CDAE Defense Jeff Frank

667 views

Published on

0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
667
On SlideShare
0
From Embeds
0
Number of Embeds
5
Actions
Shares
0
Downloads
1
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • For this reason studying happiness in Burlington is like studying plants in the Amazon.
  • Last bullet is just a note to dispel some misinterpretations I’ve experienced during research.
  • 1 out of every 32 people in the United States are in prison or on some kind of parole.
  • You can see the firefighters here enjoying the boost to GDP
  • Joe Stiglitz, 2001 Nobel Prize in Economics
  • Based on 2000 Census data, this region of Burlington is the wealthiest region in the city.
  • These are the survey regions broken up for survey teams to reach in order to gather an even distribution.
  • This survey went through 7 versions and received consultation from 3 psychologist, these committee members and scores of pretest respondents.
  • 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.
  • 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.
  • And this is an important point
  • Instead of going through absilutel comparisons of each variable, here are some samples.
  • CDAE Defense Jeff Frank

    1. 1. Experiments on Subjective Preference:<br />Toward a New Methodology <br />and <br />Applications of Subjective Income<br />Jeffrey E. Frank<br />M.S. Candidate, Community Development and Applied Economics<br />Certificate of Ecological Economics<br />Advisor: Jane Kolodinsky, Ph.D.<br />March 24, 2011<br />
    2. 2. Outline<br />Chapter 1: Continuum Happiness Surveys<br /><ul><li> Introduction: Why Happiness?
    3. 3. Methodology
    4. 4. Research Objectives
    5. 5. Burlington Happiness Survey
    6. 6. Surveying for Elasticity
    7. 7. Results
    8. 8. Elasticities
    9. 9. Loss Aversion vs. Gain Adoration
    10. 10. Lorenz Curves</li></ul>Chapter 2: On Subjectivity: Burlington, VT and the Latin American Context<br />Chapter 3:<br /><ul><li> Introduction: Middle Class
    11. 11. Methods and Data
    12. 12. Do Middle Class Attitudes Differ?
    13. 13. Results</li></li></ul><li>
    14. 14. “BTV Ranked Happiest Small-City In The Country: Gallup Finds Burlington Among The Happiest, Healthiest”<br />- March 17, 2011<br />(Terminology: Happiness = Well-Being, Subjective Well-Being) <br />
    15. 15. <ul><li>Why Happiness?
    16. 16. Because the traditional assumption that people are income-based profit-seeking agents in the economy has proven itself incomplete.
    17. 17. Because profit and income are not innate human directives, but the pursuit of our happiness is.</li></ul> (Note: Happiness data can be used both to inform the ways respondents accumulate/qualify their happiness, but also can inform how to best relieve misery.) <br />
    18. 18. Diminishing Marginal Returns on Happiness<br />
    19. 19. Environmental Recovery is Good for GDP<br />
    20. 20. Prisons are good for GDP<br />
    21. 21. Even a burning building is good for GDP<br />
    22. 22. Happiness Going Becoming Mainstream<br />“What we measure affects what we do.”<br />
    23. 23. Self-Assessment Surveys with Ordinal Categories<br /><ul><li>“Taken all together, how would you say things are these days – would you say that you are very happy, pretty happy, or not too happy?”
    24. 24. General Social Survey</li></li></ul><li>
    25. 25. Burlington Happiness Survey<br />Objectives:<br />To test a 101-point interval response category for measuring well-being.<br />To calculate elasticity values for variables associated with well-being.<br />To calculate Gini Coefficient and Lorenz Curve for Happiness<br />
    26. 26. Survey Construction<br />1) Objectives of the Survey<br />2) Population to be Sampled<br />3) Degree of Precision Desired<br />4) Data to be Collected<br />5) Methods of Measurement<br />6) Selection of the Sample<br />7) Organization of the Field Work<br />8) Summary and Analysis of Data<br />
    27. 27. 1) Objectives of Survey:<br />To test a 101-point interval response category for measuring well-being.<br />To provide data able to calculate elasticity values for variables associated with well-being.<br />To provide data able to calculate Gini Coefficient and Lorenz Curve for well-being.<br />
    28. 28. 2) Population to be Sampled<br />
    29. 29. Burlington Census Tract 9<br />
    30. 30. Survey Regions<br />Restricted Champlain College Dormitories:<br />Pop=451<br />Converse Elderly Home: Pop=66<br />Restricted residency:<br />Pop=268<br />Tract 9: 2677<br /> 451<br /> 268<br /> - 66<br /> 1919<br />
    31. 31. 3) Degree of Precision<br /> Population: 1919<br /> Confidence Level: 95%<br />Confidence Interval: 5.75<br /> Sample Size: 252<br />
    32. 32. 4) Data to be Collected<br /><ul><li>Static level of happiness for well-being variables
    33. 33. Hypothetical low happiness level for well-being variables
    34. 34. Hypothetical high happiness level for well-being variables
    35. 35. Static level of well-being variables
    36. 36. Demographic variables </li></li></ul><li>Survey Design<br />Guiding Concept:<br />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 ways. Allows happiness data able to complement income data in new ways.<br />
    37. 37.
    38. 38.
    39. 39.
    40. 40.
    41. 41. 5) Methods of Measurement<br />
    42. 42. 5) Methods of Measurement<br />
    43. 43. 6) Selection of Sample<br />Skip Interval = N/n = 1919/252 = 7.6 = 8 <br />Skip interval of every 8th house on right <br />
    44. 44. 7) Organization of the Field Work<br />- Pretested 7 versions among classes and volunteers<br />- First surveyed alone (roughly 25). <br />- Reviewed results<br />- Then recruited RM 250 Lab students. <br />- Used 2 class periods for training and survey methodology familiarization.<br />
