Beyond GDP? Welfare across Countries and Time

Transcription

1 Beyond GDP? Welfare across Countries and Time Chad Jones and Pete Klenow Stanford University and NBER February 28, 2011

2 Comparing welfare across countries and over time How successful is an economy at delivering the highest possible welfare for its citizens? Fundamental question at the heart of economic growth and development Per capita GDP is our standard (shortcut) answer Can we do better?

3 GDP per capita Welfare Utility depends on: Consumption Life Expectancy Leisure Inequality... But GDP per capita only measures income...

4 Motivating Example 1: France vs. the U.S. U.S. has higher private consumption But compared to the U.S., France has: More leisure Less inequality More public consumption (percentage) Longer life expectancy Which country delivers higher welfare, the U.S. or France?

5 Motivating Example 2: Growth in China Income has been growing rapidly in China Amidst the growth: Leisure has fallen Inequality has risen The saving rate has risen (bad, controlling for income!) Life expectancy has lengthened Has welfare risen faster or slower than income in China?

6 What We Do Assume: Perspective of one set of preferences (those of Rawls ) Popular functional form over consumption, leisure, lifespan Parameters to match U.S. consumption, leisure, value of life Evaluate outcomes using a particular set of preferences: Expected utility behind the Rawlsian veil in each country-year Flow measure of welfare, not PDV Fraction of U.S. consumption which makes Rawls indifferent Two approaches: Macro calculation: Macro data for 134 countries. Micro calculation: Household surveys for 5 countries.

7 Important Shortcomings of our Approach Factors we do not capture Morbidity (other than through health spending) Quality of the natural environment Political freedoms Crime... But neither does income!

8 Summary of Results Income and welfare are highly correlated in both levels and growth rates. Nevertheless, differences between income and welfare are economically important: Median deviation in levels is over 40 percent. Median deviation in growth rates is about 1 percentage point.

9 Related Literature Nordhaus and Tobin s Measure of Economic Welfare Consumption and Leisure in the U.S. over time No Inequality or Life Expectancy, no country comparisons U.N. Human Development Index Adds [0,1] Income, Life Expectancy, Literacy Ravallion (2010) mashup critique Becker, Philipson, and Soares (2005) Combines per capita GDP and life expectancy full income Mainly focused on evolution of cross-section dispersion Fleurbaey and Gaulier (2009) Full-income measure of life expectancy, leisure, and inequality OECD only, levels only, not consumption-based

10 Theory Underlying the Macro Calculations Let Rawls live for a year as a random person in some country, facing their mortality rates and consumption/leisure distribution.

11 Overview of Welfare Expected utility behind the Rawlsian veil of ignorance: V(e, c, l, σ) = e (ū + log c + v(l) 12 ) σ2

12 Preferences Let C denote an individual s consumption. Independent of age. Let l denote leisure or time spent in home production. Flow utility in benchmark case u(c, l) = ū + log C + v(l) ū influences the value of life given C, l.

13 Life Expectancy Rawls draws age Uniform[0,100] Faces the cross-sectional mortality rates for 2000 in a country Probability of Survival to Age a 1 Life Expectancy, e p = probability lives instead of dies 0 p = e/ Expected utility normalizing death to be 0: p u(c, l) + (1 p) 0 = e u(c, l)/100. Age, a

14 Inequality in Consumption Suppose consumption C is log-normally distributed. Arithmetic mean c (consumption per capita). Standard deviation σ. Conditional on being alive, expected utility from consumption is: E[log C] = log c 1 2 σ2

15 Rawlsian Utility for a Country Assumptions for Macro Calculation: Assume survival rates S(a) are independent of consumption. Assume log-normal consumption independent of age. Assume no inequality in leisure. Expected utility behind the Rawlsian veil of ignorance: V(e, c, l, σ) = e (ū + log c + v(l) 12 ) σ2

16 Comparing Welfare Across Countries What makes Rawls indifferent between the U.S. and country i? One answer: Scaling U.S. consumption by some proportion λ i. V(e us, λ i c us, l us, σ us ) = V(e i, c i, l i, σ i )

