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

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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?

jpinkerton@marc.org

flenk@marc.org

kceconomy.com