eco paper 12pages
YANGYANG
WellbeingGDP.pdf
Is GDP a Good Measure of Well-being?
Salvador Ortigueira (University of Miami)
January 12, 2017
Salvador Ortigueira (University of Miami)
Introduction
I Gross Domestic Product (GDP) is the market value of all final goods and services produced within a country in a given time period
I Typically, GDP is used as a measure of economic well-being in cross-country and across periods comparisons
I Many economists have criticized the use of GDP as a measure of economic well-being as it abstracts from
I Leisure
I Mortality
I Inequality, etc.
I So the question is: How good is GDP as a measure of economic well-being?
Salvador Ortigueira (University of Miami)
Two attempts at measuring well-being
I Objective: Construct a welfare measure that combines a more comprehensive list of variables associated with well-being
I Two attempts are: I The United Nations Human Development Index (HDI): This
index combines income, life expectancy and literacy. It first construct sub-indexes each variable and then averages them
I A consumption-equivalent measure of well-being. It combines data on consumption, leisure, inequality and mortality
Salvador Ortigueira (University of Miami)
The consumption-equivalent measure of well-being
I Example: Let us compare the US and France
I The consumption-equivalent measure of well-being in France is equal to the proportion of consumption in the US, given the US values of leisure, mortality and inequality, that would deliver the same expected utility as in France.
Salvador Ortigueira (University of Miami)
The consumption-equivalent measure of well-being
Let us start by looking at summary statistics for the three components of this measure
I Consumption
I Leisure
I Mortality rates
Salvador Ortigueira (University of Miami)
Within-country consumption inequality 2436 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
Consumption.—Figure 1 reports the standard deviation of log consumption across people in our household survey countries. We divide household expenditures equally across people in each household, and add per capita government consumption in the same year from the Penn World Tables 8.0 (Feenstra, Inklaar, and Timmer 2015). We use sampling weights and discount using US survival rates by age, analogous to the way we construct the mean of log consumption in equation (17). The resulting inequality is highest in South Africa, Brazil, and Mexico. Inequality is lower in France, Italy, and the United Kingdom than in the United States.
Leisure.—Figure 2 summarizes annual hours worked per person in our house- hold surveys. Figure 3 reports the standard deviation across people of annual hours worked.12 Hours worked are substantially lower in France, Italy, Spain, and the United Kingdom than in the United States, as has been widely noted. More novel, inequality of hours worked is lower in these same countries than in the United States.
Mortality Rates.—Figure 4 presents life expectancy in years from the World Health Organization for our baseline household survey years. It ranges from 50 in Malawi, the poorest country, to above 75 in the richest countries.
B. Calibration
To implement our welfare calculations, we need to specify the baseline utility function. (In Section IV we will explore a range of robustness checks to our choices
12 Parente, Rogerson, and Wright (2000) argue that barriers to capital accumulation explain some of the varia- tion in market hours worked. Like us, they emphasize that the gain in home production can partially offset the loss in market output. Prescott (2004) attributes some of the OECD differences in hours worked to differences in tax rates, as do Ohanian, Raffo, and Rogerson (2008).
Figure 1. Within-Country Inequality
Notes: The standard deviation of log consumption within each economy is measured from the household surveys listed in Table 1. We use survey-specific sampling weights and US survival rates across ages using an analog of equation (17), with no discounting or growth.
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Salvador Ortigueira (University of Miami)
Hours worked 2437JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
here.) Following the macro literature, we assume utility from leisure takes a form that implies a constant Frisch elasticity of labor supply (that is, holding the mar- ginal utility of consumption fixed, the elasticity of labor supply with respect to the wage is constant). Since labor supply in our setting is 1 − ℓ , in terms of the utility function in equation (4) this gives v(ℓ) = − θϵ _
1 + ϵ (1 − ℓ) 1+ϵ _ ϵ , where ϵ denotes the
Frisch elasticity. This leaves five parameters to be calibrated: the growth rate g , the
Figure 2. Annual Hours Worked across Countries
Notes: The measure shown here of annual hours worked per capita is computed from the household surveys noted in Table 1, using survey-specific sampling weights and US survival rates across ages as in equation (16), with no time discounting.
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Figure 3. Inequality in Annual Hours Worked
Note: See notes to Figure 2.
