aggregate happiness rises with GDP for low-income countries, there is much less consensus on the magnitude of this
relationship, or on whether a satiation point exists beyond which further increases in GDP per capita are associated with
no change in aggregate happiness (Deaton, 2008).7
The early cross-country studies of income and happiness tended to be based on only a handful of countries, often
with rather similar income per capita, and hence did not lend themselves to definitive findings. In addition, as the
relationship between subjective well-being and the log of income is approximately linear, the analysis in terms of absolute
levels of GDP per capita likely contributed to the lack of clarity around the relationship between income and happiness
among wealthier countries. As we will show, new large-scale datasets covering many countries point to a clear, robust
relationship between GDP per capita and average levels of subjective well-being in a country. Furthermore, we find no
evidence that countries become satiated—the positive income-happiness relationship holds for both developed and
developing nations.
Our macroeconomic analysis focuses on measures of real GDP per capita measured at purchasing power parity.
For most countries we use the most recent data from the World Bank’s World Development Indicators database; where
we are missing data, we refer to the Penn World Tables (version 6.2) and, failing that, the CIA Factbook. For earlier years
we use data from Maddison (2007).8 The average of log income per person may be a more desirable aggregate than the
log of average income, and so in some specifications we also account for the difference between these measures (also
known as the mean log deviation).
Measuring average levels of subjective well-being is somewhat more difficult, as this typically involves
aggregating individual responses to a qualitative question. Moreover, we wish to make comparisons across surveys that
contain subjective well-being questions with varying numbers of categories for the responses. To do this, we need to
convert the subjective well-being measures to a normalized measure, which we do through the use of ordered probit
regressions of happiness on a series of country (or country-year) fixed effects (with no other controls), and then treat these
fixed effects as average levels of well-being within a country (or country-year).9 Appendix A compares our ordered probit
index with four alternative approaches to cardinalizing both life satisfaction and happiness, demonstrating that these
alternatives yield highly correlated well-being aggregates. The distinct advantage of the ordered probit is that coefficients
can be interpreted relative to the dispersion of the distribution of latent well-being in the population. As such, our ordered
probit index should be interpreted as highlighting differences in average levels of happiness or life satisfaction between
countries, relative to the pooled within-country standard deviation.
7 Deaton finds no evidence of a satiation point. His analysis of the 2006 Gallup World Poll finds a strong relationship between log
GDP and happiness that is, if anything, stronger among high-income countries.
8 When filling in missing years, we interpolate using the annual percentage changes listed in the Penn World Tables. When filling in
missing countries, we apply the ratio of a country’s GDP per capita to U.S GDP per capita, using data from the Penn World Tables or
the CIA Factbook, to the World Bank data.
9 Throughout, we use suggested surveys weights to ensure our estimates are nationally representative for each country in each wave.
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