17 Decomposing Welfare Differences Across Countries log λ i = e i e us e us (ū + log c i + v(l i ) 1 2 σ2 i ) Life Expectancy + log c i log c us Consumption + v(l i ) v(l us ) Leisure 1 2 (σ2 i σ 2 us) Inequality

18 As a ratio to per capita GDP log λ i ỹ i = e i e us e us (ū + log c i + v(l i ) 1 2 σ2 i ) Life Expectancy + log c i /y i log c us /y us Consumption Share + v(l i ) v(l us ) Leisure 1 2 (σ2 i σ 2 us) Inequality

19 Equivalent vs. Compensating Variation What makes Rawls indifferent between the U.S. and country i? Alternative answer: Scaling foreign consumption by some proportion λ i. V(e us, c us, l us, σ us ) = V(e i, c i /λ i, l i, σ i )

20 Decomposing Welfare based on Compensating Variations log λ i = e i e us e i (ū + log c us + v(l us ) 1 2 σ2 us) Life Expectancy + log c i log c us Consumption + v(l i ) v(l us ) Leisure 1 2 (σ2 i σ 2 us) Inequality Baseline: report the geometric average of the compensating and equivalent variations.

21 Decomposing Welfare Differences Across Time log λ t,t+1 = e t+1 e t e t+1 (ū + log c t + v(l t ) 1 2 σ2 t ) Life Expectancy + log c t+1 log c t Consumption + v(l t+1 ) v(l t ) Leisure 1 2 (σ2 t+1 σ2 t ) Inequality Baseline: report the geometric average of the compensating and equivalent variations.

22 Data / Calibration for the Macro Calculations

23 Macro Data Sources Penn World Table 6.3 (National Accounts + World Bank ICP): Income Private consumption, government consumption Employment / adult population World Bank: Life Expectancy The Conference Board / OECD: Annual hours worked per worker United Nations World Income Inequality Database: Gini Coefficients

24 Leisure / Home Production l = 1 annual hours worked per worker employment adult population. Annual hours worked per worker The Conference Board has data for 50 countries Missing observations filled in with 2000 value or U.S. value. Employment per adult population From Penn World Tables and World Bank Implicitly assumes kids and adults have same leisure/hp. Micro calculation uses hours per year from household surveys, varying across people.

25 Leisure or Home Production Leisure or Home Production Yemen Jordan Belgium Bulgaria Germany Mali Egypt Puerto Rico Tunisia Pakistan Malta Italy Tajikistan South Africa Norway Nigeria Namibia Hungary Turkey Austria India Sri Lanka Poland United Kingdom Haiti Botswana Israel Finland Uzbekistan Russia Mauritius Japan Australia Panama Niger Nepal Philippines Venezuela Cyprus United States Guinea Bissau Sierra Leone Indonesia Chile Georgia Brazil Luxembourg Zambia Bangladesh Mexico Somalia Bolivia Zimbabwe Peru Swaziland Bahamas Ghana Vietnam Paraguay Jamaica Iceland Malawi Thailand Senegal Hong Kong Colombia Gambia China Cambodia Ethiopia Rwanda Kenya South Korea Singapore Madagascar Mozambique Uganda Guinea Burundi Tanzania 1/64 1/32 1/16 1/8 1/4 1/2 1 GDP per person

26 Gini Coefficients and σ 2 When consumption is log normal, there s a one-to-one mapping between the gini coefficient and the standard deviation: ( ) σ G = 2Φ 1 2 G is the value of the Gini coefficient. Φ( ) is the cdf of the standard normal distribution. Invert to get σ

27 Within-Country Inequality Standard deviation of log consumption 1.6 Namibia Zimbabwe Lesotho Central African Republic South Africa Bahamas Guinea Bissau Zambia Haiti Paraguay Kenya Brazil Honduras Mexico Mali Bolivia Chile Nigeria Cameroon Botswana Venezuela Niger China Puerto Rico Senegal Nicaragua Somalia Thailand Nepal Peru Tanzania Russia Ethiopia Fiji Hong Kong Malaysia Ghana Tunisia Vietnam Georgia Singapore Burundi Estonia India Indonesia Ukraine Mauritius United States Yemen Lithuania Italy U.K. Albania Poland Belarus Croatia Japan Bulgaria Malta Norway Hungary France Iceland Luxembourg Slovak Republic Finland Czech Republic 1/64 1/32 1/16 1/8 1/4 1/2 1 GDP per person