Salvador Ortigueira (University of Miami)
Inequality in hours worked
2437JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
here.) Following the macro literature, we assume utility from leisure takes a form that implies a constant Frisch elasticity of labor supply (that is, holding the mar- ginal utility of consumption fixed, the elasticity of labor supply with respect to the wage is constant). Since labor supply in our setting is 1 − ℓ , in terms of the utility function in equation (4) this gives v(ℓ) = − θϵ _
1 + ϵ (1 − ℓ) 1+ϵ _ ϵ , where ϵ denotes the
Frisch elasticity. This leaves five parameters to be calibrated: the growth rate g , the
Figure 2. Annual Hours Worked across Countries
Notes: The measure shown here of annual hours worked per capita is computed from the household surveys noted in Table 1, using survey-specific sampling weights and US survival rates across ages as in equation (16), with no time discounting.
1/64 1/32 1/16 1/8 1/4 1/2 1 500
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China
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Figure 3. Inequality in Annual Hours Worked
Note: See notes to Figure 2. Salvador Ortigueira (University of Miami)
Life expectancy 2438 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
discount factor β , the Frisch elasticity ϵ , the utility weight on leisure or home pro- duction θ , and the intercept in flow utility u – .
We choose a common growth rate of 2 percent per year. An alternative would be to try to forecast future growth rates for each country, but such forecasts would have very large standard errors, particularly since we would need forecasts for every year over the next century. With an annual real interest rate of 4 percent in mind, we set the discount factor to β = 0.99 . Recall that there is already additional discounting inherent in the expected utility calculation because of mortality. A 4 percent real interest rate is consistent with the standard Euler equation with log preferences, 2 percent consumption growth, roughly 1 percent discounting for mortality, and 1 percent from the discount factor.
Surveying evidence such as Pistaferri (2003), Hall (2009a, b) suggests a bench- mark value for the Frisch elasticity of 0.7 for the intensive (hours) margin and 1.9 for the extensive and intensive margins combined. Chetty (2012) reconciles micro and macro estimates of the Frisch elasticity and recommends a value of 0.5 or 0.6 for the intensive margin. We consider a Frisch elasticity of 1.0 for our benchmark calibration, which implies that the disutility from working rises with the square of the number of hours worked. As we discuss in the robustness section, the results are not sensitive to this choice.
To get the weight on the disutility from working, θ , recall that the first-order condition for the labor-leisure decision is u ℓ / u c = w(1 − τ) , where w is the real wage and τ is the marginal tax rate on labor income. Our functional forms then imply θ = w (1 − τ) (1 − ℓ) −1/ϵ /c . For our benchmark calibration, we assume this first-order condition holds for the average prime-age worker (25–55 years old) in the US Consumer Expenditure Survey (CE) in 2006. We take the marginal tax rate in the United States from Barro and Redlick (2011), who report a value of 0.353 for 2006. Taking into account the ratio of earnings to consumption and average leisure
1/64 1/32 1/16 1/8 1/4 1/2 1 GDP per person
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Figure 4. Life Expectancy
Note: Life expectancy at birth in each country is measured as the sum over all ages of the probability of surviving to each age, using life tables from the World Health Organization.
Salvador Ortigueira (University of Miami)
Welfare across countries and over time 2440 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
welfare measures 22 percent higher than their incomes. The remaining countries, in contrast, have welfare levels that are typically 25 to 50 percent below their incomes. The way to reconcile these large deviations with the high correlation between wel- fare and income is that the “scales” are so different. Incomes vary by more than a factor of 64 in our sample, i.e., 6,300 percent, whereas the deviations are on the order of 25 to 50 percent.
KEY POINT 2: Average Western European living standards appear much closer to those in the United States when we take into account Europe’s longer life expec- tancy, additional leisure time, and lower levels of inequality.
Figure 5. Welfare and Income across Countries
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Panel A. Welfare and income are highly correlated at 0.98
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Panel B. But this masks substantial variation in the ratio of λ to GDP per capita
Salvador Ortigueira (University of Miami)
Welfare across countries 2442 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
penalized less.15 As we will show, this is a key place where the equivalent variation differs from the compensating variation. The compensating variation weights differ- ences in mortality by US flow utility. In the robustness section, we’ll see this leads to much larger welfare differences.
