The need to measure a country’s economic performance, both compared to itself a year ago and compared to other countries is ever present. The most prevalent and easiest measure of economic performance at the national level is a country’s GDP (Gross domestic Product) per capita. With plenty of well-established rules and norms for its calculation, as well as the data inputs required for its calculations, almost all countries publish annual GDP figures and use them as a performance measure.
However, just as well-documented are the multitude of potential problems with using GDP. For example, GDP figures can be skewed by the presence of one major industry (such as oil in a number of Middle Eastern countries), does not take into account inequality within a country, and usually excludes domestic and black market production.
Due to these shortcomings, there have been a few efforts to come up with a better measure. The most well-known is probably the Human Development Index, which combines a country’s GDP per capita, life expectancy, and a measure of education (previously literacy rate, but now based on years of schooling). However, this too is an imperfect measure – it uses an arbitrary weighting applied to each of its three factors, and for countries that already have a GDP per capita or life expectancy above a certain level, improvements in those factors do not result in an improvement in a country’s HDI score.
More esoteric attempts to measure well-being include those that use survey data to track well-being over time or across countries. One such example is Andy Oswald and Danny Blanchflower’s use of survey data regarding people’s use of anti-depressants and finds that there is an inverted U-shape with well-being reaching a nadir in a person’s late 40s. However, as ingenious as such approaches are, the data required generally doesn’t enable comprehensive comparisons across countries or time.
A recent paper by Jones & Klenow tries to bridge this gap – it first uses a small subset of 13 countries for which substantial household-level survey data are available in order to examine the relationship between living standards and GDP in those countries. Simply put, a country’s living standards are represented by a “random person” in that country’s (expected) utility, which incorporates that person’s consumption and leisure over that person’s expected lifetime. (This latter point means that life expectancy in a country is also important, while the use of a “random” person in a country means that inequality in both leisure and consumption can be important.)
The results are illuminating – if one were to just focus on GDP, then European countries such as France, Spain and Italy would appear to be far below the US. However, once the other important lifestyle factors are taken into account, then living standards in the UK and France are pretty much the same as they are in the US, while those in Italy and Spain are not too far behind. Most of this increase comes from the inclusion of higher life expectancy and more leisure time in the Western European countries as compared to the US. Moreover, living standards globally have increased by more than GDP has, almost entirely due to increases in life expectancy.
The paper then uses the relationship found over this subset of countries to “calibrate” a similar measure of living standards that use only only the data that are usually reported by organisations such as the UN, Penn World Tables, and the World Bank. Although this requires making a few strong assumptions (related to the distribution of consumption across individuals within a population etc.) the results are generally valid, such that living-standards can be calculated for a wider range of countries using those more available data.
The results at this level are similarly illuminating – countries in Western Europe are generally much closer to the US in terms of living standards than they are in terms of GDP. On the other hand, countries in other regions are generally further away from the US in terms of living standards than they are in terms of GDP – some notable “under-performers” in this respect are Botswana, Angola, and Chile.
The obvious omissions, about which the paper itself is explicit, include things such as personal freedoms, crime etc that are likely to be an important determinant of living standards within a country, but that are not taken account of in this measure (although note that they are also not taken into account in GDP either).
One major (perhaps less obvious) criticism is that the small subset of 13 countries in the initial investigation is predominantly made up of five high-income and five upper middle income countries with only two lower middle income countries and just one low income country. Hence, although it is reasonable to believe that the calibration check might be sufficient for the high-income and upper middle income countries, it is possible that a different relationship could exist for lower middle and low income countries, but the paper does not check this. (Other potential criticisms relate to the relatively small number of robustness checks carried out regarding the weighting of the future, or the various factors.)
Nonetheless, this doesn’t detract from the fact that the paper has provided a very interesting approach to taking into account living standards in a more complete manner than is currently provided for by the focus on GDP. Given the easy availability of the data that are included in the measure of living standards, and the relative ease of calculating the measure of living standards, it would behove countries and international organisations to start using this measure of performance alongside their current measures.