Issue 242 June 2019

Issue Number 242 June 2019

1. Sensible decision by the UK Supreme Court in June 2019. 2. Challenging DNA evidence. 3. The worry about cross-border criminals. 4. Legal Notice.

Sensible decision by the UK Supreme Court in 2019

Sally Ramage

United Kingdom’s Supreme Court, London. Source: Google.

Legislation: 1843 Treaty of Maastricht1 (original) 1933 Montevideo Convention on Rights and Duties of States- Article 1. 1972 Treaty of Accession-Article 2. 1972 European Communities Act, s2 (5) and (6). 1992 Maastricht Treaty. 2003 Athens Treaty. 2003 European Union (Accessions) Act. 2004 Directive 2004/38/EC (Citizens Directive). 2004 SI 2004/1219. 2006 SI 2006/1003) Immigration (European Economic Area) Regulations. 2018 EU (Withdrawal) Act 2018.

1 This first Treaty of Maastricht was signed in 1843 by Belgium and the Netherlands four years after the Treaty of London established Belgian independence and settled the border between Belgium and the Netherlands. The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429

Caselaw Factortame Ltd v Secretary of State for Transport [1989] 2 All ER 692. Factortame Ltd v Secretary of State for Transport (No 2) [1991] 1 All ER 70. R (Lumsdon) v Legal Services Board [2015] UKSC 41. Secretary of State for Work and Pensions (Appellant) v Gubeladze (Respondent) [2019] UKSC 31; [2017] EWCA Civ 1751. Van Gend en Loos v Netherlandse Administratie der Belastingen [1963] EC 1. Zalewska v Department for Social Development [2008] UKHL 67.

Introduction This case, Secretary of State for Work and Pensions (Appellant) v Gubeladze (Respondent) [2019] UKSC 31, was decided by the UK Supreme Court in London over two long days. It was an appeal from [2017] EWCA Civ 1751. Sitting, were Justices Hale, Kerr, Carnwath, Hodge, Black, LloydJones, and Sales. This was an appeal from the UK appeal court decision concerning a European citizen (a Latvian). It basically concerned the employment pension rights in the United Kingdom of persons who come to work in the UK under freedom of movement rights. The Secretary of State sought to stop this Latvian person from receiving a pension benefit after a period of work in the UK.

Windrush strategy similarities used by the UK Secretary of State Strategically, this Gubeladze case was reminiscent of the Windrush deportations from the UK in recent years, but Windrush West Indians enjoyed no benefits nor privileges of being represented after deportation and with several breaches of the human rights of these poor West Indian families, which brought about death, mental illnesses, break-up of families and extreme

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 poverty in many of the thousands deported en masse by the Secretary of State for the Home Office. Had this Gubeladze case not succeeded in the UK Supreme Court in June 2019, it might have become a precedent case to deprive millions of European persons, legal and natural, living and working in the UK, from receiving their rightful benefits. Many millions of British persons, legal and natural, live and work in other European Union Member States and do receive benefits from those other EU Member States.

Principle of Supremacy However, as per EU(Withdrawal) Act 2018, EU law still has supremacy if the Principle of Supremacy2 continues to apply after UK exit date, if EU law is chosen to be retained since UK Government Ministers have the powers to deal with legal ‘deficiencies’ as they arise. The case discussed here, Secretary of State for Work and Pensions (Appellant) v Gubeladze (Respondent) [2019] UKSC 31, pivoted on the Accession (Immigration and Worker Registration) Regulations 2004.

Legal background The Gubeladze case concerns our appreciation of knowledge about treaties, about European Union law, United Kingdom law, human rights law, employment law, statutory instruments, and precedent caselaw. Certain

2 The Primacy of Community law prevails even where the domestic law is penal in nature, thus creating a defence of reliance on Community law. See Pubblico Ministero v Ratti (Case 148/78) [1979] ECR 1629. The primacy source of Community law is the EC Treaty as amended by the Treaty on European Union. The EC Treaty lays down the objectives of the Community; determines the power and duties of the Community institutions; and determines the rights and obligations of the Member States. Proceedings can be brought in the European Court by any Member State against another Member State which fails to fulfil its obligations under the Treaties. Treaty provisions are directly applicable, i.e. they automatically become part of the law of Member States and they can be of direct effect: they can be invoked by an individual against a Member State in the domestic courts of that Member State, where they are of sufficient clarity and require no further enactment, as the court observed in Van Gend en Loos v Netherlandse Administratie der Belastingen [1963] EC 1. 4

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definitions therefore need explanation to ensure that all are reading from the same hymn-sheet, so to speak.

A State When we write about a State, we mean ‘a person in international law’ who possesses: *a permanent population; *a defined territory; *a government; and *capacity3 to enter into relations with other States.4

The United Kingdom joined the European Communities in 1973 On 1st January 19735 the United Kingdom (UK) became a Member State of the European Communities by the 1972 Treaty of Accession, Article 2, thus accepting EEC laws. The UK thus accepts the laws6 properly made7 by the Council and Commission of the European Communities and undertakes to promote the objectives8 for which the European Communities were established.

3 A State’s capacity’ implies personality. Capacity means ability to do those particular acts in that particular Treaty. Capacity implies legal capacity. 4 The1933 Montevideo Convention on Rights and Duties of States, Article 1. This Convention was adopted by the 7th International Conference of American and Latin-American States and has since been seen as commonly accepted around the world as a reflection of Statehood in customary international law in regard to effectiveness of the body which claims the rights and duties of a State and its attainment by recognition of attaining international personality. 5 The date 1st January 1973 is the Accession Date under the 1972 Treaty of Accession, Article 2. See also European Communities Act 1972, s1. 6 The primary and secondary legislation of the EEC. 7 There is no power to question the legality of measures taken by the European Council and the European Commission. See EEC Treaty, Article 173. 8 See para 8 and Part 15 EEC Treaty. 5

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The Athens Treaty The 2003 Athens Treaty and the Act of Accession 2003 were drafted together. This Treaty was signed at Athens on 16 April 2003 (the Athens Treaty) and ten Accession States became Member States of the European Union. The 2003 Act of Accession was annexed to the Athens Treaty and permitted the existing Member States to apply national measures regulating access to their labour markets by nationals of the eight most populous Accession States (the A8 States). The A8 States include Latvia.

The Member States before 2003 had formed the European Economic Community (EEC) through the 1972 Economic Communities Treaty and the 1972 Treaty of Accession. The 2003 Accession Act required all of the original Member States to apply measures, for an initial period of two years from the date of accession, regulating access to their labour markets. The original Member States9 were permitted to continue to apply such measures until the end of the five year period following the date of the accession, after which time these original Member States could ‘in case of serious disturbances of a State’s labour market or threat thereof, and after notifying the European Commission (EC), continue to apply these measures until the end of the seven year period following the date of accession.’

SI 2004/1219: Accession (Immigration and Worker Registration) Regulations 2004 By Statutory Instrument (SI) 2004/1219, the EU’s 2003 Act of Accession was enforced in UK domestic law. SI 2004/1219 is known as the Accession (Immigration and Worker Registration) Regulations 2004 (SI 2004/1219). This

9 The original six Member States of the European Communities (EC) since the EC was established were Belgium, France, Germany, Italy, Luxembourg and the Netherlands. 6

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2004 SI established the Worker Registration Scheme (WRS) which obliged any national of an A8 State to register before starting employment and before taking up any new employment. Each registration incurred a fee of £90 and the obligation to register continued until the worker had worked for 12 months. Failure to register work in accordance with the WRS would mean that the individual would not derive from that work a right to reside in the UK.

Migration Advisory Committee (MAC) In 2009 HM Government asked the Migration Advisory Committee (MAC) to advise the government on the continuation of the WRS. Taking MAC’s advice, the UK Government decided to extend the measures applicable to nationals of the A8 States for a further two years.