A second reason that welfare is lower than income in several countries is that average consumption—as a share of income—is low relative to the United States. Utility depends on consumption, not income. Of course, an offsetting effect is that the low consumption share may raise consumption in the future. To the extent that countries are close to their steady states, this force is already incorporated in our calculation. However, in countries with upward trends in their investment rates, our calculation will understate steady-state welfare. China is an obvious candidate for this qualification, though correcting for this has a modest effect.16
15 Table A3 in the online Appendix reports the implied value of life in each of our 13 countries. 16 See Table 8 of Jones and Klenow (2010).
Table 2—Welfare across Countries
Decomposition
Welfare λ Income log ratio Life exp. C/Y Leisure Cons. ineq.
Leis. ineq.
US 100.0 100.0 0.000 0.000 0.000 0.000 0.000 0.000 77.4 0.897 877 0.538 1,091
UK 96.6 75.2 0.250 0.086 −0.143 0.073 0.136 0.097 78.7 0.823 579 0.445 826
France 91.8 67.2 0.312 0.155 −0.152 0.083 0.102 0.124 80.1 0.790 535 0.422 747
Italy 80.2 66.1 0.193 0.182 −0.228 0.078 0.086 0.075 80.7 0.720 578 0.421 905
Spain 73.3 61.1 0.182 0.133 −0.111 0.070 0.017 0.073 79.1 0.786 619 0.541 904
Mexico 21.9 28.6 −0.268 −0.156 −0.021 −0.010 −0.076 −0.005 74.2 0.879 906 0.634 1,100
Russia 20.7 37.0 −0.583 −0.501 −0.248 0.035 0.098 0.032 67.1 0.733 753 0.489 1,027
Brazil 11.1 17.2 −0.436 −0.242 0.004 0.005 −0.209 0.006 71.2 0.872 831 0.724 1,046
S. Africa 7.4 16.0 −0.771 −0.555 0.018 0.054 −0.283 −0.006 60.9 0.887 650 0.864 1,093
China 6.3 10.1 −0.468 −0.174 −0.311 −0.016 0.048 −0.014 71.7 0.658 888 0.508 1,093
Indonesia 5.0 7.8 −0.445 −0.340 −0.178 −0.001 0.114 −0.041 67.2 0.779 883 0.445 1,178
India 3.2 5.6 −0.559 −0.440 −0.158 −0.019 0.085 −0.028 62.8 0.785 918 0.438 1,143
Malawi 0.9 1.3 −0.310 −0.389 0.012 −0.020 0.058 0.028 50.4 0.923 934 0.533 997
Notes: The table shows the consumption-equivalent welfare calculation based on equation (19). See Table 1 for sources and years. The second line for each country shows life expectancy, the ratio of consumption to income, annual hours worked per capita, the standard deviation of log consumption, and the standard deviation of annual hours worked, all computed from the cross-sectional micro data, with no discounting or growth.
Salvador Ortigueira (University of Miami)
Welfare across countries 2442 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
penalized less.15 As we will show, this is a key place where the equivalent variation differs from the compensating variation. The compensating variation weights differ- ences in mortality by US flow utility. In the robustness section, we’ll see this leads to much larger welfare differences.
A second reason that welfare is lower than income in several countries is that average consumption—as a share of income—is low relative to the United States. Utility depends on consumption, not income. Of course, an offsetting effect is that the low consumption share may raise consumption in the future. To the extent that countries are close to their steady states, this force is already incorporated in our calculation. However, in countries with upward trends in their investment rates, our calculation will understate steady-state welfare. China is an obvious candidate for this qualification, though correcting for this has a modest effect.16
15 Table A3 in the online Appendix reports the implied value of life in each of our 13 countries. 16 See Table 8 of Jones and Klenow (2010).
Table 2—Welfare across Countries
Decomposition
Welfare λ Income log ratio Life exp. C/Y Leisure Cons. ineq.
Leis. ineq.
