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.

The cost of Brexit (part 2 of who knows how many)

In response to the Treasury’s report on the costs of Brexit (and, obviously, to my blog post covering that report) a group calling themselves “Economists for Brexit” published a pamphlet which they claim contains a more reasonable estimate of the impact of Brexit on the UK economy.

Unsurprisingly, they find that, contrary to the Treasury’s report (and, indeed, the vast majority of economic reports published on this issue), that Brexit would benefit the UK economy by increasing GDP growth by about 0.5% points per year on average (with the majority of this increase coming in 2020, the final year of their forecast).

Equally unsurprisingly, their estimate is fundamentally flawed. In an impressive attempt to hide these flaws, the report contains only a two page summary of the model they have used to obtain their results, but even then the numerous flaws are apparent.

First, the report assumes that leaving the EU would mean that the UK would be able to remove EU-set trade barriers to non-EU countries, but would still keep the same terms-of-trade it currently has with EU countries. Moreover, it assumes that all trade barriers will reduce by half over the next five years. These assumptions drive the report’s “finding” that Brexit would increase UK living standards by 3.2% by 2020. However, the report does not provide any evidence to support the validity of either of these assumptions. Indeed, there is plenty of evidence to suggest that they are not valid – for example, they assume a rate of decrease in trade barriers not seen since the 1960s.

Second, the report assumes that the 0.8% GDP net saving from the UK not having to contribute to the EU budget would be passed-on entirely to taxpayers in the form of an income tax cut. This is extremely unlikely to happen – due to the current government’s austerity policies, any savings from Brexit are likely to be used to reduce the government deficit rather than hand out a (potentially politically damaging) tax cut.

Third, not only does the report assume that there would be a reduction in regulation if the UK were to leave the UK (which is an unproven assumption), the report then assumes that this reduction in regulation would have exactly the same effect as a 2% point decrease in the employer rate of NI. One hopes that those writing the point must have released how barmy such an assumption is – the report doesn’t contain even a passing attempt to justify how a decrease in regulation would have exactly the same impact as reduction in employer NI. Indeed, it is barely possible to conceive how anyone could think this was a reasonable assumption.

Anyway, moving on. Finally, the report assumes that the government deficit is unchanged due to the aforementioned assumptions resulting in the government’s revenues not changed. However, this fails to recognise the possibility that some of the money that was spent on EU goods and services previously could now be spent on UK goods and services, thereby potentially increasing tax receipts. Conversely, the report also assumes that non-UK people and businesses won’t decide to move away from the UK, which would result in a decrease in tax revenues.

And all of this is to say nothing of the fact that the report has excluded countless other factors that could be detrimental to the UK. For example, the report does not even mention the potential impact Brexit could have on immigration (note that the vast majority of studies find that immigration is beneficial for the country to which immigrants relocate and this is even true for low-skilled workers in that country). Nor does it cover the costs associated with the uncertainty that would be created and persist for a number of years regarding exactly what form of agreement between the UK and the EU would be put in place post-Brexit.

In essence, the study published by the “Economists for Brexit” group is so full of holes it is no surprise that they were only able to find eight professional economists to support it. Contrast this to the almost 200 economists (including yours truly) that are signatories to a letter in the Times stating that “[l]eaving would entail significant long-term costs.” That in itself should be damning enough.

The cost of Brexit

How much does the UK’s membership of the EU actually cost? And, in fact, does being in the EU represent a net economic benefit, rather than a net cost?

If you were to believe the information provided by Vote Leave, you might think you’d know that the answer. Vote Leave has claimed that membership of the EU costs the UK about £18 billion per year, the equivalent of about £280 per person per year. However, this figure does not include the substantial rebates and public/private sector receipts that the UK receives from the EU – once these are taken into account the actual direct budgetary cost of the UK’s membership of the EU is about £8.4 billion, or £131 per person, per year (i.e. less than half of the original Vote Leave claim).

Moreover, the Vote Leave figure only includes the direct budgetary costs of being part of the EU. Importantly, it does not include, nor does the Vote Leave campaign attempt to include, any “indirect” benefits that result from EU membership. Such indirect benefits include, for example, any jobs or exports resulting from trade with EU countries that would not otherwise occur absent EU membership. If membership of the EU increases UK output above what it would have been if the UK was not part of the EU (which is likely to be the case), then leaving the EU would result in a decrease in UK output.

