Fact-checking a few claims about the NHS

What with the campaigning for the general election having gotten into full swing last week, many claims have been made regarding which Party would be better for which aspect of security, the economy, education etc. One particular video regarding the NHS started doing the rounds on Facebook a few days ago. This video makes a number of claims regarding the supposed impact that the recent Coalition and Conservative governments have had on the NHS, with the video then going on to suggest that a Conservative government would be bad for the NHS. For a bit of excitement, here is said video:

 

 

The claims made in that video are many. Some are valid, whereas others are not. Let’s take each of them in turn.

Claim 1: We are experiencing the largest sustained drop in NHS funding as a percentage of GDP since the NHS was founded.

Reality: This claim is false. As per the information shown in the graph below (from the Institute for Fiscal Studies) NHS spending as a proportion of GDP has been stable over the past couple of years, and the decrease between 2009 and 2012 was no larger or longer than decreases in the mid-to-late 1970s or mid-1990s.

bn201_fig1

Moreover, the more relevant metric of NHS spending per capita continues to increase – in other words, more is spent per person on the NHS than ever before, although the rate of that increase has slowed in recent years.

bn201_fig2

Claim 2: If the internal market was abolished we [i.e. the NHS] could save billions.

Reality: This claim is also false. The internal market actually creates savings and is not “wasteful” as is claimed in the video. On the contrary, it promotes competition and stimulates the NHS to provide better services – importantly, the benefits of competition in healthcare are well established. Furthermore, it is actually the refusal of many within the NHS to accept the proven benefits of competition that is causing some harm to the NHS – indeed one of NHS Improvement’s main aims is to promote and encourage “buy-in” of competition among those in the NHS. Hence, abolishing the internal market would actually cost billions rather than save them.

Claim 3: Health tourism costs the NHS £200 million per year, which is insignificant in terms of the overall cost of the NHS.

Reality: This is generally true – although the costs to the NHS associated with people who are not ordinarily resident in the UK are of the order of £2 billion per year, that includes many people who did not come to the UK specifically and solely to use the NHS (i.e. it includes people who are not “health tourists”. Instead, estimates put the upper bound of the costs associated with those who travel to the UK for the sole purpose of using the NHS at around £300 million per year. When compared to the total annual NHS budget of about £90 billion, the costs associated with health tourism are indeed a trivial amount.

Claim 4: Immigrants are not ruining the NHS, they’re running the NHS.

Reality: True. Immigrants from within the EU currently represent about 10% of doctors and 4% of nurses. If non-EU immigrants are included, therefore, the figures are likely to be slightly (although probably not a huge amount) higher. Given that there are already quite severe labour shortages within the NHS, it is clear that without the immigrants currently working within the NHS, the functioning of the NHS would be severely hampered. Moreover, immigrants are net contributors in terms of taxes vs benefits, so also contribute to the NHS in that way. Hence, the claim that immigrants are not ruining the NHS is clearly valid.

Claim 5: 1 in 10 nursing posts are vacant and the nursing bursary has been scrapped

Reality: True. The nursing bursary was indeed scrapped at the start of the year – this means that there is a much-reduced incentive for people to train to become nurses as they will now have to pay £9,000 in tuition fees per year in order to do so. This is likely to lead to problems recruiting sufficient nurses in future. Notwithstanding that, there are also problems recruiting nurses now – the Royal College of Nursing suggests that 1 in 9 nursing posts are now vacant. This figure is actually marginally worse than that claimed (11% vacancy rate vs the 10% claimed).

Claim 6: Tens of thousands of sick patients waited on A&E trolleys this past winter

Reality: Likely to be true. Using data from Quality Watch (and a bit of approximation / extrapolation), roughly 6 million people attended A&E last winter. Of these, around 15% were not seen within the government target of four hours – i.e. about 900,000 people waited more than four hours in A&E. Now, it seems unlikely that all of these people waited on trolleys specifically, but even if only 10% of these people (i.e. 1.5% of all admittances to A&E) did then the “tens of thousands” figure would be accurate. Hence, this claim seems plausible.

Conclusion: As with most of these election video type things, the video contains some claims that are true, some that are likely to be true, and some that are demonstrably false. Does this mean that the Conservatives are the worst Party for the NHS? Who knows?! That’s for you to decide and take into account (if you want to) when you vote. But at least when doing so, you’ll now have a more complete set of facts when you do.

 

What a surprise! Taking in refugees isn’t detrimental to society

Back in 2015, Germany, recognising the humanitarian crisis in Syria agreed to allow any refugee that had made it to another EU country to file claim asylum in Germany. Inevitably, this resulted in a large influx of refugees – roughly one million were registered at German borders in 2015, with a further 400,000 or so registering in 2016.

Equally inevitably, this was met with howls of protests from those who wanted to “protect their borders”. For example, claims regarding the level of crimes committed by refugees and immigrants littered the likes of the Daily Mail and the Express, despite the fact that said crimes accounted for an infinitesimal proportion of all crimes in Germany during that period.