US 100.0 100.0 0.000 0.000 0.000 0.000 0.000 0.000 77.4 0.897 877 0.538 1,091
UK 96.6 75.2 0.250 0.086 −0.143 0.073 0.136 0.097 78.7 0.823 579 0.445 826
France 91.8 67.2 0.312 0.155 −0.152 0.083 0.102 0.124 80.1 0.790 535 0.422 747
Italy 80.2 66.1 0.193 0.182 −0.228 0.078 0.086 0.075 80.7 0.720 578 0.421 905
Spain 73.3 61.1 0.182 0.133 −0.111 0.070 0.017 0.073 79.1 0.786 619 0.541 904
Mexico 21.9 28.6 −0.268 −0.156 −0.021 −0.010 −0.076 −0.005 74.2 0.879 906 0.634 1,100
Russia 20.7 37.0 −0.583 −0.501 −0.248 0.035 0.098 0.032 67.1 0.733 753 0.489 1,027
Brazil 11.1 17.2 −0.436 −0.242 0.004 0.005 −0.209 0.006 71.2 0.872 831 0.724 1,046
S. Africa 7.4 16.0 −0.771 −0.555 0.018 0.054 −0.283 −0.006 60.9 0.887 650 0.864 1,093
China 6.3 10.1 −0.468 −0.174 −0.311 −0.016 0.048 −0.014 71.7 0.658 888 0.508 1,093
Indonesia 5.0 7.8 −0.445 −0.340 −0.178 −0.001 0.114 −0.041 67.2 0.779 883 0.445 1,178
India 3.2 5.6 −0.559 −0.440 −0.158 −0.019 0.085 −0.028 62.8 0.785 918 0.438 1,143
Malawi 0.9 1.3 −0.310 −0.389 0.012 −0.020 0.058 0.028 50.4 0.923 934 0.533 997
Notes: The table shows the consumption-equivalent welfare calculation based on equation (19). See Table 1 for sources and years. The second line for each country shows life expectancy, the ratio of consumption to income, annual hours worked per capita, the standard deviation of log consumption, and the standard deviation of annual hours worked, all computed from the cross-sectional micro data, with no discounting or growth.
Salvador Ortigueira (University of Miami)
Main conclusions so far
I GDP per person is an excellent indicator of welfare across the broad range of countries: the two measures have a correlation coefficient of 0.98. Nevertheless, for any given country, the difference between the two measures can be important. Across the 13 countries analyzed, the median deviation is about 35 percent.
I Average Western European living standards appear much closer to those in the United States when we take into account Europe’s longer life expectancy, additional leisure time, and lower levels of inequality.
I Many developing countries, including all eight of the non-European countries in the sample, are poorer (in welfare) than incomes suggest because of a combination of shorter lives, low consumption shares, and extreme inequality.
Salvador Ortigueira (University of Miami)
Welfare and income growth 2444 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
person rose from 810 to 889 between 1984 and 2006. We estimate that this falling leisure reduced consumption-equivalent welfare growth by about a tenth of a per- centage point per year. According to the CE, consumption inequality rose, reducing growth by another 24 basis points.17 Finally, rising leisure inequality reduces US welfare growth another 8 basis points. Taken together, these three channels reduce consumption-equivalent welfare growth in the United States by 42 basis points per year.
Mexico and Italy exhibit similar patterns. Falling leisure reduces welfare growth by 0.17 percentage points per year in Italy and 0.23 percentage points per year in
17 The CE displays a relatively small increase in consumption inequality, as emphasized by Krueger and Perri (2006). According to Aguiar and Bils (2015), savings and Engel curves in the CE suggest that consumption inequal- ity rose as much as income inequality in the United States over this period.
Figure 6. Welfare and Income Growth (Percent)
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Panel A. The correlation between welfare growth and income growth is 0.97
Panel B. The median absolute value of the difference between welfare and income growth is 0.95 percentage points
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Salvador Ortigueira (University of Miami)
Welfare and income growth 2445JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
Mexico. Rising consumption and leisure inequality, combined, reduce growth by 0.46 and 0.30 percentage points per year in Italy and Mexico. The sum of these three forces is therefore about 0.63 percentage points per year in Italy and 0.53 percentage points per year in Mexico.
IV. Robustness
Here we gauge robustness to alternative assumptions, such as about the utility function. Table 4 shows that the gap we find between welfare and income is quite robust. More detailed results—including decompositions for France, China, and Indonesia—are available in the online Appendix.
The second row of Table 4 indicates that, if we do not discount or incorporate growth, the differences between welfare and income are somewhat smaller than in the baseline case (median absolute deviation between welfare and income of 30 percent rather than 35 percent). The reason is that life expectancy differences are less important if consumption is not growing over the life cycle (a bigger effect at 2 percent per year than the pure time discounting of 1 percent per year). If we retain growth but discount time more heavily at 4 percent per year rather than 1 percent per year, as shown in the third row, the median gap between welfare and income shrinks a little further to 28 percent.