Pension credit under Directive 2004/38/EC (Citizens Directive) The central issue in this case is whether Ms Tamara Gubeladze (Respondent), a Latvian national living in the UK, is entitled to receive state pension credit. The Respondent travelled to the UK in the year 2008 and worked for various employers between September 2009 and November 2012. In the periods when she was not working she was a jobseeker. She was issued with a registration certificate under the WRS on 20 August 2010. Her employment before that date was not covered by the WRS certificate.

Retired Tamara Gubeladze, right of residence in UK, applied for pension. On 24 October 2012, the respondent made a claim for state pension credit. The basis of Gubeladze’s claim was that she had a right of residence in the UK under regulation 5(2) of Immigration (European Economic Area) Regulations 2006 (SI 2006/1003), (the 2006 Regulations), which implement article 17(1)(a) of Directive 2004/38/EC (the Citizens Directive), as a person who had retired,

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 having pursued activities as a worker for at least a year in the UK, and having resided continuously in the UK for three years.

Gubeladze’s claim for pension rejected by Secretary of State The Secretary of State for Work and Pensions (Secretary of State) rejected Gubeladze’s claim on the ground that the requirement of three years’ continuous residence required three years’ continuous ‘legal’ residence, which meant a right of residence under the Citizens Directive.

The Respondent’s asserted right of residence during that time was as a worker. However Respondent Gubeladze had not been registered under the WRS for part of that period, and so the Secretary of State considered that she had not resided in the UK pursuant to a right of residence conferred by the Citizens’ Directive and therefore she did not meet the three year residence requirement in regulation 5(2) of the 2006 Regulations.

Respondent Gubladze appealed to the First-tier Tribunal and to Upper Tribunal The respondent’s appeal to the First-tier Tribunal was dismissed on jurisdictional grounds. Gubladze then appealed to the Upper Tribunal, which decided that the First-tier Tribunal did had jurisdiction to hear the appeal and it re-made the substantive decision.

Upper Tribunal: UK 2006 Regulation 5(2) (c) needs no continuous 3-year residence

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The Tribunal allowed the respondent’s appeal on two distinct grounds. First, it held that article 17 of the Citizens Directive, and therefore regulation 5(2) (c) of the 2006 Regulations, did not require that the three years’ continuous residence be in exercise of rights under the Citizens Directive.

No breach of any applicable UK law The Upper Tribunal held that ‘actual residence’ was sufficient. It also held that the UK Secretary of State’s decision to extend the WRS in 2009 was disproportionate and therefore unlawful. On that footing, the respondent’s residence in the UK at the relevant time had not involved any breach of any applicable valid domestic law and so was to be regarded as legal residence for the purposes of the 2006 Regulations.

Secretary of State’s later appeal of Upper Tribunal’s decision was dismissed The Secretary of State appealed to the Court of Appeal which dismissed the appeal. In the Court of Appeal, the Secretary of State succeeded in her appeal in relation to the first point, with the Court holding that the word ‘reside’ in article 17(1) (a) of the Citizens Directive meant ‘legally reside’ in the requisite sense; but the Court held that the extension of the WRS was disproportionate and therefore incompatible with EU law.

The Secretary of State appealed to the Supreme Court but case dismissed

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The case was heard by UK Supreme Court which unanimously dismissed this appeal. Lord Lloyd-Jones and Lord Sales give the sole judgment with which the other Justices agree.

Dismissal of Secretary of State’s appeal at UK Supreme Court Is the decision to extend the WRS open to challenge on grounds of proportionality? The Secretary of State submitted that her extension of the WRS in local UK legislation did not interfere with or derogate from any pre-existing protected interest, so it was not subject to any requirement of proportionality under EU law. The question at the heart of this issue was this: Did the Act of Accession create relevant protectable interests by conferring rights of EU citizenship on the new EU citizens from the A8 States subject to initial tapering exceptions imposed by the existing Member States, or whether it should be regarded as providing for only such rights as may be conferred by the existing Member States during the transitional period.

Zalewska v Department for Social Development [2008] UKHL 67. The House of Lords in Zalewska v Department for Social Development [2008] UKHL 67 took the former view. The Supreme Court agrees and considers that there was no intention under the Act of Accession to confer an unfettered right to derogate from general principles of freedom of movement. On the contrary, derogation from those principles must be subject to the principle of proportionality in EU law. It might be asked that if the decision to extend the WRS is open to challenge on grounds of proportionality, did the

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Upper Tribunal and the Court of Appeal err in their approaches and conclusions on this matter?

The Secretary of State simply relied upon the MAC report of April 2009 She has not filed evidence to explain any distinct reasoning as to why the extension of the WRS was justified, nor to point to any additional relevant factors other than those taken into account by the MAC in its report. This posed problems for the Secretary of State because the MAC was not asked to consider whether an extension of the WRS would be proportionate in terms of EU law and therefore expressed no view about that.

R (Lumsdon) v Legal Services Board [2015]: a three-stage test The leading decision of the UK Supreme Court on the Principle of Proportionality in EU law is R (Lumsdon) v Legal Services Board [2015] UKSC 41. The principle applies according to a three stage test. As regards the first stage of this test, the Court considers that the continuation of the WRS is suitable or appropriate to achieve the objective. The MAC report showed that extending the WRS would have a material but small effect in mitigating the serious disturbances to the UK labours market by reducing the flow of workers from A8 States which would otherwise occur. No issue arises in relation to the second stage.

Proportionality stricto sensu However, the Court found that the third stage of the proportionality analysis, i.e. ‘Proportionality stricto sensu’) is not satisfied. According to the assessment in 2009 the extension of the WRS would have only a small and rather speculative mitigating effect in relation to the serious disturbances in the UK’s labour

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 market, as found by the MAC, whereas the burdens and detriments it would impose on employers and A8 nationals working in the UK were substantial and serious. On the basis of the Court’s rulings on Issues 1 and 2, the appeal was dismissed. Even if the Secretary of State were to succeed on Issue 1 or Issue 2, does article 17(1)(a) of the Citizens Directive require a person to show that, throughout the period of continuous residence, he or she enjoyed a right of residence under that Directive? The UK Supreme Court stated that resolution of this issue was not necessary for the determination of the present appeal. However, this Court considered that it should deal with this matter since the interpretation of article 17(1) (a) may be important in other cases.

This Court concluded that the concept of residence as referred to in article 17(1) (a) is factual residence, not legal residence. This interpretation is reinforced by the purpose of the Citizens Directive, which is to enhance existing rights of free movement and residence and not to subject EU citizens to new restrictive conditions. It is for these reasons that the Upper Tribunal arrived at a correct interpretation of article 17(1) in holding that residence in article 17(1) refers to factual residence rather than legal residence in the specific sense which the term residence bears in the context of the Citizens Directive. If article 17 of the Citizens Directive requires ‘legal residence’ in the relevant sense, is ‘actual residence’ sufficient for the purposes of the 2006 Regulations? As the Court holds, the term ‘residence’ in article 17(1) (a) has the meaning set out above, and this issue does not arise. For the reasons set out above, the Supreme Court dismissed the Secretary of State’s appeal.

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Challenging DNA evidence Alec Samuels

Keywords: DNA – expert – weaknesses – challenge – defence

Abstract: DNA – weaknesses of expert – methods of challenge – duty of forensic scientist – duty of defence – better presentation by all involved.

Caselaw: Meadow v GMC [2006] EWCA Civ 1390, [2007] QB 462, CA. Gaughran v Chief Constable Police Service Northern Ireland [2015] UKSC 29, [2016] AC 345, paras 18-20. R v Adams [1996] 2 Cr App R 467. R v C [2010] EWCA Crim 2578, [2011] 3 All ER 509.

