Jeffrey E. FrankM.S. Candidate, Community Development and Applied Economics

Certificate of Ecological EconomicsAdvisor: Jane Kolodinsky, Ph.D.

March 24, 2011

Experiments on Subjective Preference:

Toward a New Methodology

and

Applications of Subjective Income

Outline

Chapter 1: Continuum Happiness Surveys• Introduction: Why Happiness?• Methodology

• Research Objectives• Burlington Happiness Survey• Surveying for Elasticity

• Results• Elasticities• Loss Aversion vs. Gain Adoration• Lorenz Curves

Chapter 2: On Subjectivity: Experiments and Applications

Chapter 3:• Introduction: Middle Class • Methods and Data• Do Middle Class Attitudes Differ?• Results

“BTV Ranked Happiest Small-City In The Country: Gallup Finds Burlington Among The

Happiest, Healthiest”- March 17, 2011

(Terminology: Happiness = Well-Being, Subjective Well-Being)

• Why Happiness?

• Because the traditional assumption that people are income-based profit-seeking agents in the economy has proven itself incomplete.

• Because profit and income are not innate human directives, but the pursuit of our happiness is.

R 2 = 0.14

10

20

30

40

50

60

70

80

90

100

0 2,000 4,000 6,000 8,000 10,000 12,000 14,000 16,000 18,000 20,000

GDP per capita (1998 PPP)

% a

bove

neu

tral

on

life

satis

fact

ion

ARGENTINACOSTA RICA

URUGUAY

MEXICO

BRAZIL

COLOMBIA

NICARAGUA

BOLIVIA ECUADOR

PERU

PARAGUAY

EL SALVADOR CHILE

GUATEMALAHONDURAS

PANAMA

VENEZUELA

U.S.A.

PORTUGAL

RUSSIA

BULGARIA

CHINANIGERIA

INDIABANGLADESH

ROMANIAS.AFRICA

HUNGARY

IRELAND

NEW ZEALAND

FRANCE

JAPAN

SWEDEN

NETHERLANDS

SWITZERLAND

CANADA

Source: Graham and Pettinato, 2002.

Diminishing Marginal Returns on Happiness

Environmental Recovery is Good for GDP

Prisons are good for GDP

Even a burning building is good for GDP

“What we measure affects what we do.”

Happiness Going Becoming Mainstream

• Revealed Preferences: Those displayed by purchasing decisions.

• Subjective Preferences: Those displayed directly through surveys.

-RP can be guided by addiction, social norms, or irrational decision making. Wealthier individuals have more power.

- SP offers context for how objective variables are influencing other variables of well-being.

Revealed & Subjective Preferences

Burlington Happiness Survey

1) To test a spatial response category for measuring well-being.

2) To calculate elasticity values for variables associated with well-being.

3) To calculate Gini Coefficient and Lorenz Curve for Happiness

Objectives:

1) To test a 101-point interval response category for measuring well-being.

2) To provide data able to calculate elasticity values for variables associated with well-being.

3) To provide data able to calculate Gini Coefficient and Lorenz Curve for well-being.

1) Objectives of Survey:

2) Population to be Sampled

Burlington Census Tract 9

Survey Regions

Restricted Champlain College Dormitories:Pop=451Restricted

residency:Pop=268

Converse Elderly Home: Pop=66

Tract 9: 2677 451

268 - 66 1919

Data to be Collected

- Static level of happiness for well-being variables

- Hypothetical low happiness level for well-being variables

- Hypothetical high happiness level for well-being variables

- Static level of well-being variables

-Demographic variables

Survey Design

Guiding Concept:

By collecting happiness data that behaves more like continuous income data we can begin to conduct similar analyses and utilize happiness data in a variety of new ways.

Methods of Measurement

Summary and Analysis of Data-Coding of all survey questions both with 101-point gradient

- Continuous to Interval scale

Methods of Measurement

What Hypothetical Data Does:

- Utilizes the brain’s ability to simulate experience in order to capture the directional forces of one’s pursuit of happiness.

ElasticityHow to do this?

- By measuring the estimated impact of increases or decreases in a well-being variable – family time,

education, income, etc.

Def: Elasticity

- The measure of the percentage change in one variable brought about by a percent change in another.

Experiments: What can this data do?

Loss Aversion = The tendency for people to strongly prefer avoiding losses over acquiring gains.

And its opposite:

“Gain Adoration” = The measure of one’s preference for acquiring a gain.

• Measurement: Must account for static levels

- For example, a low gain adoration score could mean the respondent already has a high level of the variable, or that the variable is less desired, or both.

