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.


Immigration and global output

A recently published IZA working paper by Clemens and Pritchett has provided an interesting development regarding the assessment of “optimal” rates of migration. Previous studies tended to focus on the impact of migration on income distribution in the countries that were being migrated to. The Clemens and Pritchett paper is part of a developing literature that, instead, examines the impact of migration on (global) efficiency).

Previous studies looking at the impact of migration on income distribution essentially assessed the extent to which migration affected wages in the countries/areas in which migrants were settling. These often produced mixed results – for example, Borjas found that increases in migration to a country were associated with decreases in wages in that country, whereas Ottaviano & Peri find that immigration actually increases wages in migrants’ destination countries. Just to confuse matters, Card finds that wages are completely uncorrelated with migration.

Hence, there is a need for an alternative way of looking at the impact of migration, which is where these recent developments in terms of the “global efficiency of migration” come in.

The basic idea is that the productivity of labour is low in the countries from which people migrate, but high in the countries to which migrates move. This means that moving people (i.e. labour) from a low productivity country to a high productivity country increases the mean global productivity of labour, such that global output increases.

Consider the stylised example set out in the table below, in which 50 people move from the low productivity country to the low productivity country – the rows indicate whether the situation is before or after this migration occurs. The second and third column of the table indicate the productivity of one unit of labour in, respectively, the migrants’ origin country (the low productivity country) and their destination country (the high productivity country). Columns four and five indicate the number of people in each country, while the fifth and sixth column indicate output in each country (simply each country’s labour productivity multiplied by the number of people in the relevant country).

The final column sums the output in each country to obtain total global output. Comparing this column before and after migration indicates that people moving from the low productivity country to the high productivity country can increase global efficiency. Empirical studies have found that, via this mechanism, global output could be increased by 50% – 150% if restrictions on migration were lifted.

Good case

However, a modification of this mechanism could mean that migration actually reduces global output. Specifically, it could be the case that people moving from low productivity (origin) countries to high productivity (destination) countries actually “bring” some of their low productivity with them, such that the productivity of all workers in the destination country is reduced. If the productivity of labour in the destination country is reduced by a sufficient amount, this could mean that migration reduces global output. In the previous example, it was assumed that the productivity of labour in each country (the second and third columns of the table above) remained unchanged after migration.Such “transference” of low productivity could occur via migrants bringing their cultural or institutional norms with them and potentially being slow to “assimilate” in their destination country.

The table below presents a revision of the previous example in which the only change is that labour productivity in the destination country is reduced by migration (note, however, that productivity in the destination country is still higher than that in the origin country). Even though everything else from the previous example is unchanged, if migration reduces productivity in the destination country, this could mean that migration actually reduces global output. This theory, called the “Epidemiological Model”, has been espoused by the likes of Borjas.

Bad case

Clemens and Pritchett’s working paper tries to bridge the gap between these two opposing mechanisms by modelling the impact of “transmission”, “assimilation” and “congestion” on the rate of migration that maximises global output while eqaulising labour productivity. In this context:

  • transmission refers to the extent to which migrants’ low productivity travels with (i.e. to what degree do migrants actually “bring” any cultural and institutional low productivity with them when they migrate);
  • assimilation is defined as the proportion of migrants that “convert” to being high productivity (i.e. of those that migrate, how many obtain the same high productivity as workers in the destination country); and
  • congestion refers to the impact of un-assimilated migrants on the overall productivity in the destination country.

As such, the model constructed by Clemens and Pritchett trades off the gains of moving labour from a low-productivity country to a higher-productivity country against the reduction in the productivity in the high-productivity country resulting from un-assimilated migrants. Hence, the model embodies the two opposing potential mechanisms by which migration can imapct global output as described above.

The model’s results indicate that optimal migration is higher when:

  • transmission is lower – i.e. if cultural and institutional low-productivity does not “travel” well;
  • assimilation is higher – i.e. if migrants easily and predominantly obtain the same high productivity as workers in the destination country; and
  • congestion is lower – i.e. un-assimilated migrants do not substantially reduce the productivity level in the destination country.