However, until now there hasn’t been a systematic study of the impact of Germany’s decision to accept large numbers of refugees – a paper by Gehrsitz and Ungerer fills this gap.This paper looks at the impact of the number of refugees on local crime rates; domestic and refugee success in the job market; and domestic attitudes to refugees and immigration.Although there have been some studies that find that immigration / refugees are detrimental, but those studies are either methodologically flawed (e.g. the one by Piopiunik & Ruhose) or written by biased fools such as Borjas.

Given the fact that large numbers of refugees were accepted into Germany, if accepting refugees was detrimental to any of these areas, then those effects would be almost certain to show up in this analysis. However, the study indicates that accepting large numbers of refugees is not detrimental to the local population.

In order to do so, the study makes use of the fact that refugees were allocated to different German states simply based on what accommodation spaces were available, creating a pseudo-random distribution of the number of refugees across the different German states.In essence, provided that these allocations were not correlated with factors such as the initial (and trend) income, unemployment etc across the different states, this provides a natural experiment by which the impact of the number of refugees on the domestic population can be estimated. Importantly, the paper finds that there is no correlation between a state’s initial labour market conditions, demographics, crime rates etc. such that the inferences resulting from the analysis are highly likely to be valid.

The paper then looks at the impact of the number of refugees that entered a particular state during the 2015-2016 period on a state’s 1) change in crime between 2013 and 2015; 2) change in unemployment rate between 2013Q1 and 2016Q1; and 3) change in share of the vote obtained by the anti-immigration “Alternative fur Deutschland” party between the federal election in 2013 and the state elections in 2016, while also controlling for other factors (such as state GDP per capita, demographics etc.) that vary across the German states.

Perhaps unsurprisingly, the paper finds that refugee inflows have:

  • no negative impact on the rate of domestic unemployment in a state (in fact, the results suggest that an increase in refugees actually decreases domestic unemployment, but slightly increases unemployment among non-German workers likely because the refugees themselves start to show up in the unemployment figures);
  • a tiny impact on crime rates – a large increase in refugees does not lead to an “explosion” in crime, but merely increases reported crimes by only 1.5%, with the majority of this appearing to come from an increase in fare dodging on public transport;
  • no impact on support for the anti-immigration political party – in other words, having more refugees in an area does not seem to lead to people in those areas voting in favour of decreasing immigration.

Now, one potential issue with some studies that find “no effect” of a variable is that this finding of no effect is driven by the coefficients being estimated imprecisely – this is usually indicated by standard errors that are improbably large. However, in the case of this study, the standard errors do not appear to be overly large, such that there is no reason to believe that the findings of no effect are due to imprecise estimates of the coefficient.

Hence, there is strong reason to believe that accepting even a large number of refugees is not detrimental to the local population in terms of crime or unemployment (or other factors that might drive local people to vote for an anti-immigration political party). Although these results do only refer to short-term effects (i.e. those occurring within 6-12 months of a large influx of refugees), there is no reason to believe that the long-term effect would be any different. Indeed, many studies (e.g. Foged & Peri, the IMF) find that the domestic population actually benefits from taking in refugees and immigrants in the long-run.

In other words, arguments that taking in refugees will harm (or be at the expense of) the domestic population are highly likely to be false.

Immigration benefits us all – now the IMF gets in on the act

Only a short time after the Foged & Peri paper (summarised here) found that an “influx” of immigrants to Denmark benefited the both high-skilled and low-skilled workers in the local population, the IMF has examined whether or not those results apply to other advanced economies.

And, guess what? They do! Unsurprisingly.

In order to do so, the study uses a fairly nifty approach to accounting for potential reverse causation between migration and GDP per capita (since migrants might prefer moving to countries with higher GDP per capita in the first place). The study uses a “gravity model” to instrument for the share of migrants in a country, including various “push” factors (such as growth in the origin country, demographic variables etc.) and other controls, proving once again that describing something as “gobbledygook” just because you don’t understand it isn’t a particularly sensible thing to do.

The paper’s main findings are threefold. First, a 1% point increase in the proportion of population made up by migrants actually increases GDP per capita by 2%. Interestingly, this benefit arises via an increase in labour productivity, rather than an increase in the proportion of the population that is of working age.

For example, high-skilled immigrants can increase productivity through innovation and positive spillovers on native wages, while low-skilled workers can increase productivity by enabling native workers to re-train and move into more complex occupations (exactly as was found by Foged & Peri). An alternative mechanism cited by the IMF study suggests that the presence of low-skilled female immigrants increases the provision of household and child-care service, thereby increasing the labour supply of high-skilled native women. This result is robust to controlling for technology, trade openness, demographics, and country development.

Second, these benefits arise from both low-skilled and high-skilled migrants. As above, both skill-types affect GDP per capita through increasing labour productivity, rather than via increasing the proportion of the population that is of working age. However, the effect does appear to be more statistically significant for migration by low-skilled workers than it is for high-skilled migrants.