Table 3—Welfare Growth
Decomposition
Welfare growth
Income growth Diff
Life exp. c/y Leis.
Cons. ineq.
Leis. ineq.
Russia 8.10 9.23 −1.13 0.93 −1.53 −0.29 −0.02 −0.22 (1998–2007) 65.5, 67.1 0.842, 0.745 707, 801 0.469, 0.498 997, 1,043 Brazil 4.63 3.71 0.92 1.54 −0.84 −0.06 0.06 0.23 (2003–2008) 71.2, 72.9 0.865, 0.829 845, 854 0.722, 0.720 1,050, 1,021 UK 4.42 3.12 1.30 1.16 0.12 −0.01 −0.02 0.05 (1985–2005) 75.4, 78.7 0.793, 0.827 588, 596 0.391, 0.447 860, 832 India 4.08 4.05 0.03 1.14 −1.04 0.04 −0.13 0.02 (1983–2005) 57.6, 62.8 0.973, 0.768 964, 952 0.416, 0.429 1,156, 1,149 France 3.15 2.15 1.00 1.04 0.10 −0.05 −0.16 0.07 (1984–2005) 77.1, 80.1 0.782, 0.790 480, 534 0.391, 0.422 793, 747 US 3.09 2.11 0.98 0.89 0.51 −0.10 −0.24 −0.08 (1984–2006) 75.0, 77.4 0.812, 0.892 810, 889 0.508, 0.539 1,054, 1,094 Italy 2.73 2.02 0.72 1.33 0.03 −0.17 −0.24 −0.22 (1987–2006) 76.6, 80.7 0.728, 0.719 410, 587 0.382, 0.421 782, 909 Indo. 2.65 0.39 2.25 1.43 0.81 0.18 −0.16 −0.00 (1993–2006) 62.3, 67.2 0.705, 0.780 976, 912 0.421, 0.445 1,188, 1,193 Mexico 1.87 1.05 0.82 1.09 0.26 −0.23 −0.16 −0.14 (1984–2006) 70.8, 74.2 0.838, 0.872 754, 909 0.663, 0.631 1,045, 1,101 Average 3.86 3.09 0.77 1.17 −0.17 −0.08 −0.12 −0.03 Averag e ∗ 3.14 2.13 1.02 1.15 0.11 −0.05 −0.16 −0.04 Notes: The table shows a decomposition for average annual consumption-equivalent welfare growth based on equa- tion (20). Years are shown in parentheses. Average denotes the average across the nine countries, while Average ∗ excludes Russia and Brazil. The second line for each country displays the raw data on life expectancy, the ratio of consumption to income, annual hours worked per capita, the standard deviation of log consumption, and the stan- dard deviation of annual hours worked, for the start and ending year, computed with no discounting or growth.
Salvador Ortigueira (University of Miami)
Main conclusion on welfare and income growth
I Welfare growth averages 3.1 percent between the 1980s and mid-2000s, versus income growth of 2.1 percent. A boost from rising life expectancy of about 1 percentage point per year accounts for the difference.
Salvador Ortigueira (University of Miami)
Welfare and income across countries: Macro data 2451JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
also low in Singapore and South Korea, further reducing welfare relative to income. Working hard and investing for the future are well-established means of raising GDP. Nevertheless, these approaches have costs that are not reflected in GDP.
Botswana and South Africa.—According to GDP per capita, these are relatively rich developing countries with about 20 percent of US income. AIDS, however,
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Angola
Albania
Bangladesh
Bahrain Bahamas
Bosnia/Herz.
Brazil
Botswana
Chile
Comoros
Egypt Fiji
France
Gabon
H.K.
Honduras
Ireland
Iraq
Iceland
Israel Italy
Jamaica
Kyrgyzstan
S. Korea Kuwait
Lao
Luxembourg
Macao
Moldova
Madagascar
Mali Malawi
Malaysia
Namibia
Niger
Nigeria
Norway
Oman
Poland
Portugal Qatar
Saudi Arabia
Singapore
Sao Tome/Principe
Slovakia
Sweden
Togo
Turkmenistan
Uganda
United States
Vietnam South Africa
Zambia
Zimbabwe
Costa Rica
GDP per person (US = 1)
GDP per person (US = 1)
We lfa
re, λ
Panel A. Welfare and income are highly correlated at 0.96
0
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Angola
Albania
Armenia
Australia Austria
Bangladesh
Bahrain
Bahamas
Bosnia/Herz.