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R v Dlugosz [2013] EWCA Crim 1, [2013] 1 Cr App R 467, [2013] Crim LR 684. R v Doheny [1997] 1 Cr App R 369. R v FNC [2015] EWCA Crim 1732, [2016] 1 WLR 980, [2016] 1 Cr App R 12. R v Hoey [2007] NICC 49. R v Reed [2009] EWCA Crim 2698, [2010] 1 Cr App R 23.1. R(R) v a Chief Constable [2013] EWHC 2864 (Admin), [2014] 1 Cr App R 16. R v Tsekiri [2017] EWCA Crim 40, [2017] 1 WLR 2879, [2017] 1 Cr App 32, [2017] Crim LR 628. R v Weller [2010] EWCA Crim 1085 para 38.

Introduction DNA evidence is likely to make a powerful impact upon the jury. DNA represents wonderful science: it must be right. However, the truth is that DNA is seductive but fallible. Challenge is possible. Indeed, without challenge it will very probably be accepted. So what can the defence do? How can a challenge be persuasively or effectively or at least credibly raised? The defence expert should be specifically asked to consider any possible professional criticisms of the analysis and assessment of the prosecution expert. Then the defence advocate should carefully prepare the cross- examination, going through the history of the sample, from the crime scene to the completion of the report, from source to presentation. Maybe along the way there has been a departure from best standards, sufficient to discredit the expert and his report or at the very least to raise a doubt as to quality and reliability. The Forensic Science Regulator gives guidance on standards.

Reputation

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It has to be said, with regret, that some forensic laboratories have been exposed as not conforming to the highest standards. The analysis has been done hastily, and not sufficiently deeply, and with insufficient skill and care. Perhaps the analyst was too junior and inexperienced, and unsupervised. Perhaps shortage of resources (never an acceptable excuse) is proffered as an explanation.

Question the report So how long did the analysis take? Who did the DNA tests? What method was used? Was it checked? Or did the expert misread the data, making his interpretation incorrect? How do you explain the errors in testing previously made by your company? Has your company been criticised by the Forensic Science Regulator?

Reliable expert evidence There has long been judicial concern as to whether the expert is ‘sufficiently reliable.’ 10 ‘It is clear that there are many competitor providers of expert evidence in DNA science and many individuals of great experience who can draw on their own practical experience…. we do hope that the courts will not be troubled in future by attempts to rely on published work by people who have no practical experience in the field and therefore cannot contradict or bring any useful experience to bear on issues that are not always contained in scientific

10 G. Edmond and A. Roberts, ‘Expert Evidence in Criminal Proceedings’, Law Commission Issue 325, March 2011. 15

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 journals. There are plenty of really experienced experts who are available and it is to those that the courts look for assistance in cases of this kind’.11 It goes almost without saying that the expert is aware of and complying with the Criminal Procedure Rules and the Practice Direction. For the advocate to ask the expert questions on this and to receive correct and confident answers may serve only to strengthen the credibility of the expert. However, the advocate should be alert to possible weaknesses in the particular expert and be ready greatly to expose them.12 Low copy number DNA is acceptable only if complying with the correct procedure13 , Weir J – prosecution must prove the integrity of each and every forensic stage if challenged.

The expert Competent handling of the expert witness is a vital skill for the advocate/barrister. The disagreement between prosecution expert and defence expert is the forensic opportunity every defence advocate longs for. The skill lies in seizing the opportunity, not overplaying it, with the risk of losing it, just simply getting the jury to realise that the prosecution forensic science expert who originally appeared so impressive may indeed have mishandled or misunderstood or misinterpreted or missed the significance of the forensic evidence. In a strongly contested case the defence may wish to take the expert step by step through the Codes of Practice and Conduct and Guidance on Standards and Guidance.

11 R v Weller [2010] EWCA Crim 1085 para 38. 12 R v Dlugosz [2013] EWCA Crim 1, [2013] 1 Cr App R 467, [2013] Crim LR 684. 13 R v Hoey [2007] NICC 49. 16

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Statistics The expert will often give a figure, e.g. 1:1 million or 1:1 billion, against the possibility of a match to anybody else. And that sounds impressive to the jury. Incidentally 1:1 million may not in fact be so impressive set against the population of London, approaching 10 million; though 1:1 billion is certainly impressive, say for Europe.

The Sally Clark case The Meadow saga in the Sally Clark miscarriage of justice case showed that statistics is a deep scientific mathematical discipline, and has to be much more than prediction, and does not fall within the expertise of a paediatrician. 14 The Royal Statistical Society has produced guidance on this matter. A distinction has to be drawn between a report with a conclusion based upon scientific evidence and a report with a conclusion based upon an evaluative opinion rather than scientific evidence. See also Appendix A which includes explanations concerning these four cases. 15

The common issue of contamination The sample is usually small. From the taking of the sample until the final analysis the sample may pass through many hands and many processes. Items are packaged and repackaged. The risk of contamination is always present. Emergency workers, drivers, police, laboratory personnel, and indeed laymen at the scene of the crime may have had the opportunity to handle the sample. Packaging may contain impurities, e.g. body bags, as may the clothes, gowns,

14 Meadow v GMC [2006] EWCA Civ 1390, [2007] QB 462, CA. 15 R v Dlugosz [2013] EWCA Crim 1, [2013] 1 Cr App 32, [2013] Crim LR 684. See also R v Adams [1996] 2 Cr App R 467. R v Doheny [1997] 1 Cr App R 369. R v Reed [2009] EWCA Crim 2698, [2010] 1 Cr App R 23.

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 masks and gloves worn by the forensic scientists. The analysing instruments may not be sterile. A tray used for analysing a sample from the defendant D and producing his DNA in a relatively minor case was re-used in a subsequent very serious case and the sample produced D’s DNA. It was eventually proved that D had absolutely nothing to do with the subsequent offence (unreported).

Prevention of contamination In the forensic science industry is “certified” sterile equipment to prevent contamination, e.g. special protective gloves, special tape for lifting DNA, DNA-free disposable items such as swabs. Admission of such things in a particular situation might indicate a willingness to cut corners, to be satisfied with less than with the best.

Mixed samples from two or more persons Sometimes the sample is mixed, i.e. the sample contains the DNA of two or more persons. The mixing must surely raise the possibility that this might impair the quality and reliability of a finding that one of the DNAs comes from D. The forensic scientists assert that it is sufficient if the ‘major’ DNA comes from D. D can point to the fact that a third party may have been involved, exonerating D. The tests for sufficient reliability for admissibility or weight turn on the scientific evidence or the justification for an evaluation opinion, in which latter case detailed evidence is required of the experience of the expert and the features he is relying upon.16 The jury should be warned of the limitations of opinion evidence.

Sample size

16 R v Dlugosz [2013] EWCA Crim 2, [2013] 1 Cr App R 32, paras 2-28. 18

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Perhaps the sample was small, very small, a tiny drop of blood, perhaps only a trace or a touch. The forensic scientists claim that they can produce reliable assessment from tiny samples, such as deposition of a wet source. Theoretically and scientifically nothing turns on the size provided the threshold is passed, but the advocate will test the expert, and the jury may have their reservations.17 The sample was small and stale and mixed, D being the minor rather than the major offender. Quality not quantity is the criterion. 18

Mix-up in samples A sample may pass through many hands. The police and the forensic science laboratory may handle a considerable number of samples at any one time. The possibility of a muddle or mix-up in the samples cannot be ruled out. Labelling can always go wrong. The police may make a mistake. The wrong sample may be analysed. No rechecking may take place. The defence should be on the alert.