28 Calibrating the Utility from Leisure Assume v(l) = θɛ 1+ɛ 1+ɛ (1 l) ɛ Frisch elasticity of labor supply is ɛ Hall (2009a,b) surveys/reports a Frisch elasticity of 0.7 for intensive margin and 1.9 for both margins together. We choose ɛ = 1 for our baseline results not sensitive Using the standard F.O.C.: For the U.S.: u l /u c = w = θ = w(1 τ)(1 l) 1/ɛ /c c w(1 l), τ.387, l.798 = θ 14.97

29 Calibrating the Intercept in Utility Estimates of the value of remaining life for a U.S. 40-year old: Range from less than $2 million to more than $6 million. See Murphy and Topel (2006), Ashenfelter and Greenstone (2004), Viscusi and Aldy (2003), etc. We calibrate to $4 million in our baseline case This requires ū 5.54 if we normalize C us,2000 = 1 Note: ū raises the value of longevity relative to c, l.

30 Example: Indifference Between Life and Death If willing to slash c to fraction f to stay alive, then: ū + log(f c) + v(l) = 0 The solution for ū is then: ū = log(f c) v(l) ū 5.54 = f When consumption is 0.53% of the U.S. value, flow utility turns negative (at l us ).

31 Main Results

32 Key Point 1: (a) GDP per person highly correlated with welfare across the broad range of countries: (b) Nevertheless, differences are often important: typical deviation is 46%.

33 Welfare and Income Are Correlated 0.95 in 2000 Welfare, λ 1 1/4 1/16 1/64 1/256 1/1024 Sweden Luxembourg U.S. France Norway Malta Hong Kong Ireland Czech Rep. Singapore Costa Rica South Korea Tunisia Chile Bahamas Bosnia / Herz. Mexico Malaysia Jordan Venezuela Albania Russia Moldova Vietnam China Tajikistan South Africa India Bolivia Uzbekistan Djibouti Yemen Haiti Benin Madagascar Cote d Ivoire Lesotho Ethiopia Nigeria Tanzania Sierra Leone Zimbabwe Guinea Bissau Central African Republic Rwanda Somalia Zambia Botswana Namibia 1/64 1/32 1/16 1/8 1/4 1/2 1 GDP per person (US=1)

34 But Welfare typically differs from Income by about 46% The ratio of Welfare to Income Sweden France Malta Greece Germany U.K. Japan Albania Bosnia / Herz. Israel Austria Cyprus Jordan Macedonia Canada Costa Rica Bulgaria Slovenia Switzerland United States Tunisia Hong Kong Moldova Sri Lanka Portugal Egypt Mauritius Norway Tajikistan Hungary Romania Vietnam Nicaragua Ireland Estonia Puerto Rico Luxembourg Philippines Pakistan Chile Mexico South Korea Yemen India Brazil Ghana Bangladesh China Iran Malaysia Thailand Venezuela Singapore Nepal Bolivia Russia Madagascar Haiti Bahamas Guyana Niger Cambodia Gambia Turkmenistan Ethiopia Djibouti Tanzania Kenya Guinea South Africa Somalia Nigeria C.d Ivoire Rwanda Botswana Zambia Zimbabwe 1/64 1/32 1/16 1/8 1/4 1/2 1 GDP per person (US=1)

35 Key Point 2: Western Europe is much closer to the U.S. when we take into account Europe s longer life expectancy, additional leisure, and lower inequality.

36 U.S. vs. Western Europe in 2000 Decomposition Welfare Log Life λ Income Ratio Exp. C/Y Leis. Ineq. U.S France Western Europe s high taxes and generous social safety net may reduce work effort and GDP. But these programs have benefits that are not measured by GDP...

