Economic Inequality and Health Timothy S. Sullivan, Ph.D. Department of Economics and Finance Southern Illinois University Edwardsville tsulliv

Economic Inequality and Health

Timothy S. Sullivan, Ph.D.Department of Economics and Finance

Southern Illinois University Edwardsville

www.siue.edu/~tsulliv


Measuring Economic Inequality Difficulties in Measuring Inequality Myths Surrounding Inequality The Connection Between Economic

Well-being and Health




Measuring Inequality

Quintiles Lorenz Curves & The Gini Coefficient

Quintiles

Second Quintile

$16K-$31K

First Quintile

<$16KThird Quintile

$31K-$48K

Fourth Quintile

$48K-$75K

Fifth Quintile

>$75K

1998 values for Households, from http://www.census.gov/hhes/income/histinc/rdi06.html, accessed 10/21/00

Income Distribution 1970-1998

204.1 4.3 3.9 3.6

20

10.8 10.3 9 9

20

17.4 16.9 15.9 15

20

24.5 24.9 24 23.2

20

43.3 43.7 46.6 49.2

0%

20%

40%

60%

80%

100%

Equality 1970 1980 1990 1998

Source: U.S. Census Bureau, reported in The Wall Street Journal, October 1, 1999, B1.

Building a Lorenz Curve

0

20

40

60

80

100

0 20 40 60 80 100

Equality19701998

Source: U.S. Census Bureau, reported in The Wall Street Journal, October 1, 1999, B1.

Gini Coefficient

Portion of lower triangle not covered by Lorenz Curve

Perfect Equality : Gini=0

One Household Earns all Income: Gini=1

US Gini Coefficient: 1979-1998

0.403 0.446

0

0.2

0.4

0.6

0.8

1

1979 1981 1983 1985 1987 1989 1991 1993 1995 1997

Source: US Bureau of Census, available at http://www.census.gov/hhes/income/histinc/rdi05.html

International Gini Coefficients

South Africa (1993):0.623 Mexico (1992): 0.503 US (1991): 0.379 China (1992): 0.378 Sweden (1992): 0.324 Canada (1992): 0.277

Source: The World Bank, available at http://www.worldbank.org/research/growth/dddeisqu.htm



well-being and Health

Difficulties Measuring Inequality

What to measure?– Net Income– Total Compensation– Wealth– Consumption

“Feast or Famine” workers Household Structure

What to Measure?

Net Income?– Welfare benefits?– EITC?– After taxes? Which taxes?

Total Compensation?– Medical Plans?– 401K Contributions by Employer?

What to Measure?

Wealth?– House?– Pension Plan?– Family Farm?

Consumption?– Reduces Gini (1994) from 0.432 to 0.317

Source: US Bureau of the Census, reported by Litvan, Laura M., “Do Income Data Give Americans Wrong Ideas About Inequality,” Investor’s Business Daily, July 23, 1996.

Feast or Famine Workers

0

10

20

30

40

50

60

70

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6

Worker #1 Worker #2

Country A

Gini=.225


0

10

20

30

40

50

60

70

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6

Worker #1 Worker #2

Country BGini=0.225 & 0.420


Adding annual variability to workers’ salaries will typically increase annual measures of inequality, even when there is no change in long-run inequality.

Variability may be due to more bonus & commission-based pay or more workers in cyclical industries.

Changing Household Structure

If inequality is measured at the household level, composition of household is important:– 1996: 53.1% married couple– 1969: 70.4% married couple

Source: Author’s calculations based upon McNeil, Jack, Changes in Median Household Income: 1969-1996, US Bureau of the Census, P23-196, Table 3, available at

http://www.census.gov/hhes/income/mednhhld/p23text.html




Myths Surrounding Inequality

The Rich are Getting Richer ……. The rich are:

– Born into money– Extravagant– Lucky

More inequality means more poor people doing without basics

Myth #1: “The Rich are Getting Richer, and the Poor are getting Poorer”

0

20

40

60

80

100

High Score Low Score

ECON 327 Quiz Scores

Quiz1 Quiz2 Quiz3

The statement implies that people remain in their category

1993-94:– 24% of poor became

non-poor– 3% of non-poor

became poorSource: Author’s calculations based upon Dynamics of Economic Well-Being: Poverty 1993-1994, US Bureau of the Census, available at http://www.census.gov/hhes/poverty/povdynam/pov93t5.html

Myth #2: “The rich are …..”

