a much wealthier area
An African-American single mom currently lives in
the KCMO’s urban core PUMA (Missouri River to
roughly 35th Street) making $23,616
She moves to a moderately wealthy suburban
PUMA (Olathe, KS). What change might she
expect in her income?
Model estimates that her income would increase
to $26,634, a 13 percent increase
Note that the suburbanizing poor are already
trying to implement this solution without any policy
support
Simulation 2: A place-
based approach
Predicting various aspects of place wealth:
Median housing value
Jobs
Transit ridership
Road density
Single-parent families
Life expectancy
Educational attainment
Percentage employed
Home ownership percentage
Simulation 2: A place-
based approach What if we were able to increase each of these 1 percent in
the favorable direction.
Fairly comprehensive intervention
What is the impact on individual income?
Trace impact on household wealth
Home value increases by $661, on average
Impact on individual earnings of someone earning $22,000 per
year:
$77, or 0.35%.
Simulation 3: Impact of HS
graduation
Assume baseline is a minimum wage job earning
about $15,600 per year
Model estimates an average 21 percent increase
in earned income in the first year after
graduation.
To $18,825, a $3,225 increase.
Combination of three effects
Years of education
Getting a degree
Improvement to the set of occupational
opportunities
Simulation 3: Impact of
College graduation
Assume same baseline education and earnings.
Model estimates a 97 percent increase in earned income in the first year after graduation.
To $30,805, a $15,205 increase.
There is also a household effect
Just like occupational opportunities improve, so do household opportunities
Likelihood of becoming a married-couple/partner household increases
Expected value of this effect averages an additional $30,000 - $60,000 across all household types.
Lessons Learned
If at first you don’t succeed, try, try again.
“Do, or do not. There is no try.”
Confusion is the first step to wisdom
Data creates opportunities for change, but does
not cause change
Stories, not data.
Confusion is also the first step to change
Having a narrative at the moment of policy
confusion is essential to shift the conversation
Most of the research must have already been
done.
“If you can’t explain it simply, you don’t understand it well enough”
Albert Einstein
Data Sources
Common Data Sources
Census
American Fact Finder
Community Facts
Detailed (Tracts – Map)
On the Map
LODES
Bureau of Labor Statistics
Employment (Industry)
Unemployment Rate
Wage
Community Commons
www. Census.gov
www. Census.gov
www. Census.gov
www. Census.gov
www. Census.gov
www. Census.gov
www. Census.gov
www. Census.gov
www. Census.gov
County
Place (City)
Census Tract
Block Group
www. Census.gov
www. Census.gov
www. Census.gov
www.bls.gov
www.bls.gov
www.bls.gov
www.bls.gov
www.bls.gov
www.bls.gov
www.communitycommons.or
g
What Data Do You Use?
What Are Your Data Issues?
kceconomy.com