37 U.S. vs. W. Europe in VIIRXI\XMRHMGEXIWVE[HEXE Decomposition Welfare Log Life λ Income Ratio Exp. C/Y Leis. Ineq. U.S Germany France Italy U.K

38 Key Point 3: Many developing countries are much poorer than incomes suggest because of a combination of shorter lives and extreme inequality.

39 Welfare and Income, U.S. vs. Developed Asia in 2000 Decomposition Welfare Log Life λ Income Ratio Exp. C/Y Leis. Ineq. U.S Japan Hong Kong Singapore South Korea C/Y: 71% in Hong Kong vs. 43% in Singapore

40 Welfare and Income, U.S. vs. Emerging Asia in 2000 Decomposition Welfare Log Life λ Income Ratio Exp. C/Y Leis. Ineq. U.S Thailand Indonesia China India

41 Welfare and Income, Other Emerging Markets in 2000 Decomposition Welfare Log Life λ Income Ratio Exp. C/Y Leis. Ineq. U.S Mexico Brazil Russia

42 Welfare and Income, Sub-Saharan Africa in 2000 Decomposition Welfare Log Life λ Income Ratio Exp. C/Y Leis. Ineq. U.S South Africa Botswana Malawi South Africa: Life expectancy = 56 years factor of 4!

43 Key Point 4: Growth rates, Welfare: 4.0% Income: 3.0% Life expectancy adds more than 1.0%, except in Africa

44 Welfare, Income Growth Correlated 0.82 Welfare growth 8% 6% 4% 2% 0% 2% 4% Indonesia Egypt Malta Vietnam Albania Turkey Singapore Laos Japan India Malaysia Ireland Guinea Luxembourg Guatemala Brazil Norway Romania Netherlands Uganda Haiti Colombia Botswana Nicaragua Burkina Faso South Africa Cameroon Kenya Namibia Swaziland Nigeria Sierra Leone Somalia Cote d Ivoire Rwanda Zimbabwe Lesotho South Korea China 6% Zambia 8% 4% 2% 0% 2% 4% 6% 8% Per capita GDP growth

45 Welfare vs. Income Growth, ifference between Welfare and Income growth 6% 5% 4% 3% 2% 1% 0% 1% 2% 3% Albania Guatemala Turkey Indonesia Haiti El Salvador Nicaragua Egypt Romania Brazil Japan Hong Kong South Korea Niger Italy Sierra Leone Laos Vietnam Jordan India Malawi Thailand Cameroon U.S. Ireland Singapore China Denmark Somalia Malaysia Central African Uganda Kenya Republic Nigeria Luxembourg Zambia Rwanda Cote d Ivoire Zimbabwe Lesotho Swaziland Botswana 4% 4% 2% 0% 2% 4% 6% 8% Per capita GDP growth

46 Key Point 5: The mean absolute deviation between welfare growth and income growth is 1.15 percentage points.

47 Welfare vs. Income Growth, Regional Averages Decomposition Welfare Life Country λ Income Diff Exp. C/Y Leis. Ineq. CoastAsia W. Europe U.S L.A SSAfrica

48 Welfare vs. Income Growth, Growth Stars Decomposition Welfare Life Country λ Income Diff Exp. C/Y Leis. Ineq. S Korea , , , ,.481 China , , , ,.642 H.K , , , ,.763 Sing , , , ,.670 India , , , ,.669

49 Welfare vs. Income Growth, U.S. and OECD Decomposition Welfare Life Country λ Income Diff Exp. C/Y Leis. Ineq. Japan , , , ,.498 Italy , , , ,.539 France , , , ,.449 U.K , , , ,.524 U.S , , , ,.628

50 Welfare vs. Income Growth, U.S. and OECD Decomposition Welfare Life Country λ Income Diff Exp. C/Y Leis. Ineq. Japan , , , ,.498 Italy , , , ,.539 France , , , ,.449 U.K , , , ,.524 U.S , , , ,.628

51 Welfare vs. Income Growth, Developing Countries Decomposition Welfare Life Country λ Income Diff Exp. C/Y Leis. Ineq. Brazil , , , ,.892 Mexico , , , ,.872 Botswa , , , ,.878 SAfrica , , , ,1.140