Born into money– 80% are first-generation– Most attended public school

Lazy– average 45-55 hours per week– Self-employed in a practical business

Extravagant– 40% drive cars over 3 years old– 81% clip coupons

Source: Stanley, Thomas J., and William D. Danko, The Millionaire Next Door, reported by Anita Sharpe in The Wall Street Journal, November 12, 1996, pg B1.

Myth #3: “More inequality means more poor people doing without the basics”

Poor Households Own:– Color Television (93%)– VCR (75%)– Automobile (70%)

• 27% own two, or more, cars

– Air Conditioning (67%)– Microwave (64%)– Home (41%)

Source: Rector, Robert, “America Has the World’s Richest Poor People,” The Wall Street Journal, September 24, 1998.




Components of Health

Inputs Health Insurance Risk Factors Outcomes

Inputs: Food

In 1994, poor children were eight times more likely than non-poor children to report that there was sometimes “not enough to eat.”

In general, the nutrient intake of poor children is similar to non-poor children.

Sources: America’s Children: Key National Indicators of Well-Being, Federal Interagency Forum on Child and Family Statistics, 1997, pg. 16.

US Department of Agriculture, Agricultural Research Service, February 1999, Data Tables: Food and Nutrient Intakes by Income, 1994-96, http://www.barc.usda.gov/bhnrc/foodsurvey/pdf/Income.pdf.

Input: No Physician Contact (children under 6)

0

2

4

6

8

10

12

Per

cent

White Black Hispanic

Poor Near Poor Middle/High

Source: Pamuk E., Makue D, Heck K, Reuben C, Lochner K. Socioeconomic Status and Health Chartbook, National Center for Health Statisics, Hyattsville, MD, 1998, pg. 151.

Input: No Physician Contact (children under 18: 1999-2000)

0

5

10

15

20

25

30

35

Per

cent


Poor Near Poor Nonpoor

Source: Health, United States, 2002; with Chartbook on Trends in the Health of Americans, National Center for Health Statistics, Hyattsville, MD, 2002, pg. 224.

Input: No Physician Contact (children under 18: 1999-2000)

0

5

10

15

20

25

30

35

40

Per

cent

Poor Near poor Nonpoor

Insured Uninsured


Input: No Physician Contact (men 18-64)

0

5

10

15

20

25

Per

cent




Input: No Physician Contact (women 18-64)

0

2

4

6

8

10

12

14

16

Per

cent




Input: Mammogram w/in 2 yrs(women 50+)

0

10

20

30

40

50

60

70

80

Per

cent




Input: Dental Visit w/in 1 year(children 2-17: 2000)

010

20304050

607080

90

Per

cent




Input: Dental Visit w/in 1 year(adults 18-64: 2000)

0

10

20

30

40

50

60

70

80

Per

cent




Input: No Physical Leisure Activity (adults 18+)

0

10

20

30

40

50

60

Per

cent

Men Women

Below Poverty 1-2 x Poverty 2-4 x Poverty 4 x Povert

Source: Schoenbron, Charlotte A., Patricia Barnes, Leisure-Time Physical Activity Among Adults: United States, 1997-98, Advance data from vital and health statistics; no. 325, NCHS, April, 2002 .

