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Experiments on Subjective Preference: Toward a New Methodology  and  Applications of Subjective Income Jeffrey E. Frank M.S. Candidate, Community Development and Applied Economics Certificate of Ecological Economics Advisor: Jane Kolodinsky, Ph.D. March 24, 2011
Outline Chapter 1: Continuum Happiness Surveys ,[object Object]
 Methodology
 Research Objectives
 Burlington Happiness Survey
 Surveying for Elasticity
 Results
 Elasticities
 Loss Aversion vs. Gain Adoration
 Lorenz CurvesChapter 2: On Subjectivity: Experiments and Applications Chapter 3: ,[object Object]
 Methods and Data
 Do Middle Class Attitudes Differ?
 Results,[object Object]
“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)
[object Object]
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.,[object Object]
Environmental Recovery is Good for GDP
Prisons are good for GDP
Even a burning building is good for GDP
Happiness Going Becoming Mainstream “What we measure affects what we do.”
Revealed & Subjective Preferences ,[object Object]
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. ,[object Object]
Burlington Happiness Survey Objectives: To test a spatial response category for measuring well-being. To calculate elasticity values for variables associated with well-being. To calculate Gini Coefficient and Lorenz Curve for Happiness
1) Objectives of Survey: To test a 101-point interval response category for measuring well-being. To provide data able to calculate elasticity values for variables associated with well-being. To provide data able to calculate Gini Coefficient and Lorenz Curve for well-being.
2) Population to be Sampled
Burlington Census Tract 9
Survey Regions Restricted Champlain College Dormitories: Pop=451 Converse Elderly Home: Pop=66 Restricted residency: Pop=268 Tract 9:  2677                  451 	        268            -       66                1919
Data to be Collected ,[object Object]
 Hypothetical low happiness level for well-being variables
 Hypothetical high happiness level for well-being variables
 Static level of well-being variables
Demographic variables  ,[object Object]
Methods of Measurement
Summary and Analysis of Data ,[object Object]
 Continuous to Interval scale,[object Object]
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.
Elasticity How 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.
[object Object]
  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.,[object Object]
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; where0=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

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CDAE Defense Jeff Frank

  • 1. Experiments on Subjective Preference: Toward a New Methodology and Applications of Subjective Income Jeffrey E. Frank M.S. Candidate, Community Development and Applied Economics Certificate of Ecological Economics Advisor: Jane Kolodinsky, Ph.D. March 24, 2011
  • 2.
  • 6. Surveying for Elasticity
  • 9. Loss Aversion vs. Gain Adoration
  • 10.
  • 12. Do Middle Class Attitudes Differ?
  • 13.
  • 14. “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)
  • 15.
  • 16. Because the traditional assumption that people are income-based profit-seeking agents in the economy has proven itself incomplete.
  • 17.
  • 19. Prisons are good for GDP
  • 20. Even a burning building is good for GDP
  • 21. Happiness Going Becoming Mainstream “What we measure affects what we do.”
  • 22.
  • 23. Subjective Preferences: Those displayed directly through surveys.
  • 24. RP can be guided by addiction, social norms, or irrational decision making. Wealthier individuals have more power.
  • 25.
  • 26. Burlington Happiness Survey Objectives: To test a spatial response category for measuring well-being. To calculate elasticity values for variables associated with well-being. To calculate Gini Coefficient and Lorenz Curve for Happiness
  • 27. 1) Objectives of Survey: To test a 101-point interval response category for measuring well-being. To provide data able to calculate elasticity values for variables associated with well-being. To provide data able to calculate Gini Coefficient and Lorenz Curve for well-being.
  • 28. 2) Population to be Sampled
  • 30. Survey Regions Restricted Champlain College Dormitories: Pop=451 Converse Elderly Home: Pop=66 Restricted residency: Pop=268 Tract 9: 2677 451 268 - 66 1919
  • 31.
  • 32. Hypothetical low happiness level for well-being variables
  • 33. Hypothetical high happiness level for well-being variables
  • 34. Static level of well-being variables
  • 35.
  • 36.
  • 38.
  • 39.
  • 40. 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.
  • 41.
  • 42.
  • 43.
  • 44. Elasticity How 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.
  • 45.
  • 46.
  • 47. 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.
  • 48.
  • 49. 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.
  • 50. Static Level = Respondent’s definition of Happiness w/ well-being variable is based on internal high and low anchors.
  • 51. Measuring elasticities above and below static rather than the distance from static allows for directional forces to be compared across well-being variables.
  • 52.
  • 53. 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; where0=Max equality and 1=Max inequality. - New Economics Foundation as a frontier for Happiness research. - Illustrated using a Lorenz Curve:
  • 54.
  • 55. Burlington Tract 9 Gini = .116
  • 56.
  • 57.
  • 58. To collect happiness data that can behave as though it is a continuous data, such as income.
  • 59. To calculate elasticity values for variables contributing to well-being.
  • 60.
  • 61. Experiments on Subjectivity in Burlington Applications of Subjectivity in the Latin American Context
  • 62. An Exploration of Subjective Income in theLatin American Middle Class Jeff Frank MS CDAE Candidate, University of Vermont   Carol Graham and Julie Markowitz Brookings Institution   Jane Kolodinsky, Jon Erickson and Qingbin Wang University of Vermont
  • 63. Methods Subjective 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
  • 64. 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)
  • 65. 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)
  • 67.
  • 69. Do Middle Class Attitudes Differ?
  • 70.
  • 71.
  • 72.
  • 73. 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.
  • 74.
  • 75.
  • 76.
  • 77. 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.
  • 78. 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.

Notas del editor

  1. For this reason studying happiness in Burlington is like studying plants in the Amazon.
  2. Last bullet is just a note to dispel some misinterpretations I’ve experienced during research.
  3. 1 out of every 32 people in the United States are in prison or on some kind of parole.
  4. You can see the firefighters here enjoying the boost to GDP
  5. Joe Stiglitz, 2001 Nobel Prize in Economics
  6. Based on 2000 Census data, this region of Burlington is the wealthiest region in the city.
  7. These are the survey regions broken up for survey teams to reach in order to gather an even distribution.
  8. This survey went through 7 versions and received consultation from 3 psychologist, these committee members and scores of pretest respondents.
  9. Roughly 2 million years ago when the brain of our ancestors tripled in size we gained a prefrontal lobe. The prefrontal lobe is the part of the brain that allows us to simulate experience. When activating the prefrontal lobe’s experience simulator we are using the experience of our memory to predict an outcome.
  10. What these tell us are how respondents may design their pursuit of happiness, what behaviors may guide them in the near future and what may provoke them through both fear or loss and desire for gain.
  11. And this is an important point
  12. Instead of going through absilutel comparisons of each variable, here are some samples.