    45. 45. 8) Summary and Analysis of Data<br /><ul><li>Coding of all survey questions both with 101-point gradient
    46. 46. Continuous to Interval scale</li></li></ul><li>What Hypothetical Data Does:<br /> - Utilizes the brain’s ability to simulate experience in order to capture the directional forces of one’s pursuit of happiness. <br />Prefrontal cortex simulations<br />
    47. 47. Elasticity<br />How to do this? <br /> - By measuring the estimated impact of increases or decreases in a well-being variable – family time, education, income, etc.<br />Def: Elasticity<br /> - The measure of the percentage change in one variable brought about by a percent change in another.<br />
    48. 48.
    49. 49.
    50. 50.
    51. 51. Calculating Arc Elasticity<br />
    52. 52. Calculating Arc Elasticity<br />=<br />
    53. 53.
    54. 54. Experiments: What can this data do?<br />Loss Aversion = The tendency for people to strongly prefer avoiding losses over acquiring gains. <br />And its opposite:<br />“Gain Adoration” = The measure of one’s preference for acquiring a gain.<br />
    55. 55. <ul><li> Must account for static levels
    56. 56. 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.
    57. 57. Static Level = Respondent’s definition of Happiness w/ well-being variable is based on internal high and low anchors.
    58. 58. Measuring elasticities above and below static rather than the distance from static allows for directional forces to be compared across well-being variables.
    59. 59. 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.</li></li></ul><li>Loss Aversion & Gain Adoration<br />
    60. 60. Experiments: What can this data do?<br />Gini Coefficients for Happiness:<br /> - A measures 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. <br /> - Noted by UK Group as a frontier for Happiness research.<br /> - Illustrated using a Lorenz Curve:<br />
    61. 61.
    62. 62. Burlington Tract 9 Gini = .116 <br />
    63. 63.
    64. 64. Conclusions and Future Research<br /><ul><li>It is possible:
    65. 65. To collect happiness data that can behave as though it is a continuous data, such as income.
    66. 66. To calculate elasticity values for variables contributing to well-being.
    67. 67. To calculate Gini coefficients for happiness. </li></ul>Future Research:<br /><ul><li> Without repetitive studies, we can only consider these results tentative. </li></li></ul><li>Subjective Well-being:<br /><ul><li>Burlington H. Survey offers experimental methods of capturing subjective assessments of well-being.
    68. 68. Subjectivity has flaws
    69. 69. We lie – intentionally or unintentionally.
    70. 70. We gravitate toward expected norms
    71. 71. We adapt</li></ul>- Policy implications of subjective well-being research is still undetermined and highly contentious. <br />- To get closer to applications we study subjective income. <br />
    72. 72.
    73. 73. Revealed vs. Subjective Preferences<br /><ul><li>Revealed Preferences: Those displayed by purchasing decisions.
    74. 74. Subjective Preferences: Those displayed directly through surveys.
    75. 75. Complementary relationship
    76. 76. Much used RFK quote, “GDP tells us everything except that which makes life worthwhile.”
    77. 77. The wealthy get to have more preference.
    78. 78. Subjectivity can offer
    79. 79. Include more about complementary nature </li></li></ul><li>Subjective Income<br /><ul><li> In order to make the best complements between subjective and objective income, we can measure income subjectively and assess those issues untouched by objective measures. </li></li></ul><li>An Exploration of Subjective Income <br />in theLatin American Context<br />Jeff Frank<br />MS CDAE Candidate, University of Vermont<br /> <br />Carol Graham and Julie Markowitz<br />Brookings Institution<br /> <br />Jane Kolodinsky, Jon Erickson and Qingbin Wang<br />University of Vermont<br />
    80. 80. What is the Middle Class?<br />- Highly difficult to identify so many income cut-offs are arbitrary. <br />
    81. 81. Methods<br />Subjective definition of Income.<br />Economic Ladder Scale:<br />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?<br />Source: Latinobarometro Survey<br />
    82. 82. 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)<br />
    83. 83. Data<br />Latinobarometro Survey<br /> - Annual survey of 19,000 respondents from 19 Latin American countries representing over 450 Million inhabitants. <br /> - Dataset analyzed is comprised of data from 1997-2010, N=<br /> - Dataset coded and compiled by (Frank, Markowitz and Graham, 2011)<br />
    84. 84. Attitudinal Variables<br />
    85. 85. Strength of Correlation against Economic Ladder Scale<br />
    86. 86.
    87. 87. High<br />Middle<br />Low<br />
    88. 88. Do Middle Class Attitudes Differ?<br />
    89. 89.
    90. 90.
    91. 91. Econometric Approach <br /><ul><li>Multinomial logistic model:
    92. 92. 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.
    93. 93. 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 chosen a given step of the ladder based on their responses to attitudinal variables. </li></li></ul><li>
    94. 94.
    95. 95.
    96. 96. Some Findings: <br /> - Education increases slightly as subjective income increases. <br /> - Those at the higher end of the subjective income scale have a lower level of support for democracy. <br /> - Fear of unemployment has a strong influence with feeling poor and relieving this fear of unemployment has a strong influence on feeling wealthier. <br /> - 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. <br />

    ×