This could happen for such wide-ranging reasons as EU consumers have more diverse tastes than just those in the UK allowing a larger number of different firms to flourish in the UK and export their output to the EU than would otherwise prevail if the UK left the EU and UK firms would have reduced demand from EU countries; or collaboration between EU and UK firms enables a wider spread of technology that would not be possible after Brexit such that UK productivity is higher than it would be outside the EU; or membership of the EU encourages investment not just from EU firms but from firms located in the Rest of the World that would not occur if the UK were to leave the EU. There are plenty of other potential mechanisms through which EU membership increases UK output.

Importantly, although Vote Leave has not attempted to include such factors, the Centre for Economic Performance (CEP) has done so and finds that leaving the EU would reduce the UK’s output by at least £850 per household per year. That is the best case scenario for the Vote Leave supporters. Note, too, that this only includes “static trade consequences” – i.e. the impact that can be attributed just to losing the ability to trade freely with EU countries; it does not include any of the costs associated with reduced migration, technology transfer, investment etc that would also result from leaving the EU. In fact, once these factors are taken into account, the cost of leaving the EU could be as high as £6,400 per household per year.

As such, the £850 figure likely underestimates the true cost of leaving the EU.

Nonetheless, that is not to say that every part of the CEP analysis is beyond criticism. For example, the study assumes that intra-EU trade costs will continue to fall as they have done in the past, but does not provide any evidence to suggest that such an assumption is reasonable. If, in fact, intra-EU trade costs were to fall less quickly than assumed by the study, then the costs to leaving the EU would be reduced.

Moreover, little information is provided regarding how the estimates of the cost of leaving the EU that account for the aforementioned “dynamic” factors (such as migration and investment) are obtained. Given that those estimates are likely to be based (at least in part) on complex (albeit commonly used) statistical methods, a higher level of transparency regarding the approach used would be welcome so as to enable a higher degree of confidence that the estimates have been obtained via a reasonable approach.

Overall, therefore, although the Vote Leave figure regarding the benefits of leaving the EU is an egregious over-estimation, and it is actually highly likely that there would be a large net cost to leaving the EU, it is unclear what exactly the cost per household per year is. However, this uncertainty regarding the exact cost should not detract from the fact that the cost to leaving the EU is large.

Battle of the ex-MPC members: Blanchflower vs Sentance

On the off chance that none of you have been following twitter recently, there has been something of a running disagreement (not quite a spat, but certainly not a friendly discussion) between two former members of the Bank of England’s Monetary Policy Committee (the group of learned people that, among other things, set the Bank of England’s base rate). Specifically, Danny Blanchflower and Andrew Sentance have been airing their widely different opinions regarding the state of the UK economy, and its recovery (or lack thereof) since 2008/2009. (See, for example, Blanchflower’s tweet in response to Sentance – there are plenty of others, although they do verge on the childish at times.)

By way of background, it’s helpful to note that during their time on the MPC, Blanchflower was noted as an inflation “dove” (i.e. someone who is not overly concerned with inflation as long as it was at extreme levels), whereas Sentance was one of the most “hawkish” (the opposite of a dove – i.e. someone who is concerned about inflation as (practically) the be-all-and-end-all) members.

This difference of opinion regarding the importance of inflation seems to have spilled over into their interpretation of the UK economy’s performance since 2008/2009. Blanchflower views the UK’s “recovery” since 2008/2009 as pitiful, and makes the (valid) point that it has taken over 60 months for the UK to return to its pre-2008 GDP level. Indeed, he uses the graph below to indicate that it has been the lengthiest recovery for over 100 years – each line represents the progression of GDP during each recession and recovery since 1920. The line representing the 2008-2013 recovery takes almost 12 months more than the next lengthiest recovery (1973-1976) to return to pre-recession levels, appearing to support Blanchflower’s claim. (In fact, Blanchflower makes the claim that it has been the lengthiest recovery for over 300 years, although the data to substantiate this claim have not yet been presented).

The picture is even more striking when looking at GDP per capita. Due to increases in population over time, the length of a recovery in terms of GDP per capita is increased relative to looking at GDP on its own. The graph below shows the difference between GDP per capita and its peak for each of the fours most recent recessions that were presented in the previous above. Due to limitations in the data available from the ONS, the series in the graph below are calculated using the ONS’ quarterly GDP data and their annual population data (assuming that quarterly population changes within a year are minimal).

Nonetheless, the implications of the graph are clear – it took even longer for GDP per capita to return to its pre-2008 level than was the case for just GDP: 7 years for GDP per capita versus about 5 a bit years for GDP on its own. Moreover, the difference between the current recovery and the next most lengthy is 13 quarters – i.e. just over 3 years.

GDP per capita

So, then, it appears as though Blanchflower is correct in terms of the length of the recovery. It is difficult to see how Sentance can disagree with Blanchflower on this issue.

Another matter is the reason for the lengthy recovery, but that’s for another blog post!