Measuring living standards – GDP or not GDP

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.

(Not) Eating for two: Fasting and educational attainment

Recent news regarding the possibility of moving the dates on which core GCSE exams are held forward so as to avoid Ramadan has highlighted the impact that fasting and nutrition can have on exam results and educational attainment.

The impact of fasting / poor nutrition during exam periods and/or while growing up has been subject to numerous studies before – see, for example, Anderson et al. and  Glewwe & Miguel.

However, what has received less attention (at least until now) is the impact of a mother’s fasting during pregnancy on a child’s educational attainment. That is why a paper by Douglas Almond, Bhashkar Mazumder, and Reyn van Ewijk in the latest issue of The Economic Journal is rather interesting – it looks at precisely this issue.

Specifically, the paper looks at the impact of Ramadan falling during pregnancy (i.e. the impact of fasting during the gestation period) on the educational attainment of Pakistani and Bangladeshi children (i.e. those children most likely to be of Muslim decent and, hence, whose mothers are most likely to have fasted during Ramadan) at age seven compared to that of other groups of children and finds that there is a small, but significant, decrease in educational attainment of the Pakistani and Bangladeshi children. The authors use this result to suggest that “brief prenatal investments may be more cost effective than traditional educational intervention in improving academic performance.

On the whole, the paper is a good one, following a clearly set out method, and the results provide useful policy indications. However, that is not to say that the paper does not have some flaws.

First, the paper uses the educational attainment of children of Caribbean decent as the “control group” against which the educational attainment of Pakistani and Bangladeshi children is tested. The authors justify this on the grounds that Caribbean families are unlikely to fast during Ramadan, such that the control group is unaffected by Ramadan. Although this might seem reasonable from a statistical perspective (although I’d argue that it still introduces unacceptable biases into the analysis), from a policy perspective it is less desirable.

Specifically, the relevant control group to determine whether a policy would be worthwhile is “the average student” – the authors do not provide any evidence to indicate that Caribbean students represent the average attainment in the UK. Indeed, the paper actually suggests that Caribbean students’ educational attainment tends to be below the UK average. In other words, the effect that the paper finds is likely to be understated relative to the average student, such that the paper’s conclusions could be much stronger if the average educational attainment was used. (Although the paper conducts a “robustness check” using White British students as an alternative control group, this still fails to get at the effect compared to the average student.)

Second, the paper assumes a “standard” gestation period of xx days, but does not investigate the extent to which changing the length of this gestation period affects the results. It could be that a slight change in the length of the gestation period assumed by the authors would affect the effect they find, which is important given that the effect they find is relatively small (albeit statistically significant). Hence, the rigour of the paper could have been improved by including this sensitivity check.

Third, the authors fail to make sufficient inference from the results presented in the paper. In particular, the paper’s results indicate that the impact of fasting on educational attainment differs according to the stage during the gestation period at which the fasting occurred – the impact of gestational fasting on educational attainment is largest when it happens during the third and fourth month of gestation and is almost negligible when it occurs after the seventh month. In other words, the paper could have highlighted the importance of early nutritional interventions for policy during gestation, but failed to do so.

Nonetheless, despite these flaws, the paper is an interesting one, with some important policy implications.