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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.
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