DNA properly and lawfully obtained For an analysis to be admitted in evidence the DNA must have been properly and lawfully obtained, e.g. by the police or forensic scientist from the scene of the crime. The problem usually arises where the police have retained the sample following a conviction or acquittal in an earlier case year ago. The prosecution must show that admissibility would be proportionate and justifiable. Is the retention sufficiently important and rational? Would a less intrusive measure be possible? Is the decision balanced and proportionate? 19

17 R v Reed [2009] EWCA Crim 2698, [2010] 1 Cr App R 23 18 R v C [2010] EWCA Crim 2578, [2011] 3 All ER 509. 19 . Gaughran v Chief Constable Police Service Northern Ireland [2015] UKSC 29, [2016] AC 345, paras 18- 20. R (R) v A Chief Constable [2013] EWHC 2864 (Admin), [2014] 1 Cr App R 16. 19

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Time between the crime and collection of samples The sample may be old, there may have been a considerable passage of time between the crime and the collection of the sample, and the sample, for example a bloodstain on a wall, may have been there a long time. The forensic scientist may claim that age is irrelevant; but the jury may feel that age could bring staleness and deterioration and a touch of unreliability. So the advocate may try some subtle probing.

Where the sample was found The point from which the sample was found and taken may be significant. Was the blood sample showing D’s DNA on the tip of the lethal knife, supporting his story that he was acting in self-defence, or on the handle, indicating the prosecution story that he was the assailant. The closer the sample to the crime itself the more cogent it will be. D’s DNA found on the victim V, or V’s clothing, or the weapon that injured or killed her, or on the door handle of her vehicle from which she was drugged in a robbery, tells its own story. D’s DNA found at the scene, e.g. on the furniture, indicates possible presence but not necessarily involvement in the crime. 20

When is sample taken? If D and V only had contact on the one occasion of the crime then that must have been where D’s DNA was deposited. But if D can show (evidential

20 R v Tsekiri [2017] EWCA Crim 40, [2017] 1 WLR 2879, [2017] 1 Cr App 32, [2017] Crim LR 628. R v FNC [2015] EWCA Crim 1732, [2016] 1 WLR 980, [2016] 1 Cr App R 12. 20

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 burden only) that there have been other contacts, innocent contacts, then a very different picture could emerge.

The surface from which sample is taken The surface from which the sample was taken may be significant. Wood is a good surface from which to take a sample, fabric (porous) is not so good, and glass is not at all good, though this is contested by some experts. Did the surface more readily or less readily contain and allow the DNA to persist?

Transference The fact that D’s DNA is found on V or her clothes could lead to a false inference of contact. D’s DNA can pass from D to C and on to V wholly accidentally, wholly innocently. So the closer the relationships or contact between D, C and V can be shown (evidential burden only) the greater the chance of transference. This indirect transference is often known as secondary transference.21

Innocent explanation The prosecution produce D’s DNA found at the scene of the crime, D pleads not guilty, but can give no explanation for the presence of his DNA. His prospects must be bleak. So he needs to show (evidential burden) that he was indeed at the scene of the crime but innocently, or on another occasion, or has had contact with V on another occasion. He did not steal or tamper with the motorcycle, but he did see it and admire it and touch it, and that must have been how his DNA trace was found on the motorcycle JS (A child) v DPP [2017] EWHC (Admin), [2017] Crim LR 718.

21 R v Reed [2009] EWCA Crim 2698, [2010] 1 Cr App R 23.21. 21

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The presence or absence of DNA can go a long way to supporting or undermining the assertion by D or a witness that he was present or absent at a particular place at a particular time.

DNA the sole evidence The sole evidence for the prosecution against D is that his DNA was found at the scene of the crime. There is, or certainly may be, a case to answer; though the burden of proof remains throughout on the prosecution. In the absence of an explanation the jury may draw an adverse inference. There may be an innocent explanation. D lives nearby and often passes the crime location. Or he was indeed present at the time of the crime, just passing by. Or he does indeed know V as a colleague at work or socially. But D’s DNA on V or her clothing or in her car may not be so readily explained in the context .22

Planted DNA can be taken from a source and taken to another location, e.g. placed on a wall or chattel or on clothing, maliciously, corruptly and unlawfully. C could place a cigarette smoked by D, containing D’s DNA, at the scene of the crime, D never going anywhere near the scene. Any such accusation is a very serious matter, and should only be made if there is some good reason for doing so, beyond mere assertion.

The ultimate issue The forensic expert analyses the sample, reaches and reports upon his rational findings of scientific fact, with such degree of certainty or confidence as he can give, exercising extreme caution about expressing any opinion. He avoids the

22 R v Tsekiri [2017] EWCA Crim 40, [2017] 1 WLR 2879, [2017] 1 Cr App 1 32, [2017] Crim LR 628.

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 ultimate issue in the case, because there may be other expert DNA reports and non-forensic evidence, and the judge will direct on the law and the jury will reach a verdict, guilty or not guilty.

Conclusion Forensic practitioners exercise a high degree of knowledge and skill and experience. Laymen are impressed, even mesmerised. Science carries much credibility in our society. There are risks for the prosecution. The forensic scientist may not measure up to the meticulous care and detail to be expected; he may be poor in expressing himself; he may move beyond his field of expertise. Too much reliance can be placed upon DNA evidence, ignoring or underestimating other evidence. The forensic scientist may appear over- confident, even arrogant, thus impairing credibility. The defence labours under considerable disadvantages. Raising the money for a forensic DNA report may present real problems. Good experts cost money. The defence advocate needs to have a grip of what is involved in the forensic science, to recognise the strengths and weaknesses in a DNA case, to know how to probe, to avoid by inept questioning strengthening the prosecution case, when to contest a point and when to yield a point, and how subtly to plant a doubt in the mind of the jury. Justice requires forensic scientists and lawyer advocates being of the highest quality.

Bibliography J.A.Prahlow et al, (2017)‘DNA testing in homicide investigations’, (2017) 57 Med Sci , 179-191, with full references. Editor, ‘Identification by DNA evidence’, Halsbury’s Laws of England, fifth edition, volume 28, paras 552 and 565.[2017] Crim LR 628, 629-634, with full references.

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The worry about cross-border criminals

Sally Ramage If the United Kingdom (UK) leaves the European Union (EU) without a deal, cross-border criminals will be most pleased, was the opinion of the Law Society of England and Wales.23 This is because the UK will no longer be able to enjoy the legal cooperation presently enjoyed in respect of crime including

23 Editor, ‘No deal a good deal for criminals’, Gazette, 1.3.19. 24

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 terrorist crimes among all the Member States of the European Union.

If the UK leaves the EU without a deal, the UK will have to negotiate with each of the EU Member State a Memorandum of Understanding on the subject of criminal law since without any such agreements it will be difficult to investigate crime in European countries, to arrest suspects, extradite persons and to extract justifiable compensation from criminals assets once tried and convicted in the United Kingdom. After the UK leaves the EU, it will no longer enjoy the benefits of: the European Arrest Warrant; the European Investigation Order; the agency Europol24; or the EU Judicial Cooperation Unit, Eurojust, unless the UK leaves the EU with a deal to assist the UK in the sphere of criminal justice.25 Let us examine one such operation which benefited enormously from European Arrest Warrants. The Phish & Chip operation was at the time, a second generation investigation about phishing. Imagine how worse such cybercrime is today after examining a phishing fraud as related below: March 2005 In Italy, the first phishing e-mail occurred in March 2005.