Although these results might seem relatively obvious given the description above, the paper then goes on to use estimates of  the rates of transmission, assimilation, and congestion to obtain an estimate of the “optimal” rate of migration from the perspective of maximising global output. The paper finds that this optimal rate is substantially higher than the actual rate of migration, with the implication that global output could be raised by reducing the current restrictions on migration.

However, there are some flaws with the paper. First, the model of global output that is used to determine the optimal rate of migration only includes labour as an input – i.e. it does not include capital (machinery, infrastructure etc.) as a determinant of output. This is despite the fact that most basic models of output do include capital. The absence of capital from this model is not a problem if migration does not affect incentives to invest in capital, but if migration does affect those incentives, then the results of the model are unlikely to hold in reality.

In particular, if migration increases investment (by reducing labour productivity, thereby making investment more attractive relative to labour), then increased migration increases output such that optimal migration would be higher. Alternatively, if migration reduces incentives to invest, then increases in migration could lead to reductions in capital, potentially decreasing global output. Although the paper tries to cover this off in a single paragraph towards the end of its results, this is far from sufficient (the paper only mentions the first potential impact of capital and sues that to claim that its results are conservative).

Second, the paper notes that the rates of assimilation, transmission, and congestion are relatively unknown yet it does not include a rigorous assessment/estimation of the true value of these parameters. Instead, in order to obtain empirical estimates of these rates, the paper relies on very simple regressions that appear far too basic to capture the various determinants of these rates. For example, the estimates of the rates of assimilation and transmission are based on regressions where the dependent variable is a person’s wage yet the paper only includes controls for age, education, and gender as well as the immigrant status of a person (the variable of interest), despite the fact that estimating the determinants of wages is a highly complex exercise.

Finally, the paper assumes that changes to productivity only flow in one way (i.e. that low productivity workers reduce the productivity in the destination countries but productivity in the origin country is unchanged despite the potential for technology transfers or stimulation of foreign direct investment) and claims that is conservative. In other words, the paper claims that ignoring this possible transfer means that their estimate of the optimal rate of migration is actually lower than the truly optimal rate.

However, this fails to take into account the fact that if such productivity changes flowed both ways, then the productivity in the low-productivity origin country would increase in future, thereby reducing the productivity difference between the high and low productivity countries (i.e. reducing the positive impact of labour moving from the origin to the destination country). This could have the effect of reducing the future optimal rate of migration, but is further complicated by the fact that raising productivity in the origin country might also mean that any reduction in productivity in the destination country through migration is ameliorated somewhat. However, the paper just glosses over this complex dynamic aspect.

Nonetheless, despite these flaws the paper does provide a useful framework and some novel insights regarding how the assessment of restrictions on migration can be developed in future.

Universal Basic Income – not all “basic” things are bad

A couple of weeks ago, Finland announced that they were going to completely reform their benefits system by doing away with all means-tested (and other) benefits and replacing them with a “universal basic income” of €800 per month.

A very good summary of the majority of the benefits and potential drawbacks can be found here – I strongly recommend you go and read it. It falls a little short in some areas: for example, it doesn’t really investigate whether or not the new system will be more or less expensive of the old system, yet still dismisses as false claims that the new system will be too expensive.

However, more importantly, even though the article claims that it would reduce instances of “out-of-work claimants afraid to take up short-term job offers for fear of losing benefit entitlement” this misses off a crucial element (although the main thrust of this point is correct).

In particular, it ignores the fact that some people currently in employment might decide to remove themselves from the workforce – some people currently in work might decide that €800 per month is more than sufficient for them to live on and that they can therefore get by without working. This would result in people that were previously productive (i.e. contributing to GDP) no longer working, such that GDP could fall.

However, the size of this effect could be quite small. Indeed, it could be argued that the people most likely to be disincentivised by the new system are those that are already out of the workforce in the first place. If this is the case, then the policy likely would have a negligible impact on GDP since the reduction in the size of the workforce it would inspire would be limited.

