Metrics Of Happiness

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Metrics Of Happiness

  1. 1. - Jeff Frank M.S. Candidate Community Development and Applied Economics University of Vermont The Metrics of Happiness: Assessing Subjective Well-Being using Anchoring Vignette Surveys
  2. 2. Objective <ul><li>To see if we can use an adapted version of the Anchoring Vignette survey technique to measure happiness in a way that will offer elasticity values for the contributing factors of happiness as well as a a workable social utility function. </li></ul><ul><li>Terminology: Happiness = Subjective Well-Being </li></ul>
  3. 3. How it’s currently done <ul><li>ESM – Experience Sampling Method </li></ul><ul><ul><li>Edgeworth’s Hedonimeter device </li></ul></ul><ul><li>DRM - Day Reconstruction Method </li></ul><ul><li>- Remembrance of things past, synthetic happiness </li></ul><ul><li>The U-Index </li></ul><ul><li>- Happiness through unhappiness, default state + </li></ul><ul><li>Brain Imaging </li></ul><ul><li>- Jevons, fMRI, neuro-parameters, </li></ul><ul><li>Self-Assessment Surveys </li></ul><ul><li>- (aka How yu doin?), ordinal categories </li></ul>
  4. 4. Self-Assessment Surveys with Ordinal Categories <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?” </li></ul><ul><ul><li>General Social Survey (Davis, Smith and Marsden, 2001) </li></ul></ul>
  5. 5. What’s wrong with Self-Assessment? <ul><li>Ordinal category response surveys aren’t very good at big self-assessment topics like Happiness. </li></ul><ul><ul><li>People have bias embedded in their self-assessments. </li></ul></ul><ul><li>Cross-population comparability </li></ul><ul><ul><li>Respondents understand ordinal categories differently. </li></ul></ul><ul><ul><ul><li>One person’s “Strongly Agree” may be another person’s “Agree” </li></ul></ul></ul><ul><ul><li>OJ Trial - When asked, “Did he do it?” </li></ul></ul><ul><ul><ul><li>Whites 90% Yes, Blacks 90% No </li></ul></ul></ul>
  6. 6. Anchoring Vignettes <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? </li></ul><ul><ul><li>Research assumes a difference between Real and Reported values (How to measure real value?) </li></ul></ul><ul><ul><li>Difference is presence of DIF: </li></ul></ul><ul><ul><ul><li>DIF ( response-category differential item functioning) ie. Survey conditions, temporary environmental influences (candy bars, sunny day) </li></ul></ul></ul><ul><li>Accounting for DIF you need to pivot reported value against real values. </li></ul>
  7. 7. Anchoring for Happiness <ul><li>Prevailing contributors to happiness </li></ul>Friends, Family, Marriage, Intelligence, Spirituality, Income, Extroversion, Aesthetics, Employment <ul><li>Isolate variables with vignettes on continuum </li></ul>Positive : “Paul has a normal life and a very loving family. How happy is Paul?” 0___________________________________________________100 ||||||||||1|||||||||2||||||||||3||||||||||4|||||||||5|||||||||||6|||||||||||7|||||||||8||||||||||9|||||||||| Negative: “Kim has a normal life but has no family at all. How happy is Kim?” 0___________________________________________________100 ||||||||||1|||||||||2||||||||||3||||||||||4|||||||||5|||||||||||6|||||||||||7|||||||||8||||||||||9||||||||||
  8. 8. Finding Variable Elasticities % Chg Q % Chg P = % Chg Happiness = (High-Low)/ Low % Chg Variable = 100% Def Elasticity: The measure of the percentage change in one variable brought about by a 1% change in another variable. 0____________|____________________|__________________100 ||||||||||1|||||||||2||||||||||3||||||||||4||||||||||5|||||||||||6|||||||||||7||||||||||8||||||||||9|||||||||| Low Family =25, High Family =65 %Chg F = 1.6 E(H,F) = 1.6 / 1 = 1.6 Means that a 1% increase in Family will result in a 1.6% increase in overall happiness.
  9. 9. Happiness Function <ul><li>Two-Input CES Function: </li></ul><ul><ul><li>Q(x,y) = γ ∙[ β ∙x ρ + (1- β )∙y ρ ] 1/ ρ </li></ul></ul><ul><li>Cobb-Douglas Utility Function: </li></ul><ul><li>Y = AL α K β </li></ul><ul><li>Need to understand substitutability of inputs </li></ul>
  10. 10. Substitutability of Inputs <ul><li>Priority Matrix: </li></ul><ul><li>Comparing each match, which makes you more happy? </li></ul>Marriage = M Intelligence = I Spirituality = S Family = F Wealth = W Volunteering = V Extroversion = X Aesthetics = A Job satisfaction = J J J A J X X J V X V J W X V W F F F F F F J A X V S F S J I X I I F S I M M M M M F M I M J A X V W F S I M
  11. 11. Results of Priority Matrix <ul><li>Family = 8 </li></ul><ul><li>Marriage = 6 </li></ul><ul><li>Job Satisfaction = 6 </li></ul><ul><li>Extroversion = 5 </li></ul><ul><li>Intelligence = 4 </li></ul><ul><li>Volunteerism = 3 </li></ul><ul><li>Spirituality = 2 </li></ul><ul><li>Wealth = 1 </li></ul><ul><li>Aesthetics = 1 </li></ul>F = 1 M = 0.75 J = 0.75 X = 0.625 I = 0.5 V = 0.375 S = 0.25 W = 0.125 A = 0.125 Variable Priority 8 H f(M) = 0.75 M ^ 1.6
  12. 12. Sample Survey <ul><li>N = 21 Elasticities </li></ul><ul><li>Marriage = 0.404 </li></ul><ul><li>Intelligence = -0.055 </li></ul><ul><li>Spirituality = 0.182 </li></ul><ul><li>Family = 0.483 </li></ul><ul><li>Wealth = 0.241 </li></ul><ul><li>Volunteering = 0.157 </li></ul><ul><li>Extroversion = 0.328 </li></ul><ul><li>Aesthetics = 0.243 </li></ul>
  13. 14. Estimating Real Happiness <ul><li>We can estimate Real Happiness to compare with Reported Happiness using census and biographic data to apply to our Happiness function. </li></ul><ul><li>Difference between Real an Reported is presence of DIF </li></ul>
  14. 15. Uses and Policy Implications <ul><li>Could help prioritize social programs by identifying what actions will cause best results. </li></ul><ul><li>Identifies negatives of threats to these variables. </li></ul><ul><li>Identify policy directives, inclusion of well-being. </li></ul><ul><li>Offers hard measures for well-being valuation. </li></ul><ul><li>Can help define: Development </li></ul><ul><li>Help identify community and international development goals. </li></ul>

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