24 Europol’s crucial role has been recognized for its indispensable multilateral approach. Europol cooperates externally with 18 non-EU countries, nine EU bodies and agencies and three other international organizations. Cooperation with INTERPOL and EU agencies such as Eurojust, CEPOL and Frontex greatly enhances Europol’s overall reach. INTERPOL works in conjunction with police in its member countries. Operations are conducted in all regions of the world and can target a variety of different crime types sometimes organized and conducted with national and regional law enforcement partners. 25See https://www.lawsociety.org.uk/support-services/advice/articles/no-deal-brexit-criminal-justice-cooperation/ 25

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It was a small attack against Poste Italiane, the government-owned postal service that offers financial services across Italy. The first investigation began in May 2005. Soon after this time there followed a larger phishing attack, this time against Banca Intesa plus three other Italian banks which had their registered offices in Milan. At that time police did not understand why the money was not moving directly abroad (from where the phishing e-mails were sent), but moved instead from the defrauded bank accounts to other people living in Italy. Police searched ten homes across the Italy, and then discovered how the money was then transferred abroad to St Petersburg in the Russian Federation, using the Western Union money transfer system. The thieves sent two types of e-mail. The phishing e-mail was sent out to obtain the data of legitimate on-line current accounts of those that responded. Another e-mail was sent, offering work to people as a financial manager. When they had a sufficient number of ‘financial mangers’ and the details of a sufficient number of on-line bank accounts, the thieves then authorised the transfer of funds from the victim’s account, and paid it in to the account of a newly-recruited financial manager. After the money had been successfully transferred, the thieves directed the financial manager to transfer the funds abroad.

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International Seizure Warrant It is for this reason that it is important to profile a cybercrime during the investigation: in this case, as soon as police discovered how this new criminal method worked, the Italian Judicial Authority contacted the Western Union Inc. in the United States of America and an agreement was drawn up. Western Union told the Italian authorities that it was the first time any prosecuting authority had approached them to discuss and reach an agreement which they decided to name International Seizure Warrant. The agreement with Western Union requested Western Union to delay the suspect money transfers for 48 hours, which gave sufficient time to verify whether the transfer was genuine. Italian authorities provided Western Union with a list of the names of people, and destination countries, and Western Union contacted Italian authorities, in real time, providing Italy with the Money Transfer Control Number (MTCN) of the suspect transaction. This was a very demanding exercise, and with help from the Military Financial Police in Milan (Gruppo Repressione Frodi or Antifraud Group), over 250,000.00 Euros were seized in two months, all of which was intended for the Russian Federation.

Year 2006 In 2006, Italy had the first instance of people entering Italy from East Europe to collect money by themselves from phishing attacks, rather than through Western Union bank. In Milan, two Latvians were arrested. Their purpose in moving to live in Italy was to open bank accounts in several banks with false passports and documentation. In the beginning of 2007, a new method of cybercrime26 was immediately discovered through the Phish & Chip operation. The aim of the phishing

26 Years later, as predicted, cybercrime emerged as a critical issue for all commercial organisations and for individuals. It was estimated that in the UK alone the annual cost of cybercrime is £27 billion in 2015 and 27

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 criminal was to remain in his or her own country and send phishing e-mails and receive money without using the Western Union, but using a large number of prepaid credit cards bought in Italy by a group of people directly managed by the phishing criminal. Italian police became aware of this phishing theft in February 2007, when the managers in charge of preventing fraud at Poste Italiane reported some unusual operations concerning prepaid credit cards (called Postepay cards) bought in Milan. The investigation was conducted by Milan Police and the Provincial Command of the Military Financial Police in Milan, the Guardia di Finanza, Gruppo Pronto Impiego, with cooperation of the Romanian investigators of the Brigata de Combattere a Criminalitatii Organizate (the brigade to combat organised crime). The first step of the investigation was to discover the meaning of this unusual buying pattern. Two connected organisations were identified, made up of Italian and Romanian citizens. The operative framework was the same: Members of the criminal organisation activated the prepaid cards (they also used Banca Intesa cards, called Intesa flash cards). The phisher sent e-mails. The Phisher collected the relevant data to enable them to gain access to on-line bank accounts. The boss’s duty was to collect the prepaid cards, paying 50.00 to 100.00 Euro for each. The boss gave the phisher instructions in order to prepay the cards. The phisher was instructed to withdraw the money once each card was topped up. The method used was called the ‘Casinò system’. Further to the monitoring of the prepaid card activations within the territory of Milan, Italian Police analysed all the IP addresses of the illegal on-line bank transfer operations, in order to find any vulnerability in the framework used by the phishers.

especially today as electronic communication and data storage is used by almost everyone, the challenges are rife and exploitation is rife by those who aim to unlawfully acquire our data. See article in 2015 Current Criminal Law, Volume 8, Issue 2, titled Cybercrime.

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At the beginning of the investigation, success seemed a long way off: the criminal bosses used a number of aliases and sophisticated electronic tools. Complete and accurate identification of one of the bosses was obtained by chance because police intercepted telephone conversation on 11 April 2007, during which the person under investigation contacted a car dealer and asked for information concerning the transfer of ownership of a Porsche Boxster motor car purchased a few weeks previously. Italian police then investigated the ownership of the car and found that it belonged to the wife of the person under investigation, and he had underwritten, in her name, the insurance for the car. The operators of the Guardia di Finanza - Gruppo Pronto Impiego (Italian Judicial Police) investigated the credit card used for the payment of the premiums of the insurance, and also the mobile telephone number given to the bank when the current account was opened that was linked to the credit card. The criminal had not only made use of false documents, but one shop in Milan, prepared not to ask questions, provided numerous SIM cards which were registered to fictitious Greek citizens. This criminal phishing association committing the phishing crime used the same SIM card to carry out their illegal activities via the Internet as well as for the conversations between the people taking part in the crime. This enabled police to intercept the gang (after Italian police identified an IP address used by them for the illegal operations over the Internet) and A mixture of house searches, telephone interceptions and the analysis of the content of Internet chats that took place among the various targets in Italy and Romania during the first phase of the investigation guaranteed police a collection of digital evidence and important confirmation relevant to the investigative hypothesis. Execution of Warrants of Arrest The Guardia di Finanza executed 26 Warrants of Arrest for those people belonging to the two criminal associations that took advantage of the home

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 banking service Personal Access Codes of the customers of Poste Italiane and Banca Intesa, one of the most important banks in Italy. The Gruppo Pronto Impiego of Guardia di Finanza di Milano, is perhaps the first attempt to face the phenomenon of the criminal organisations apt to the systematic attempt of phishing in an organic manner, both from the investigative point of view and also contesting offences of association. Charges were brought against 26 people for the offences of: *criminal association, * falsification of IT communication content, *unauthorized access to IT systems, * aggravated fraud, and *unauthorized use of credit cards. All the members of the first of the two criminal organisations had been living in Italy for a number of years. The information system hacker of the first group was a 22 year- old Romanian man. During his questioning at the office of the Italian Prosecutor (which lasted for many hours), he confessed to sending e- mails as if they had been sent by Poste Italiane, and to collecting the data belonging to victims with e-mail addresses of providers operating in Italy, but with servers based abroad. The computer forensic analysis confirmed his confession. Covering the four months of this first phase of the investigation, fraudulent activity amounting to 250,000.00 Euro was discovered by the investigators. In April 2007, the investigators knew that the main person responsible for the second criminal organisation was returning to his home in Craiova, Romania. It was extremely important to ensure there was excellent cooperation between the Milan, Bucharest and Craiova judicial authorities. The investigation was headed by the commissary, Silviu Vacaru, who ensured the efficient coordination and cooperation between the Italian Guardia di Finanza and the Romanian police. Italian authorities asked for and obtained, using the

The Criminal LAWYER Issue Number 242 June 2019 ISSN 0956-7429 necessary rogatory.

Phishing fraud Everything starts from the SIM card: its internal chip gathers information that is updated by the mobile telephone company. The information contained in the chip provides a great deal of data about the life of the SIM card: external data concerning the calls and internet connections made with the SIM card. The exchange of information, often in real time, between the investigators in Craiova and the officials of the Guardia di Finanza in Milan, turned out to be of vital importance for the identification and the subsequent capture of the main person responsible, together with a number of fugitives who escaped to Romania during the July arrest.