The study suggests that this difference could reflect differences in the impact of high-skilled migrants across different countries, but this seems unlikely to be sufficient to render the impact insignificant. More likely is the second reason posited by the study – namely, that high-skilled migrants initially might have to obtain jobs for which they are over-qualified, thereby meaning that their impact on the incentives of high-skilled native workers to retrain etc. is limited at first.

Third, the benefits to native workers arise across the entire income distribution. Both low-skilled and high-skilled immigration increase the GDP per capita of those in the bottom 90% of the income distribution by roughly the same amount, while high-skilled immigration increases the GDP per capita of those in the top 10% of the income distribution by roughly twice as much as does low-skilled immigration.

However, the study does not really examine the distribution within the bottom 90% particularly closely – the study just looks at the estimated impact of immigration on the Gini coefficient to conclude that the distribution within the bottom 90% would not be changed significantly. The study should, instead, have looked at, say, the impact of immigration on each decile or quintile of the income distribution separately so as to give a more complete picture of the impact of immigration across the income distribution.

The paper (and particularly the blog post linked to above) ends by getting somewhat more political. In particular the study suggests that there is a need for improvement in terms of providing support for native workers that want to re-train, find a new job etc. However, these policy suggestions are made without taking into account the fact that some countries do already have plentiful such schemes in place, to the extent that increasing the provision of such schemes in those countries might not be efficient. Of course, that’s not to say that some countries would benefit from increasing the provision of such schemes.

Pesky immigrants, coming over here, making our lives better

Further to a previous post about how immigration can benefit the global economy, it’s also important to examine how immigration can affect the people in areas to where people have migrated. One of the main arguments used by those that try to claim that immigration is harmful is that immigration hurts the wages / employment of the low-skilled because supposedly cheaper immigrants take those jobs away from domestic workers. The fact that this argument is based on the false “lump of labour” theory has been covered well elsewhere (see, for example, here), but it can also be tested empirically.

To that end, a recent paper by Foged & Peri investigates the impact of immigration on domestic workers of various different skill types. To do so, it makes use of a quirk in the system that Denmark used to re-locate successful asylum-seekers and the fact that in subsequent years, relatives of those asylum seekers came to join them. Specifically, between 1986 and 1998, Denmark distributed refugees across the country taking into account only the refugee’s family size, the nationality of the refugee (so as to try to achieve “clusters” of refugees that would help each other out), and the availability of housing in each area.

In this way, the location of refugees was almost entirely independent of local labour market conditions. (Note that it is not entirely independent because there is likely to be some relationship between an area’s labour market and the availability of housing.) Nonetheless, this distribution of refugees was followed by a period between 1995 and 2003 in which immigrants from various regions moved to Denmark (most of these were from the likes of the Former Yugoslavia, Somalia etc. and were trying to escape local conflicts).

The people in this “new wave” of immigrants tended to settle in areas where earlier refugees/immigrants with the same nationality had settled – in this way, the initial distribution of refugees (that was mostly independent of labour market conditions) also drove future immigrants’ decisions of where to settle. Hence, the increase in immigration to Denmark from 1995 on can be used to assess the impact of immigration on the labour market in Denmark since the locational decisions of those immigrants was mostly independent of the labour market conditions themselves. (This is important because otherwise the results of an analysis of the impact of immigration on the labour market would be biased if the amount of immigration to a particular area itself was affected by the labour market in that area).

Therefore, in order to assess the impact of immigration on Danish workers, the paper uses a “longitudinal cohort study” – i.e. a frequent survey of a large group of people over a prolonged period of time (in this case, the Danish Integrated Database for Labour Market Research). This survey contains information related to someone’s age, municipal location, whether or not they are employed, their occupation, the number of hours they work, their salary etc. Coupled with information on where immigrants settled as per the previous paragraph, this dataset thus enables the researchers to investigate the impact of immigration on domestic workers.

The results of the study are extremely interesting. Contrary to the fallacious argument that low-skilled workers are harmed by immigration, the results of this study indicate that immigration actually benefits low-skilled workers in multiple ways.

First, immigration motivates and enables low-skilled workers to progress into more complex occupations. Second, these more complex jobs are associated with higher wages, such that immigration actually increases the hourly wage of low-skilled workers. Third, immigration does not result in (some number) of low-skilled workers becoming unemployed. In other words, immigration doesn’t force low-skilled workers out of a job, but instead enables them to obtain better jobs.

In addition, the study also looks to see if the impact of immigration falls different across different sets of low-skilled workers. It finds that the positive impact of immigration is felt most strongly by those low-skilled workers that are either young (less than 46 years old) or have not been in their jobs very long (less than four years), but also that there are no statistically significant negative effects for older workers or those that have been in their jobs a long time.

Overall, therefore, this study is pretty conclusive that immigration is beneficial to low-skilled workers and that those that claim that immigration harms those on low wages really don’t know what they’re talking about.

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.