Bolivia
Barbados
Botswana
C. Afr. Rep. Switzerland
Chile
China
Cote dIvoire Cameroon
Congo
Colombia
Comoros Costa Rica
Cyprus
Denmark Egypt
Spain
Estonia
Ethiopia
Fiji
France
Gabon
U.K.
Georgia
Guinea
Greece
Guatemala
Hong Kong
Honduras
Croatia
Indonesia Ireland
Iran
Iceland
Israel Jamaica Jordan
Kenya
Kyrgyzstan
S. Korea
Kuwait
Liberia
Saint Lucia
Luxembourg Latvia
Macao
Morocco Mexico
Malta
Malawi
Namibia
Niger
Nigeria
Norway
Oman
Peru
Poland
Portugal
Qatar
Russian Fed.
Saudi Arabia
Sudan
Singapore
Sierra Leone
Serbia Sao Tome/P.
Sweden
Chad
Togo
U.S.
St. Vincent
Venezuela
South Africa
Zambia
Zimbabwe
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Panel B. But this masks substantial variation in the ratio of λ to GDP per capita. The mean absolute deviation from unity is about 27%
Liberia
Figure 7. Welfare Using Macro Data, 2007
Salvador Ortigueira (University of Miami)
Welfare and income across countries: Macro data 2453JONES AND KLENOW: WELFARE ACROSS COUNTRIES AND TIMEVOL. 106 NO. 9
AIDS in Africa.—Young (2005) pointed out that AIDS was an humanitarian trag- edy in Africa, but that it might boost GDP per worker by raising capital per worker. Our welfare measure provides one way of adding these two components together to measure the net cost. As Young suspected, the net cost proves to be substantial. Botswana loses the equivalent of 1.1 percentage points of consumption growth from seeing its life expectancy fall from 60.5 to 52.1 years, similar to the loss in South Africa. Botswana’s growth rate falls from one of the fastest in the world at 6.27 per- cent to the much more modest 2.94 percent. Already poor, sub-Saharan Africa falls
Table 7—Welfare across Countries in 2007: Macro Data
Welfare λ Per capita
income log ratio
Decomposition
Country LifeExp C/Y Leisure C ineq. United States 100.0 100.0 0.000 0.000 0.000 0.000 0.000
77.8 0.845 836 0.658
Sweden 91.2 79.4 0.139 0.181 −0.186 0.010 0.135 80.9 0.701 807 0.404
France 91.1 70.3 0.259 0.176 −0.085 0.063 0.106 80.8 0.776 629 0.471
Japan 82.6 71.3 0.147 0.265 −0.154 −0.028 0.063 82.5 0.724 912 0.554
Norway 81.0 112.8 −0.331 0.148 −0.598 0.019 0.100 80.4 0.464 780 0.483
Germany 77.4 74.4 0.039 0.098 −0.195 0.047 0.089 79.5 0.695 687 0.506
Ireland 69.6 96.4 −0.325 0.069 −0.454 −0.022 0.082 79.0 0.536 896 0.519
Hong Kong 59.0 83.4 −0.345 0.239 −0.433 −0.151 −0.000 82.4 0.548 1,194 0.658
Singapore 56.7 117.1 −0.726 0.139 −0.685 −0.180 −0.000 80.4 0.426 1,251 0.658
South Korea 45.3 58.3 −0.252 0.078 −0.290 −0.116 0.076 79.3 0.632 1,120 0.531
Argentina 21.8 26.2 −0.181 −0.121 −0.108 0.048 −0.000 75.1 0.759 684 0.658
Chile 19.7 30.9 −0.451 0.029 −0.254 −0.026 −0.199 78.5 0.655 908 0.912
Thailand 10.9 18.1 −0.507 −0.158 −0.207 −0.043 −0.099 73.5 0.687 951 0.794
South Africa 4.5 17.4 −1.351 −0.931 −0.053 0.061 −0.427 51.0 0.801 636 1.135
Botswana 4.3 25.1 −1.767 −0.852 −0.574 −0.008 −0.333 52.1 0.476 859 1.048
Vietnam 4.0 5.9 −0.378 −0.082 −0.269 −0.020 −0.006 74.2 0.645 893 0.668
Zimbabwe 3.1 8.3 −0.972 −0.983 0.155 −0.050 −0.094 45.8 0.986 969 0.789
Kenya 1.9 2.8 −0.388 −0.394 0.104 0.059 −0.157 54.4 0.938 644 0.865
Notes: The table shows the consumption-equivalent welfare calculation based on equation (7). The second line for each country shows life expectancy, the ratio of consumption to income, annual hours worked per capita, and the standard deviation of log consumption. Results for additional countries can be downloaded at http://www.stanford. edu/~chadj/BeyondGDP500.xls.