Money flows from Italy to Romania had been analyzed and rebuilt: this enabled the fees that were paid to the technical experts who participated in the crime to be identified. Using the result of the Romanian interceptions, in October 2007 the first of two young Romanian phishers was arrested in Craiova. He was extradited to Italy. His confession helped police arrest a second young man, who was much more expert at hacking. One person who assisted them was well known to the criminal underground because he successfully took part in the Romanian Information Technology Olympic Games in 2004. For the first time in a phishing case, the rules of the Italian law that ratified the United Nations Convention against Transnational Organised Crime were applied.

The worry

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When the UK leaves the EU, the agile cooperation model between police and the legal experts will be absent. ENDS

Registered as a Newspaper at the Post Office. Copyright SALLY

RAMAGE ® 2019. All Rights Reserved. No part of this publication may be reproduced in any material form (including photocopying or storing it in any medium by electronic means and whether or not transiently or incidentally to some others use of this publication) without the written permission of the copyright holder except in accordance with the provisions of the Copyright, Design and Patents Act 1988 ; United States Patent and Trademark Office Reg. No. 3,440,915, or under the terms of a licence issued by the Copyright Licensing Agency, Saffron House, 6-10 Kirby Street, London, England EC1N 8TS. Application for the copyright owner’s written permission to reproduce any part of this publication should be addressed to the publisher. Warning: the doing of an unauthorised act in relation to a copyright work may result in both a civil claim for damages and criminal prosecution. The Criminal Lawyer ®.

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UK Border Agency

UK NATIONAL REPORT: SATISFYING LABOUR DEMAND THROUGH MIGRATION

Prof. John Salt, Dr Alan Latham, Dr Pablo Mateos, Dr Janet Dobson, Prof. Peter Wood, Dr Adam Dennett and Ms Viktorija Bauere

The EMN has been established by Council Decision 2008/381/EC and is financially supported by the European Commission. Contents

EXECUTIVE SUMMARY 3 1. INTRODUCTION 6 1.1 Methodology 6 1.2 Definitions 7 2. APPROACH TO ECONOMIC MIGRATION POLICY IN THE UK 10 2.1 National Vision and Policy 10 2.2 Legislative and Institutional Framework 11 2.2.1 Work Permit System (WPS) (1920 to 2008) 11 2.2.2 Other pre-PBS Labour Immigration Schemes 12 2.2.3 Points-Based System (2008-present) 14 2.2.4 Limiting third country migration 16 2.3 Political Debate and Involvement of Other Stakeholders 17 3. APPROACH TO IMPLEMENTING ECONOMIC MIGRATION POLICY 19 3.1 Implementation of Economic Migration Policy/Legislation 19 3.1.1 Shortage occupation list and the role of the MAC 19 3.1.2 Other MAC activities 21 3.1.3 Job-matching 21 3.1.4 Skills assessment and recognition 21 3.1.5 Integration measures 22 3.2 Statistics and Trends 23 3.2.1 Statistics on the labour market and migration 23 3.2.2 Analysis of trends and relevant developments 32 4. CO-OPERATION WITH THIRD COUNTRIES FOR ECONOMIC MIGRATION 44 4.1 Co-operation with particular third countries 44 4.2 Combating brain drain 45 4.3 Stockholm Programme 45 4.4. Youth Mobility Scheme 45 5. ANALYSIS AND CONCLUSIONS 46 5.1 Linking overall labour migration policy and outcomes 46 5.1.1 Fiscal effects 46 5.1.2 Labour market effects 48 5.2 Effectiveness of specific government policies 49 5.2.1 Low-skilled workers 49 5.2.2 Skilled workers 50 5.3 Issues for future policy 50 5.3.1 Demographic change 51 5.3.2 Language and the assimilation of European migrants 51 5.3.3 Government policies 52 ANNEX A: BIBLIOGRAPHY 53 ANNEX B:GLOSSARY - List of abbreviations used in the report 57 ANNEX C:METHODOLOGY AND DEFINITIONS 58 ANNEX D: COMMON STOCK AND FLOW TABLES 64

1 List of figures and tables

Figure 3.1 Work permit applications approved by type, 2004 to 2008 33 Figure 3.2 Highly Skilled Migrant Programme applications approved, 2004 to 2008 37 Figure 3.3 Total approved applicants for Worker Registration Scheme, by quarter and year of application, Q2 2004 to Q4 2009 37 Figure 3.4 All Sectors Based Scheme work permits approved by industry, 2004 to 2008 39 Figure 3.5 Seasonal Agricultural Workers Scheme applications approved 2004 to 2009 40 Figure 3.6 Working Holidaymakers admitted to the UK, 2004 to 2008 40 Figure 3.7 UK employment change (seasonally adjusted) June 2006 to 2008, 2008 to 2010 43 Table 3.1 Granted Main Applications for Tier 2 and Work Permits 2009 34 Table 3.2 Top 10 Tier 2 jobs (4-digit SOC occupation) by total Tier 2 jobs, J uly 2009 to June 2010 35 Table 3.3 Granted Main Applications for Tier 1 and HSMP 2009 36 Table 3.4 Worker Registration Scheme applications approved, 2004 to 2009 38 Table 3.5 Worker Registration Scheme for top 20 occupations in which registered workers are employed, May 2004 to March 2009 38 Table 3.6 Overseas nationals entering the UK and allocated a National Insurance Number 41 Table C1 Aggregation of LFS SOC2000 variables into EMN broad occupational groups 61 Table C2 Aggregation of LFS SOC2000 detailed variables into EMN/ISCO-88 4 digit occupational groups used to produce Table D5 (Stocks of workers employed by specific occupations) 62 Table C3 Aggregation of IPS detailed occupations into EMN broad occupational groups used by ONS to produce Tables D2 and D4 (Flows of workers by main category of employment and nationality) 63 Table D1 Stock of workers by main category of employment, 2004 - 2009 64 Table D2 Flows of workers by main category of employment, 2004 - 2009 70 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 82 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 100 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 124 Table D6 Flows by occupational category, 2004 - 2009 142

2 EXECUTIVE SUMMARY

This report draws on a range of research and policy documents concerning the relationship between labour shortages and migration policies in the United Kingdom (UK). Its analysis is based primarily on available quantitative data from the Labour Force Survey (LFS), to examine trends between 2004 and 2009 in the UK and foreign-born1 labour stock, and the International Passenger Survey (IPS), to measure worker flows in and out of the country during the period.2 Administrative data are also presented which relate to the implementation of past and ongoing UK policy programmes affecting the issuing of work permits to third country3 and European Union (EU) nationals (Section 1).

The approach to economic migration policy in the UK has changed significantly since the mid 1990s, when it was regarded as offering a basis for selecting skilled workers from third countries to support economic competitiveness. From 2011 the emphasis will be on reducing net third country labour immigration by raising the qualifications and salary thresholds required and placing annual limits on the numbers admitted (2.1; 2.2).

The work permit scheme before 2008 defined general skills shortage occupations and was supplemented by specialist provisions for other skilled and less skilled workers (2.2.1; 2.2.2). Rising migrant numbers from inside and outside the European Economic Area (EEA) created pressures to simplify this system, culminating in the introduction of the points-based system (PBS) for third country immigration in 2008. Some 80 separate routes of entry for labour and students were consolidated into five PBS tiers (2.2.3).

The UK Border Agency is responsible for the operation of the PBS and for migration policy. The Migration Advisory Committee (MAC) responds to strategic labour market questions raised by the Government related to the design of the PBS. The results of this process may then be used by the UK Border Agency to support their policy-making process. The MAC defines and reviews shortage occupations qualifying for skilled (Tier 2) Certificates of Sponsorship (CoS). It published its first full list of shortage occupations in autumn 2008. The main evidence was taken from wage and employment trends and vacancy levels, complemented by information from employer surveys and consultations. There were also six-monthly reviews of selected occupational groups (3.1).