Moreover, even for those that are disincentivised, the overall size of the disincentive depends on the amount of the basic income relative to the amount that someone could expect to obtain from remaining in the workforce, as well as the sort of lifestyle that the basic income can obtain. If the basic income is small relative to the wages obtainable through employment and/or relative to the cost of maintaining the desired lifestyle, then the disincentive effects of the policy are likely to be negligible.

Indeed, these disincentive effects are likely to be outweighed by the positive incentive provided by the removal of means-tested benefits. This removal reduces the marginal tax rate paid by those that would otherwise be on means-tested benefit, thereby increasing their incentive to increase their income.

To see this, suppose (in a hypothetical stylised example) that someone earns €10,000 through working and obtains €9,600 in means-tested benefits that are withdrawn at a rate of €1 per every extra €2 earned – in this scenario, the person gets €19,600 per year through income and benefits combined. Further, suppose that the income tax rate is 0% until someone earns more than €25,000. Under the means-tested benefit scenario this person has the chance to increase their in-work income to €12,500 – this might seem automatically worthwhile because they will get €2,500 per year more. However, the fact that their means-tested benefits are withdrawn gradually means that the total amount they would get is lower than this. Specifically, their means-tested benefits would now only amount to €8,350, such that this person would obtain €20,850 per year. This is an increase in total money of only €1,250 despite the person’s increase in work income of €2,500. In other words, this person faces a marginal tax rate of 50%, which could provide a large disincentive from taking on more work.

The situation with the universal income is much simpler – the person gets €9,600 benefit regardless of their paid income. Hence, any increase of the person’s income (below the income tax threshold) is kept by that person, so that in this scenario the person’s total annual money would increase by the full €2,500 to €22,100. Therefore, under universal income, a potentially very strong disincentive to work is removed, encouraging people to work and thereby increasing GDP.

As such, the issue regarding work incentives and the impact the policy will have on employment via the change in work incentives is far more nuanced than the article suggests. Hence, the article may well understate the benefits to this policy.

Climate change and the importance of the discount rate

The ongoing talks regarding climate change (and what to do about it) seem to take as given that “something” should be done to try to prevent climate change. Abstracting from arguments regarding any scientific consensus about whether or not humanity’s actions are at least contributing to climate change, these talks still do not question whether it is economically rational to do something about climate change.

Indeed, the fact that the impact of any climate change is likely to be (relatively) far into the future, whereas the costs that would be incurred to try to stop climate change would be incurred much sooner does make it difficult to evaluate the extent to which there is an economic case for doing something to stop climate change.

This is where something like the Stern Review should come in – it was an attempt to examine the economic impact of climate change and, hence, whether or not it made economic sense to try to counter the effects of climate change. Stern concluded that there was a strong economic case for acting to prevent/mitigate climate change. However, its approach was fundamentally flawed.

In particular, the Stern Review’s treatment of the “discount rate” (i.e. the amount used in the conversion of future sums to present values) meant that it overstated the current value of the future benefits of preventing climate change. Although the Stern Review accepts that there is some need for discounting due to the fact that £1 in the future can buy less than £1 today (i.e. inflation erodes the value of the same nominal sum), the Stern Review unjustifiably rejects the notion that people have an inherent preference for receiving beneficial things sooner rather than later.

The rejection of the second reason for discounting future sums means that the discount rate used by Stern is an artificially low 1.4%. This is in stark contrast to the 4% – 6% discount rate for developed countries that is suggested by Markandya et al (2001) and even below the 2% -3% suggested by Halsnae et al (2007) – both of these figures were estimated by the IPCC itself.

Although these differences of a few percentage points a year might not seem like much, over time they add up. To see this, the graph below shows the present value of receiving £100 x years into the future under different discount rates. The x axis indicates how many years into the future the sum is received, while the y axis shows how much that sum would be worth in present value. The black line indicates how the present value of the £100 declines over time under a discount rate of 1%, while the blue and red lines show the progression under discount rates of, respectively, 3% and 5%.