Salvador Ortigueira (University of Miami)
Welfare and income growth: 1980-2007 (percent) 2456 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2016
One could carry out similar calculations across geographic regions within coun- tries, or across subgroups of a country’s population (e.g., by gender or race). Even more ambitious would be to try to account for some of the many important fac- tors we omitted entirely, such as morbidity, the quality of the natural environment, crime, political freedom, and intergenerational altruism. We hope our simple mea- sure proves to be a useful building block for work in this area.
REFERENCES
Aguiar, Mark, and Mark Bils. 2015. “Has Consumption Inequality Mirrored Income Inequality?” American Economic Review 105 (9): 2725–56.
Barro, Robert J., and Charles J. Redlick. 2011. “Macroeconomic Effects from Government Purchases and Taxes.” Quarterly Journal of Economics 126 (1): 51–102.
Becker, Gary S., Tomas J. Philipson, and Rodrigo R. Soares. 2005. “The Quantity and Quality of Life and the Evolution of World Inequality.” American Economic Review 95 (1): 277–91.
Boarini, Romina, Asa Johansson, and Marco Mira d’Ercole. 2006. “Alternative Measures of Well- Being.” Organization for Economic Co-operation and Development Social, Employment and Migration Working Paper 33.
Chetty, Raj. 2012. “Bounds on Elasticities with Optimization Frictions: A Synthesis of Micro and Macro Evidence on Labor Supply.” Econometrica 80 (3): 969–1018.
Cordoba, Juan Carlos, and Genevieve Verdier. 2008. “Inequality and Growth: Some Welfare Calcula- tions.” Journal of Economic Dynamics and Control 32 (6): 1812–29.
Feenstra, Robert C., Robert Inklaar, and Marcel P. Timmer. 2015. “The Next Generation of the Penn World Table.” American Economic Review 105 (10): 3150–82.
Fleurbaey, Marc. 2009. “Beyond GDP: The Quest for a Measure of Social Welfare.” Journal of Eco- nomic Literature 47 (4): 1029–75.
Fleurbaey, Marc, and Guillaume Gaulier. 2009. “International Comparisons of Living Standards by Equivalent Incomes.” Scandinavian Journal of Economics 111 (3): 597–624.
Hall, Robert E. 2009a. “By How Much Does GDP Rise If the Government Buys More Output?” Brookings Papers on Economic Activity 40 (2): 183–231.
Hall, Robert E. 2009b. “Reconciling Cyclical Movements in the Marginal Value of Time and the Mar- ginal Product of Labor.” Journal of Political Economy 117 (2): 281–323.
Hall, Robert E., and Charles I. Jones. 2007. “The Value of Life and the Rise in Health Spending.” Quarterly Journal of Economics 122 (1): 39–72.
−4 −2 0
2
4
6
8
10
12
Albania
Bolivia
Brazil
Bhutan
Botswana
C. Afr. Republic
China
Cote dIvoire
Congo
Comoros
Cyprus
Ecuador
Egypt
Fiji
Guinea
H.K. India
Ireland
Iran
Iraq
Jamaica
Japan
Cambodia
S. Korea
Liberia
Luxembourg
Madagascar
Maldives
Niger
Nigeria Norway
Panama
Poland
Qatar
Saudi Arabia
S. Leone Swaziland
Chad
Turkey
S. Africa Zimbabwe
Macao
Per capita GDP growth
−6 −4 −2 0 2 4 6 8
W e lf a re
g ro
w th
Figure 8. Welfare and Income Growth, 1980–2007 (Percent)
Salvador Ortigueira (University of Miami)