The analysis of UK workforce stocks shows a significant shift both in the scale and skill level of the foreign contribution to the UK workforce between 2004 and 2009. The total workforce grew by 443,000, reaching 28.9 million, but the proportion of workers of UK nationality fell from 95 per cent to 92 per cent. The number of foreign workers rose by almost 50 per cent, from 1.526 to 2.285 million. The most significant growth took place in EU10 and EU 2 nationals.4 EU10 nationals 1 The term ‘foreign-born’ refers to those born outside the United Kingdom; the term ‘foreign national’ refers to those not holding UK citizenship. 2 Both sources present problems of sample size and reliability in detailed analyses and inconsistencies in definitions. 3 ‘Third country nationals’ are not citizens of the European Union. 4 EU 10 nationals are those from Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia. EU2 nationals are those from Bulgaria and Romania. 3 increased from 85,000 to over 500,000 and EU2 nationals increased from 12,000 to 61,000 mainly after 2007. Together, by 2009 these made up 24 per cent of the foreign workforce compared with six per cent in 2004. Nevertheless, over half of foreign workers in 2009 were still from third countries. Foreign nationals also made a considerable contribution to the increase of one million highly skilled workers during the period. Of these, 787,000 were UK nationals, a growth of over seven per cent, but third country nationals grew by almost 131,000, (30%). The fastest growth rate was among EU10 nationals whose highly skilled numbers increased by over 54,000 from 17,000 to 71,000 between 2004 and 2009. Both skilled and low-skilled numbers declined in the period, but in both cases a greater loss among UK nationals was partly balanced by gains from the EU10, EU2 and third countries (3.2.1.1). Nationals from seven countries were consistently in the top ten non-EU sources of the UK workforce growth between 2004 and 2009: India, Australia, South Africa, United States, Philippines, Zimbabwe and Pakistan (3.2.1.2).

LFS data also show the contribution of foreign workers to specific shortage occupations.Throughout the period, third country nationals made up the largest numbers, especially in the medical professions, personal care and as cooks. The most rapid growth, however, was in EU10 nationals, especially in catering, child and personal care, and labouring, which also attracted significant numbers from EU2 countries (3.2.1.3).

The flow data, based on IPS sources, highlight recent developments in labour migration patterns, although reliable data are available only for highly skilled and skilled inflows and outflows by UK and third country nationals, including those from India and Australia. There was considerable variation from year to year, peaking in 2008, but over two-thirds of the predominantly male in- and outflows of these two nationalities were highly skilled (3.2.1.4).

Estimates from the Office for National Statistics (ONS) suggest that the economic downturn had some impact on labour immigration into the UK in 2009, although this may also have been affected by the introduction of the PBS in 2008. They also confirm that the net inflow of foreign professional and managerial workers, especially from the lesser developed economies, more than made up the loss of UK workers through net emigration, and that foreign workers supplied most of the increase in clerical and manual labour (3.2.1.6).

The MAC reviews since 2008 have more than halved the numbers employed in shortage occupations under Tier 2 of the PBS. This appeared to be due more to refinements of its methodology than the direct effects of the economic downturn (3.2.2.3).

Administrative data demonstrate how the pre-2008 work permit schemes were implemented, favouring science and technology occupations and senior personnel, a high proportion of whom were from India. Other routes were predominantly used by nurses, medical practitioners, care workers and catering staff including a high proportion of Filipinos (3.2.2.1).

4 The UK does not have many bilateral agreements on economic migration. There are arrangements primarily to control recruitment by the National Health Service (NHS) from third countries, and support visits to the UK by doctors from such countries for training purposes. There are also arrangements to support temporary visits to their origin countries by members of diaspora-based organisations and highly skilled UK-based professionals (Section 4).

Research evidence for the economic effects of migration indicates that the scale and nature of fiscal benefits of labour migration to the economy are unclear and the effectiveness of labour migration policies is uncertain. Most studies of the effects of immigration on the wages and the employment prospects of UK workers find them to be small or absent. There is some evidence of negative employment effects (e.g. reduced wages or increased unemployment) of migration overall for those with intermediate levels of education but positive effects on the better qualified. No statistically significant impact of EU10 migration has been detected on aggregate claimant unemployment, either in total or for any identifiable subgroup. In particular, there is no evidence of any adverse impacts of EU10 migration on the young or low-skilled, nor was a significant impact detected on wages, either on average or at any point in the wage distribution (5.1).

The evidence presented for the nationality and occupations of third country nationals in this report suggests that the UK policy of confining them to skilled workers, including those in shortage occupations, has generally been successful. Intra-company transfers (ICTs) present particular difficulties, however, in the context of growing economic globalisation. The November 2010 measures to tighten conditions of entry exempted these from the limit imposed on other Tier 2 routes of entry (5.2).

It is difficult to assess directly the effectiveness of government policy on labour migration and shortages in the period 2004 to 2009. The introduction of the PBS, economic downturn and the expansion of the EU all affected the quantitative trends. Other factors will also affect the role of third country immigration in the future. They include longer-term demographic changes, the degree of assimilation of European migrants and policy changes affecting labour demand in specific areas such as health and education (5.3).

5 1. INTRODUCTION

The aims of the study are: to understand the migration strategies adopted by the United Kingdom (UK) Government between 2004 and 2009 to address labour market needs and shortages; to assess the perceived effectiveness of these strategies; and to examine the impact of the economic downturn and recovery on these strategies. It presents data and analysis to indicate the effectiveness of the approaches adopted with particular emphasis on the last two years when the new points-based system (PBS) for managing labour migration was introduced. It seeks to inform policy-makers and analysts about the migration policy options available to address the needs of labour demand, both in respect of long-term skills and in response to the impacts of changes in the economy. The report has been written by staff from the Migration Research Unit in the Department of Geography at University College London.

Although the study specification refers to third country labour sources, the authors have included reference to intra-European Economic Area (EEA) mobility – especially from the A8 states (Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia) – because of its importance in UK labour migration since May 2004. The report also includes information on intra- company transfers (ICTs) which are a major element in the immigration of highly skilled people.

1.1 Methodology

The study adopts both qualitative and quantitative methods. Government policy developments during the period of interest (2004 to 2009) are summarised in a documentary review, which focuses particularly on the introduction of the new PBS for managing migration in 2008.

Most of the evidence, however, is based on statistical sources, predominantly from large scale national surveys; the Labour Force Survey (LFS) and the International Passenger Survey, (IPS), both produced by the UK Office for National Statistics (ONS). Government administrative data on migrant entry are also examined.

Small sample sizes may make some data unreliable. Only cells with adequate sample size (e.g. cell sizes of 10,000 or more for LFS data and standard errors of 20 per cent or less for IPS data) will be discussed in the report.

Interpretation of vacancy data also needs to take into account changes in Jobcentre Plus procedures in 2006 which prevent comparison before and after this date.

The statistical sources, together with other details on methodology, are described in Annex C.

6 1.2 Definitions

1.2.1 Migrant Stock Common Tables

Tables D1, D3 and D5 (see Annex D) include figures on migrant stocks and were compiled using the quarterly LFS for 2004 to 2009 as the only source of data providing details of workforce nationality. They show the number of migrants by occupation groups.

In Tables D1 and D2 vacancy data comprise vacancies notified to Jobcentre Plus offices. Jobcentre Plus data do not provide information relating to all vacancies in the economy. For Table D5 no data were available for vacancies by specific occupation.

1.2.2 Migrant Flow Common Tables

Tables D2, D4 and D6 include figures on migrant flows and were compiled from the IPS and include standard errors (provided by ONS). They exclude short term moves of less than a year. Inflows into the labour market are composed of two groups: those already in the country who obtain jobs, and those recently coming from overseas. Similarly, among those leaving the labour market, some go into non-employment or unemployment in the UK while others leave the country. Ultimately there is no way of knowing how many exits from the labour market are also exits from the country.