52 Robustness Checks

53 Robustness Key Points are all qualitatively robust. Sensitivity of magnitudes in order of importance: CV versus EV ū U.S. value of life K/Y: current level versus steady state Coefficient of relative risk aversion Parameterization of utility from leisure

54 Robustness Summary Results # of countries Median absolute deviation with negative Robustness check Levels Growth rate flow utility Benchmark case Equivalent variation Compensating variation γ = 1.5, c = γ = 1.5, c = γ = 2.0, c = θ from FOC for France Frisch elasticity = Frisch elasticity = Value of Life = $3m Value of Life = $5m

55 Robustness France (y = 70.1) Welfare Log Decomposition Country λ Ratio L.E. C/Y Leis. Ineq. Benchmark case Equivalent variation Compensating variation γ = 1.5, c = γ = 1.5, c = γ = 2.0, c = θ from FOC for France Frisch elasticity = Frisch elasticity = Value of Life = $3m Value of Life = $5m

56 Baseline Welfare Measure, 2000 Welfare, λ 1 1/4 1/16 1/64 1/256 1/1024 Sweden Luxembourg U.S. France Norway Malta Hong Kong Ireland Czech Rep. Singapore Costa Rica South Korea Tunisia Chile Bahamas Bosnia / Herz. Mexico Malaysia Jordan Venezuela Albania Russia Moldova Vietnam China Tajikistan South Africa India Bolivia Uzbekistan Djibouti Yemen Haiti Benin Madagascar Cote d Ivoire Lesotho Ethiopia Nigeria Tanzania Sierra Leone Zimbabwe Guinea Bissau Central African Republic Rwanda Somalia Zambia Botswana Namibia 1/64 1/32 1/16 1/8 1/4 1/2 1 GDP per person (US=1)

57 Equivalent Variation, 2000 Welfare, λ 1 1/2 1/4 1/8 1/16 1/32 1/64 1/128 Luxembourg Sweden France Germany Italy Japan Belgium Austria Netherlands Norway United Australia Canada Iceland Switzerland Kingdom United States Spain Denmark Hong Kong Finland Malta Greece Cyprus Israel New Zealand Ireland Slovenia Portugal Puerto Rico Mauritius Czech Republic Singapore Slovak Hungary Republic South Korea Costa Croatia Poland Rica Belarus Chile Bahamas Tunisia Lithuania Estonia Bosnia Macedonia Bulgaria Malaysia Trinidad &Tobago and Latvia Herzegovina Mexico Dominican Jamaica Jordan Brazil Republic Armenia Egypt Romania Sri Georgia Lanka Turkey Venezuela Kazakhstan Iran Albania Ecuador El Guatemala Ukraine Panama Fiji Salvador Colombia Russia Thailand Indonesia Paraguay Peru Moldova Azerbaijan Kyrgyzstan Philippines Turkmenistan China Algeria South Africa Pakistan India Honduras Bolivia Djibouti Tajikistan Vietnam Nicaragua Swaziland Botswana Bangladesh Mauritania Mongolia Guinea Ghana Uzbekistan Haiti Laos Senegal Nepal Cambodia Cameroon Guyana Namibia Yemen Cote d Ivoire Benin Lesotho Kenya Papua New Guinea Mali Gambia Sierra Mozambique Nigeria Leone Zimbabwe Niger Ethiopia Madagascar Uganda Burkina Faso Malawi Guinea Bissau Burundi Rwanda Tanzania Central Zambia African Republic Somalia 1/64 1/32 1/16 1/8 1/4 1/2 1 GDP per person (US=1)