Discount rates

The difference between the three lines is stark – despite only a two percentage point difference between a 1% discount rate and a 3% discount rate, the gap between the present value under the different discount rate is substantial after just 10 or so years.

How, then, does using a more appropriate discount rate alter Stern’s conclusions. Well, Stern puts the central estimate of the global cost of not acting to prevent climate change at about 5% of global GDP – i.e. roughly $5 trillion. Let’s (conservatively) assume that the full extent of these costs would be incurred starting from 2100 – i.e. that in 85 years the costs of not doing anything to combat climate change would be $5 trillion. Using Stern’s own discount rate of 1.4%, that puts the net present value of mitigating climate change at about $1.5 trillion per year. Given that Stern estimates that the costs of mitigating climate change are about $1 trillion per year in about 2050 (and let’s not get into whether or not Stern has underestimated these costs as that has been covered thoroughly elsewhere, nor the fact that these costs would need to be incurred on an annual basis for at least 30-odd years before the benefits from mitigating climate change would be felt) – i.e. about $615 billion per year in the present day using Stern’s discount rate – Stern concludes that acting to mitigate climate change is economically rational.

However, using the more appropriate discount rate of 4% per year, the benefits from ameliorating climate change have a present value of the benefits from mitigating climate change of  about $178 billion per year, while the present value costs of mitigating climate change (using that same discount rate) are roughly $253 billion per year.

In other words, using a more appropriate discount rate means that Stern’s conclusions are completely reversed – instead of there being a net economic benefit from acting to mitigating climate change, doing so actually results in an economic loss.

It is rather damning that Stern chose to bury this part of his assessment in a technical annex to an addendum to his original review. And yet this report is relied on as evidence that there is an economic case for climate change despite the fact that the evidence does not support that conclusion.

The reasons for the prolonged recovery in the UK

In a previous blog post, I demonstrated that the UK’s current recovery from the 2008 crisis has taken longer to reach its pre-recession peak than any other recovery over the past 50-odd years.  Moreover, things look even worse if figures are calculated on a GDP-per-capita basis than they do using simple GDP – GDP-per-capita remains below the pre-crisis peak despite GDP having surpassed it a couple of years ago.

Obviously there is plenty of debate as to why the UK’s recovery has been so protracted. Some, like Simon Wren-Lewis, think that the main (sole?) reason is due to the government’s implementation of fiscal austerity. Others point to the nature of the crisis having been a financial one as the main explanatory factor.

However, one thing that seems to have been neglected in terms of the UK’s recovery is the fact that the crisis was a global one and that the UK is more reliant on exports than some of the other countries that had quicker recoveries. In particular, according to the World Bank the UK’s exports of goods and services amount to about 30% of UK GDP, whereas for the US (commonly held up as the country that has recovered the best post-crisis) this figure is close to 14%. (And although Germany’s exports amount to about 45% of their GDP and Germany has also recovered more quickly than the UK, the fact that the German economy relies much more on manufacturing exports than the UK means that it is not a good comparator for the UK.)

Hence, it seems plausible (at least at first glance) that the global nature of the crisis coupled with the UK’s greater reliance on exports means that the path of exports can explain at least some of the difference between the current UK recovery and previous ones.

The graph below shows the path of the UK’s exports on a quarterly basis since the start of a recession for the past four recessions and recoveries.

ExportsIt is interesting to note that the 1973-1976 recovery was associated with a rapid increase in exports during the recovery period, while exports during the the other three recoveries (1979-1983, 1990-1993, and 2008-2013) followed a more similar path until about three years into the recovery period. At that stage of the recovery, exports during both the 1979-1983 and 1990-1993 recoveries continued to increase at a relatively gradual but noticeable pace. However, exports during the 2008-2013 recovery stagnated and have not increased substantially at all over the past three-to-four years.

In other words, it seems that at least part of the reason the UK’s current recovery has been so slow recently is due to the lack of growth in exports over the past few years. It is important, therefore, that the “blame” for the prolonged nature of the UK’s current recovery is not entirely placed at the foot of fiscal austerity.

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!