Usefulness of GDP, Inflation and Unemployment Statistics for Comparing Living Standards

Question

In 2015, Singapore’s GDP per capita was US$70,704 at 2010 prices, one of the highest in the world. Singapore also experienced deflation of 0.5%, while overall unemployment stood at 1.9%.

Assess the usefulness of the above statistics in comparing the standard of living in Singapore with that of other countries. [15]

Answer

Standard of living (SOL) is the level of economic and social well-being enjoyed by an average resident, comprising a material aspect — the quantity and quality of goods and services available for consumption — and a non-material aspect — the intangible quality of life. This essay assesses how useful real GDP per capita (in US$, at constant prices), the inflation rate and the unemployment rate are for comparing living standards across countries. My thesis is that, on their own, these statistics are not sufficiently useful, because they neither correct for cross-country distortions in the material measure nor capture the non-material dimension.

The statistics do carry useful information. GDP is the market value of final goods and services produced within a country, and so is a reasonable proxy for the quantity available for consumption — an indicator of material well-being. Expressing it per capita corrects for population differences, giving an average share; expressing it in a common currency (US$) allows cross-country comparison; and measuring it at constant prices removes inflation, as the real-versus-nominal distinction requires. The unemployment rate carries non-material information too: a low rate (Singapore’s 1.9%) is associated with socio-political stability and lower crime. The inflation rate informs the cost of living — though, as a rate, it is only useful in tandem with a national-income figure (to compute real GDP), not alone.

But the material measure is distorted across countries. National income statistics have well-known limitations. GDP per capita at market exchange rates fluctuates with speculation and intervention, so the figure changes arbitrarily as rates move, and — more importantly — it ignores differences in the cost of living. The correction is purchasing power parity (PPP): once cost-of-living differences are taken in, a country that looked poorer on market rates may in fact enjoy higher material well-being. Evaluation: PPP data are not readily available — they must be specially constructed around a common basket, which is hard when consumption patterns differ (rice is a staple in Asia but not the West). The given statistics also ignore the distribution of income: two countries with equal GDP per capita can have very different living standards if one is far more unequal, so the Gini coefficient is needed alongside. And they ignore differences in the size of the non-monetised sector: developing economies with subsistence farming and unpaid family labour have their true output understated.

And the non-material dimension is missed entirely. None of the three statistics captures the non-material aspects of well-being. Higher output is often accompanied by greater negative externalities of production — pollution, congestion, resource depletion — that lower the intangible quality of life, and by disamenities such as longer working hours and higher stress. A higher GDP per capita driven by overwork does not signify a better life.

Judgement. On their own, these statistics are not useful enough for cross-country SOL comparison: they require correction (PPP, the Gini coefficient) even to compare the material aspect fairly, and they say nothing about the non-material aspect. For the material dimension, real GDP per capita in PPP terms together with the Gini coefficient would be far more reliable; for the non-material dimension, separate indicators (pollution, leisure hours) are needed. No single statistic gives an all-round comparison, which is why composite measures such as the Human Development Index — folding in education and life expectancy alongside income — are easier to track across countries. A final caveat: for developing countries, even where the right indicators are chosen, the reliability of the underlying data is itself a constraint on the accuracy of any comparison. The given statistics are therefore a starting point, not a sufficient basis, for comparing living standards across space.