58 Compensating Variation, 2000 Welfare, λ 1 1/4 1/16 1/64 1/256 1/1024 1/4096 France Germany Belgium Australia Austria Canada Cyprus Greece Finland Denmark Hong Iceland Kong Israel Italy Luxembourg Japan Netherlands Norway Malta New Spain United Sweden Switzerland Kingdom United States Zealand Ireland Czech Portugal Slovenia Republic Puerto Rico Mauritius Singapore Costa Slovak Croatia Rica Hungary Republic Poland Chile South Korea Belarus Bahamas Bosnia Macedonia Bulgaria and Tunisia Herzegovina Lithuania Estonia Dominican Jamaica Latvia Mexico Malaysia Trinidad &Tobago Jordan Brazil Republic Albania Armenia Egypt Georgia Romania Sri Ecuador Lanka Turkey Panama Venezuela El Guatemala Ukraine Fiji Salvador Colombia Kazakhstan Iran Moldova Indonesia Azerbaijan Kyrgyzstan Philippines Paraguay Peru Thailand Russia China Algeria Vietnam Turkmenistan Nicaragua Honduras Tajikistan Pakistan India Bolivia South Africa Uzbekistan Mongolia Bangladesh Mauritania Guyana Djibouti Laos Senegal Nepal Yemen Ghana Haiti Cambodia Guinea Namibia Swaziland Papua New Guinea Botswana Gambia Benin Kenya Cameroon Madagascar Niger Mali Lesotho Cote d Ivoire Ethiopia Burkina Faso Nigeria Tanzania Uganda Mozambique Malawi Burundi Zimbabwe Guinea Bissau Sierra Leone Central African Republic Somalia Rwanda Zambia 1/64 1/32 1/16 1/8 1/4 1/2 1 GDP per person (US=1)

59 Adjusting the Consumption Share for Transition Dynamics (C / Y) ss Rwanda Ghana Sierra Leone Senegal Guinea Bangladesh Madagascar Haiti Ethiopia Vietnam Mauritius Malawi Gambia Burundi Guatemala Cameroon Nepal Bolivia Zimbabwe Djibouti Bahamas Philippines Costa Rica United States Paraguay Mauritania Portugal Greece South Africa Brazil China India Colombia Ireland Germany South Korea Somalia Luxembourg Sweden Mongolia Botswana Singapore Bulgaria Japan Malaysia Thailand Norway Iraq Venezuela Romania Guyana Algeria C / Y

60 Robustness: Inferring C/Y from K/Y, 2000 Rising I/Y (C/Y) adj > C/Y Per capita Benchmark Welfare w/ Benchmark Adjusted Country Income welfare C/Y adj. C/Y C/Y U.S Germany France Japan U.K Hong Kong Singapore S Korea China India

61 Micro Calculations

62 Micro Calculations Household Surveys for various country-years Household expenditures Age, Hours Worked of each household member Have analyzed micro data for: U.S. ( ) France ( ) India ( ) Mexico ( ) South Africa (1993)

63 10 Advantages to Micro Calculations Make sure consumption (not income) inequality Allow arbitrary (non-normal) distribution of consumption Drop durables (lumpy) Individual (rather than household) consumption Better measure of hours worked if non-oecd Incorporate inequality in leisure Adjust for age composition of population Incorporate survival rates by age Uniform use of sampling weights Allow government consumption to lower inequality (if desired)

64 Theory for the Micro Calculation Basic notation: a age j people within age group S i a Probability of surviving to age a in country i Mortality notation s us a Sus a a Sus a s i a Si a S a us a Sus a Demographically-adjusted averages: c i s us a ω jac i i ja a j l i a s us a ω jal i i ja j

65 Micro Welfare Decomposition log λ i = a si au i a Life Exp. + log c i log c us Consumption +v( l i ) v( l us ) Leisure +E log c i log c i (E log c us log c us ) Cons. Ineq. +Ev(l i ) v( l i ) ( Ev(l us ) v( l us ) ) Leis. Ineq.

66 Micro Calculations: Levels Decomposition Welfare Log Life Cons Leis λ Income Ratio Exp. C/Y Leis. Ineq Ineq France (macro) India (macro) Mexico (macro) S Africa (macro)

67 Micro Calculations: Growth Rates Decomposition Welfare Income Life Cons. Leis. Growth Growth Diff Exp. C/Y Leis. Ineq. Ineq France (macro) India (macro) Mexico (macro) U.S (macro)

68 Conclusions Income and welfare are highly correlated in both levels and growth rates. Nevertheless, differences between income and welfare are often economically important: Western Europe looks much closer to U.S. living standards. Most other countries are further behind, primarily due to lower life expectancy. Longer lives add over one percentage point, on average, to welfare growth per year