The Economics of Happiness and Health Andrew Oswald IZA and Warwick

1. The Economics of Happiness and Health Andrew Oswald IZA and Warwick I would like to acknowledge that much of this work is joint with coauthors Chris Boyce, Andrew Clark, Nick Powdthavee, David G. Blanchflower, and Steve Wu.

2. This week I’d like to propose a number of ideas.

3. #1 ‘Happiness’ data offer us interesting potential as proxy-utility data. u = u(y, z, ..)

4. Regression equations Mental well-being = f(Age, gender, education level, income, marital status, friendship networks, region, year…)

5. We now know: • There is a lot of regularity in these regression-equation patterns, across countries and well-being measures. • Fairly robust to panel estimators and different methods. • Progress can be made on causality.

6. One potentially important implication:

7. If this form of function can be estimated (and K, L, M are life events): Happiness = a + bK + cL + dM +eY where Y is income,

8. If this form of function can be estimated (and K, L, M are life events): Happiness = a + bK + cL + dM +eY where Y is income, then we may be able to use such equations to calculate the implied dollar value of the happiness from life events K, L, M.

9. Monetary equivalences A life satisfaction equation: Life satisfaction = B1*income + B2*Event + error Marriage - $100,000 (Blanchflower and Oswald, 2004), Neuroticism - $314,000 (Boyce et al., in press), Widowhood – ($175,000-$496,000), Health limiting daily activities ($473,000) (Powdthavee, van den Berg, 2011)

10. #2 The next 20 years are likely to see economists work more and more with physiological and hard-science data.

11. #3 Biomarker data will (slowly) be used more and more in economics.

12. #4 Empirically, there are strong relative effects on utility:

13. #4 Empirically, there are strong relative effects on utility: u = u(y, y*) eg. if y* is others’ incomes.

14. #5 A crucial role in social-science behaviour is played by the second derivative, v″, of the function utility = v(relative status)+ ..

15. In humans (I shall argue) • Concavity of v(.) leads to imitation and herd behaviour • Convexity of v(.) leads to deviance.

16. #6 The Stiglitz Commission’s ideas will eventually take hold.

17. Stiglitz Report 2009: “Measures of .. objective and subjective well-being provide key information about people’s quality of life. Statistical offices [worldwide] should incorporate questions to capture people’s life evaluations, hedonic experiences … in their own survey.” P.16. Executive Summary of Commission Report.

18. So........

19. Could we perhaps learn …

20. ..how to make whole countries happier?

21. Preferably not like this…

22. Germany 4 England 1

23. Germany 4 England 1

24. Useful introductions • “Relative Income, Happiness and Utility: An Explanation for the Easterlin Paradox and Other Puzzles” (Andrew Clark, Paul Frijters and Mike Shields), Journal of Economic Literature, 2008. • The Happiness Equation (Nick Powdthavee), Icon Books, 2010.

25. This is a good time for general questions if people would like to ask some?

26. Now let’s think about how human beings report their feelings (for example, in a survey).

27. First, they have genuine feelings inside themselves (about how happy they are, say).

28. Second, they make a decision about how to report those feelings.

29. There are then two processes going on inside a person.

30. Human feelings Human reporting

31. Let’s think of the example of money and people’s well-being.

32. Assume

33. Assume People get true happiness, h, from income, y. Call it h(y).

34. Assume People get true happiness, h, from income, y. Call it h(y). They give a number for this, which is their reported happiness, r. Call it r(h).

35. The Reporting Function

36. The Reporting Function Write R(y) which is reported happiness as a function of income. This is what is studied in well-being regression equations.

37. Now think of the function-of-a-function rule in calculus.

38. By definition R(y) = r(h(y))

39. By definition R(y) = r(h(y)) so Rʹ(y) = rʹ(h) hʹ(y) > 0 where y is income.

41. In the cross-section, income is positively correlated with happiness Take America in 1994 for example

42. From Deaton-Kahneman in PNAS 2010

43. Now let’s think of the second derivative

44. The first derivative earlier was: Rʹ(y) = rʹ(h) hʹ(y)

45. The first derivative earlier was: Rʹ(y) = rʹ(h) hʹ(y) where y is income, r is reported happiness, h is actual happiness.

46. Think of the second derivative The curvature of reported happiness is

47. Think of the second derivative The curvature of reported happiness is R″(y) = r″(h) hʹ(y) hʹ(y) + rʹ(h) h″(y)

48. But if R″(y) is found to be negative that does not prove that h″(y) is negative. R is reported happiness h is true happiness

49. Hence there are lots and lots of papers in the literature that get this wrong.

50. Reiterating why: The curvature of reported happiness is R″(y) = r″(y) hʹ(y) hʹ(y) + rʹ(h) h″(y)