Inputs: ImmunizationsBelow

PovertyAbove

Poverty

Combined

(4:3:1:3)

71% 78%

DTP 76% 84%

Polio 87% 90%

Hib 90% 95%


Inputs: Uncontrolled Hypertension

(males 20+)

0

5

10

15

20

25

30

35

Per

cent




Inputs: Uncontrolled Hypertension

(females 20+)

0

5

10

15

20

25

30

Per

cent




No Health Insurance(children under 18)

0

5

10

15

20

25

30

35

Per

cent


Poor Near Poor Middle High


No Health Insurance(men 18-64)

0

10

20

30

40

50

60

Per

cent




No Health Insurance(women 18-64)

05

101520

25303540

45

Per

cent




Risk Factor: Cigarette Smoking (Adolescents 12-17)

0

5

10

15

20

25

Per

cent

Boys Girls



Risk Factor: Cigarette Smoking (adults 18+)

0

5

10

15

20

25

30

35

40

Per

cent

Men Women



Risk Factor: Sedentary Lifestyle (adolescents 12-17)

0

5

10

15

20

25

30

Per

cent

Boys Girls



Risk Factor: Overweight(adolescents 12-17)

02468

101214161820

Per

cent




Risk Factor: Overweight(men 20+)

05

10152025

303540

45

Per

cent




Risk Factor: Overweight(female 20+)

0

10

20

30

40

50

60

Per

cent




Outcome: Elevated Blood Lead(children 1-5)

0

5

10

15

20

25

Per

cent




Outcome: Elevated Blood Lead(adults 18+)

02468

101214161820

Per

cent




Outcome: Asthma Hospitalization (children 1-14)

0

1

2

3

4

5

6

7

8

Per

cent

White Black

<$20K $20K-$30K $30K-$40K >$40K


Outcome: Life Expectancy (45-year-old male)

0

5

10

15

20

25

30

35

Yea

rs

White Black

<$10K $10K-15K $15K-$25K >$25K


Outcome: Life Expectancy (65-year-old male)

0

246

81012

141618

Yea

rs

White Black

<$10K $10K-15K $15K-$25K >$25K


Outcome: Life Expectancy (45-year-old female)

0

5

10

15

20

25

30

35

40

Yea

rs

White Black

<$10K $10K-15K $15K-$25K >$25K


Outcome: Life Expectancy (65-year-old female)

0

5

10

15

20

25

Yea

rs

White Black

<$10K $10K-15K $15K-$25K >$25K


Outcome: Heart Disease Death Rate (25-64-year-old male)

0

50

100

150

200

250

300

350

400

White Black

<$10K $10K-15K >$15


Outcome: Heart Disease Death Rate (65+-year-old male)

0

50

100

150

200

250

300

350

400

White Black

<$10K $10K-15K >$15


Outcome: Heart Disease Death Rate (25-64-year-old female)

0

50

100

150

200

White Black

<$10K $10K-15K >$15


Outcome: Heart Disease Death Rate (65+-year-old female)

0

200

400

600

800

1000

1200

1400

1600

White Black

<$10K $10K-15K >$15


Outcome: Lung Cancer Death Rate (25-64-year-old)

0

20

40

60

80

100

Men Women

<$10K $10K-15K $15K-$25K >$25K


Outcome: Lung Cancer Death Rate (65+-year-old)

0

100

200

300

400

500

600

Men Women

<$10K $10K-15K $15K-$25K >$25K


Outcome: Diabetes Death Rate (45+-year-old)

0

10

20

30

40

50

60

Men Women

<$10K $10K-15K $15K-$25K >$25K


Outcome: Fair or Poor Health(all persons 2000)

0

5

10

15

20

25

30

Per

cent




Outcome: Fair or Poor Health(males 18 or older)

0

5

10

15

20

25

30

35

40

Per

cent




Outcome: Fair or Poor Health(females 18 or older)

0

5

10

15

20

25

30

35

40

Per

cent




Outcomes: Activity Limitation: All Persons (2000)

0

5

10

15

20

25

30

Per

cent




Outcome: Activity Limitation (adults 18-64 years old)

0

5

10

15

20

25

30

35

40

Per

cent




Outcomes: Activity Limitation: Children under 18

01

2345

678

9

Per

cent




Outcome: Difficulty with Daily tasks

(70+-year-old)

05

1015202530354045

Per

cen

t

Men Women



Summary

There are strong correlations between income and health inputs & outcomes

Correlation isn’t causation!– Reverse Causation?– Intervening variables (risk factors)– Common response

Thank You Very Much!

Documents

Economic Inequality and Health Timothy S. Sullivan, Ph.D. Department of Economics and Finance Southern Illinois University Edwardsville tsulliv