- Static Level = Respondent’s definition of Happiness w/ well-being variable is based on internal high and low anchors.

- Measuring elasticities above and below static rather than the distance from static allows for directional forces to be compared across well-being variables.

- LA and GA are not necessarily inversely related because each are measured against the preference for much more/less of the variable, not a finite sum of H.

Education

Spirituality

Family

Income

Socializing

Aesthetic

Government

Violence

Job

Crime

Health

Relationship

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

LossGain

Loss Aversion & Gain Adoration

Experiments: What can this data do?

Gini Coefficients for Happiness:

- A measure of statistical dispersion, typically used to measure the inequality of income among a

population. Generates a value between 0-1; where 0=Max equality and 1=Max inequality.

- New Economics Foundation as a frontier for Happiness research.

- Illustrated using a Lorenz Curve:

Burlington Tract 9 Gini = .116

Conclusions and Future Research

-It is possible:

- To collect happiness data that can behave as though it is a continuous data, such as income.

- To calculate elasticity values for variables contributing to well-being. - To calculate Gini coefficients for happiness.

Future Research:

- Without repetitive studies, we can only consider these results as being experimental.

Experiment to Application

Subjective Measures Objective Measures

Income IncomeExperiments

Elasticity

Gini Coef Debt

Savings rates

GDP

Deficit

Mortality Rates

Fertility Rates

Experiments on Subjectivity in Burlington

Applications of Subjectivity in the Latin American Context

An Exploration of Subjective Income

in the Latin American Middle Class

Jeff FrankMS CDAE Candidate, University of Vermont

Carol Graham and Julie Markowitz

Brookings Institution

Jane Kolodinsky, Jon Erickson and Qingbin WangUniversity of Vermont

MethodsSubjective definition of Income.

Economic Ladder Scale:

Imagine a staircase with 10 steps, in which on the first step are located the poorest and on the 10th step , the richest. Where would you put yourself on this staircase?

Source: Latinobarometro Survey

Data

Latinobarometro Survey

- Annual survey of 19,000 respondents from 19 Latin American countries representing over 450 Million inhabitants.

- Dataset analyzed is comprised of data from 1997-2010, N=246870

- Dataset coded and compiled by (Frank, Markowitz and Graham, 2011)

Source: All figures are in percentages. Source for income groups is Cardenas and Henao (2011); source for other figures is Latinobaremetro data in Frank, Markowitz, and Graham (2011)

Attitudinal Variables

MiddleLow High

Do Middle Class Attitudes Differ?

Bad or Very Bad Same Good or Very Good0

10

20

30

40

50

60

In general, how would you describe the country’s present economic situation? (All Countries)

Whole Population Middle Class

Perc

ent

Dis

trib

ution

Bad or Very Bad Average Good or Very Good0

10

20

30

40

50

60

70

In general, how would you describe your present economic situation and that of your family? (All Countries)

Whole Population Middle Class

Perc

ent

Worse or Much Worse Same Better or Much Better0

5

10

15

20

25

30

35

40

45

50

In the next 12 months do you think that your economic situation and that of your family will improve, stay the same or get worse compared

to the way it is now? (All Countries)

Whole Population Middle Class

Perc

ent

Econometric Approach -Multinomial logistic model:

- Virtues – Allows for a step-by-step analysis of the Economic Ladder Scale by predicting different possible outcomes of a categorically distributed dependent variable, given a set of independent variables which are real-valued, binary-valued, or categorical-valued.

- Separate regressions for each step of the ladder. Offers the ability to view the variation in how independent variables contribute to log probability of a respondent chosing a given step of the ladder based on their responses to attitudinal variables.

Some Findings:

- Education increases slightly as subjective income increases.

- Those at the higher end of the subjective income scale have a lower level of support for democracy.

- Fear of unemployment has a strong influence with feeling poor and relieving this fear of unemployment has a strong influence

on feeling wealthier.

- People on the lower end of subjective income have lower expectations for increasing on that scale in the future and visa

versa with those on the higher end.

Acknowledgements

This research would not have been possible

without the Community Development Applied Economics (CDAE)

department’s promotion of transdisciplinary research. Additionally, this

work would not have been possible with out the continued guidance of my

thesis committee members, Dr. Jon Erickson, Dr. Jane Kolodinsky, Dr. Carol

Graham and Dr. Qingbin Wang. This work also benefited greatly from the

revisions and suggestions of CDAE graduate students, Daniel Kirk, David

Propen and Jonathan Bond.