Easterlin Slides 09 30 05

Information about Easterlin Slides 09 30 05

Published on August 9, 2007

Author: Richie

Source: authorstream.com

Content

Life Cycle Happiness and Its SourcesIntersections of Demography, Economics and Psychology:  Life Cycle Happiness and Its Sources Intersections of Demography, Economics and Psychology Richard A. Easterlin University of Southern California Questions:  Questions 1. On average, at what stage of life are people happiest? On the threshold of their adult lives? At mid-life? In their golden years? 2. What factors principally determine the life cycle pattern of happiness? personality and genetics Life circumstances (work, family, health etc.) Evidence on life cycle happiness :  Evidence on life cycle happiness Psychology: Constant (Myers, Costa et al) Rising (Carstensen, Mroczek andamp; Kolarz) Inverted-U (Mroczek andamp; Spiro) Economics: U-shape (Blanchflower-Oswald, Frey-Stutzer) What is the question being addressed?:  What is the question being addressed? Pure effect of age itself: U-shape (most economists) All effects considered, as people age: Constant (Myers, Costa et al) Theory: Strong Economic Model:  Theory: Strong Economic Model Happiness varies with OBJECTIVE life circumstances (income, employment, health…) Theory:Psychology (1):  Theory: Psychology (1) Effect on Happiness of objective conditions is mediated by psychological mechanisms, such as ADAPTATION. Psychology (2)Strong Setpoint Model:  Psychology (2) Strong Setpoint Model Happiness depends on personality and genetic heritage Individuals adapt rapidly and fully to life events (serious accident, marriage, lottery) What people say is important for their happiness:  What people say is important for their happiness Present Study:  Present Study Data: US General Social Survey, 1973-1994 Principal Variables HAPPY: Global happiness (scaled from 3 to 1) Satisfaction Variables: SATFIN: financial situation (3 to 1) SATJOB: work including keeping house (4 to 1) SATFAM: family life (7 to 1) SATHEALTH: health (7 to 1) DemographySynthetic Panel Method: The Ideal:  Demography Synthetic Panel Method: The Ideal Record the Reported Happiness of a random sample of persons born in a given year (birth cohort) as they age year by year from 18 to 89. Problem:  Requires 81 years of data Synthetic Panel Method: Actual:  Synthetic Panel Method: Actual Piece together the full life course from 21-year segments of experience of single year birth cohorts - - young, midlife, 'golden years'. (21 yrs of data for each of 51 cohorts, born 1905-1955) (Average of 10.5 yrs. Data for 21 earlier cohorts, 21 later cohorts) Method of Estimating Life Cycle Pattern:  Method of Estimating Life Cycle Pattern Eq. (1) Ordered Logit Regression Dependent Variable: Happy (or Domain Satisfaction) Independent Variables (5): Age, with controls for Year of Birth Race Gender Education Figure 1: Life Cycle Happiness:  Figure 1: Life Cycle Happiness Figure 2a: Life Cycle Happiness and Domain Satisfaction:  Figure 2a: Life Cycle Happiness and Domain Satisfaction Figure 2b: Life Cycle Happiness and Domain Satisfaction:  Figure 2b: Life Cycle Happiness and Domain Satisfaction Explaining domain satisfaction:  Explaining domain satisfaction Objective conditions evident: sathealth, satjob, satfam Subjective perceptions evident: satfin Implications of Domain Patterns for Causality:  Implications of Domain Patterns for Causality Top-down vs. bottom-up theory in psychology Top-down: Personality/genetics -andgt; Happy, Domain sat. -- contradicted by domain patterns Bottom-up: Domain sat. -andgt; Happy to be tested How Happiness Varies with Domain Satisfaction:  How Happiness Varies with Domain Satisfaction Eq. (2) Ordered Logit Regression (coefficients all significant at .001 level) Satfam .461 Satjob .498 Satfin .573 Sathealth .242 Cut 1= 4.299 Cut 2 = 7.744 N = 18,440 LL = -14,852 Chi2 = 3,200 Pseudo R2 = .133 Causality:  Causality How important are Life Circumstances in explaining: Individual differences? vs. Life cycle trend? Can life cycle domain patterns explain life cycle happiness? Method for Predicting Life Cycle Happiness at Each Age:  Method for Predicting Life Cycle Happiness at Each Age Enter in regression equation (2) the predicted value at each age of each domain satisfaction variable plotted in Figures 2a and 2b. Figure 3: Life Cycle Happiness, Actual and Predicted:  Figure 3: Life Cycle Happiness, Actual and Predicted Implications of Fig. 3:  Implications of Fig. 3 Causality: Supports 'Bottom-up' theory Stability in life cycle happiness results from larger but offsetting changes in particular areas of life. Could stability in life cycle happiness be due to Adaptation-across-Domains?:  Could stability in life cycle happiness be due to Adaptation-across-Domains? (1) No: In regression, domain weights are constant Could stability in life cycle happiness be due to Adaptation-across-Domains?:  Could stability in life cycle happiness be due to Adaptation-across-Domains? (2) No: Statistical tests do not reveal systematic shift in weights by age consistent with Adaptation-across- Domains (for example, Sathealth and Satfin move contrary to adaptation hypothesis) What is the Pure Effect of Aging?:  What is the Pure Effect of Aging? Method of Estimating Pure Effect of Age Eq. (3) Ordered Logit Regression Dependent Variable: Happy Independent Variables (9): Age, with controls for Year of Birth Satfin Gender Satjob Education Satfam Race Sathealth Fig. 4: The Pure Effect of Aging:  Fig. 4: The Pure Effect of Aging Conclusion (1) Facts:  Conclusion (1) Facts If one maintains an intact marriage and good health into old age then it is likely that he or she will be happier than at age 20 - the pure effect of aging dominates the trend in happiness. But for the average person changing life circumstances override the effect of aging - happiness rises slightly to midlife and then declines. Conclusion (2) Theory:  Conclusion (2) Theory Not supported Strong economic model Strong setpoint model Top-down theory Supported 'Bottom up' theory in which both objective and subjective conditions determine satisfaction in each domain. Conclusion (3) Policy:  Conclusion (3) Policy Is there an 'Iron Law of Happiness' – is stability inevitable? Answer: No Policies that raise domain satisfaction will increase happiness.

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