1.2.3 Occupation groups

The LFS uses the UK Standard Occupational Classification (SOC2000) which is similar but not identical to the International Standard Classification of Occupations (ISCO-88) the classification requested by EMN. An approximation has been constructed between the detailed SOC2000 categories and the ISCO-88 major groups with the objective of aggregating them to the five major groupings specified by EMN and listed below. See Annex C for details of how this was done.

Once the data were constructed at the ISCO-88 level the definitions of the broad occupational groups given in the EMN specifications were followed:

a) Highly Skilled (Highly Qualified Migrant): ISCO-88 Major Groups 1–3 (1 digit); b) Skilled: ISCO-88 Major Groups 4 – 8 (1 digit); c) Low-skilled: ISCO-88 Major Group 9 (1 digit); d) Researcher: ISCO-88 Minor Group 1237 (4 digit); e) Seasonal Worker Migrant: not available in the UK.

It should be noted that the EMN occupational categories above do not map on to the skill categories used in the UK’s current PBS immigration system (see section 2.2.3 for more details on PBS skill levels). In the EMN classification used in this report, highly skilled workers include a wide range

7 of occupations such as medical practitioners, senior government officials, architects, accountants, lawyers, teaching professionals, civil engineers, nursing and midwife professionals, along with authors, journalists and other professionals in the creative arts. Skilled workers include data entry operators, fire fighters, prison officers, dairy and livestock producers, chefs and cooks, childcare workers, bakers, plumbers, and motor vehicle mechanics. Low skilled covers occupations from messengers, domestic helpers and cleaners, building caretakers, to labourers in mining, manufacturing, construction, and transportation. The category of researcher includes a small subset of highly skilled workers employed to undertake research such as research scientists. See Annex C for more details on how the occupational groups were aggregated for this study.

1.2.4 Nationality

The following national groups were included in Tables D1-D5 by aggregating individual countries according to EMN definitions:

1. UK nationals 2. Other EU15 Nationals (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, and Sweden. The UK is excluded) 3. EU10 Nationals (Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia) 4. EU2 Nationals (Bulgaria, Romania) 5. Third Country Nationals (Rest of the World)

These national groups are consistent throughout the period of analysis in this report (2004 to 2009), regardless of when individual countries joined the EU.

In the tables, EU refers to the 27 member states; the EEA additionally includes Iceland, Liechtenstein, Norway and Switzerland; third countries are the rest of the world.

In parts of the report the authors also refer to ‘A8 countries’, which is a subgroup of eight EU10 accession countries (all EU10 above except Cyprus and Malta). A8 countries had a significant impact on the UK labour migration over the study period, and the exclusion of Cyprus and Malta is justified since they already enjoyed full access to the UK labour market when they joined the EU and the numbers of Cypriots and Maltese are relatively small so make little difference to the overall analysis.

1.2.5 Workers

Tables D1, D3 and D5 (Stocks) use the LFS definition of employed people:

“Employed persons are persons aged 15 years and over who, during the reference week, performed work, even for just one hour a week, for pay, profit or family gain, OR who were not at work but

8 had a job or business from which they were temporarily absent because of e.g., illness, holidays, industrial dispute or education and training.”

Tables D2 and D4 use IPS data where a migrant is defined as employed if they gave work as their principal reason for coming to or leaving the UK.

In Table D6 an alternative measurement of labour flows, derived from the IPS, is used because it allows a breakdown by major skill groups. In this measurement, a labour migrant is someone who was in the labour market before and after moving.

See Annex C for more details.

1.2.6 Migrants

The definition of migrants used in Tables D2, D4 and D6 (flows) are those used for the IPS. An immigrant is someone who enters the country with the intention of staying for a year or more, having lived outside the country for a year or more. An emigrant is someone who leaves the country with the intention of being away for a year or more, having lived in the country for a year or more. IPS data are based on intentions and so it is likely that they exclude people who switch statuses. An adjustment is made for these, details of which are in Annex C.

In Tables D1, D3 and D5, stock data from the LFS relate to all people residing in the UK for at least six months prior to the survey.

9 2. APPROACH TO ECONOMIC MIGRATION POLICY IN THE UK

2.1 National Vision and Policy

This section provides an overview of the UK’s recent approach to economic migration policy. This has been dominated by the change in 2008 to the PBS for labour immigration by third country nationals and by the move to reduce net migration and limit third-country migration, introduced following a change of government in 2010. Migrants have been regarded by successive UK governments as having a significant role to play in combating labour shortages and achieving economic goals. However, in recent years there has been an increasing emphasis on specifying required skills and more recently on limiting numbers.

Economic competitiveness became a major theme in UK migration policy during the latter part of the 1990s. The Government recognised that the UK needed “to attract the most skilled and enterprising people from abroad to add to the skill pool of resident workers” (HM Treasury, 1999). Hence it would make it “easier for skilled foreign workers in key areas to come and work in the UK, where they have the skills and attitudes to help generate an enterprise economy” by extending the shortage occupation list for work permit purposes, reviewing work permit arrangements and investigating ways of attracting foreign entrepreneurs and small investors to come and start businesses in the UK.

To increase the flow of foreign workers into the UK economy, the Labour Government also instituted a number of schemes, for example the Highly Skilled Migrants Programme (HSMP) and the Workers Registration Scheme (WRS), aimed at particular sectors and occupations. These schemes are discussed below. During this period there was a large inflow of workers from the Eastern European states that joined the EU in May 2004.5 The UK was one of three countries, with Ireland and Sweden, which did not require a transition period for labour immigration from the new members and allowed instant access to the UK labour market.6

Increasing numbers of migrants, together with increasingly complex routes of entry, led the Government to begin a process of reviewing and simplifying the mechanisms for managing the various routes of entry for third country labour. After widespread consultations, this led to the introduction of the PBS in 2008 (see below) which was designed to manage the inflows of economic and student migrants from outside the EU. No quotas were established, since it was assumed that the number of points required for entry could be changed as circumstances or policy dictated. As part of these changes, the Migration Advisory Committee (MAC) was established to advise the Government on whether skilled labour shortages exist that can be filled by suitably skilled migrants from outside the European Economic Area.

5 Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovak Republic, Slovenia. 6 In contrast, a transition period was required for Bulgaria and Romania after their accession in 2007. 10 After the General Election of May 2010, the Coalition Government announced that the number of workers entering the UK from outside the EEA would be subject to new limits. A speech in September 2010 by the Minister of State for Immigration stressed that the UK has always benefited from immigration, but “will only continue to do so if it is properly controlled”.

“We absolutely need sustainable immigration levels. This will relieve pressure on public services, and stop immigration being such a delicate political issue… At the same time, we must be confident enough to say Britain is open for business and study to those who will make this a better country, and a more open society.” (Green, 2010)

Subsequently, in November 2010, the Home Secretary emphasised the importance of ensuring that only the “brightest and the best” can come to the UK. She made it clear that the Government was determined to increase the number of high-value migrants coming to the UK, including investors and research scientists, while encouraging employers to fill vacant jobs with people who were already in the country and out of work (May, 2010).

2.2 Legislative and Institutional Framework

2.2.1 Work Permit System (WPS) (1920 to 2008)

The WPS was used to manage the flows of most third country workers. Work permits were issued to employers on behalf of nominated foreign workers who were permitted to take up employment in the UK for periods normally up to four years. Until the introduction of the PBS there were four types of work permit:

●● work permits issued for third country workers from outside the UK;

●● first permissions issued for third country workers already living in the UK;

●● extensions for employees to continue working for the employer beyond the expiry date of the initial permit;

●● changes of employment, either to a new employer or for a change of job type with the existing employer.

The WPS (which included all four types) was market driven and aimed at filling shortages in the labour market. It was based on the assumption that most immigration would be temporary and that in due course any chronic shortages would be alleviated by training of domestic workers. Dependants of work permit holders were also allowed to enter the labour market. Although a permit was granted for a defined period, there are no statistics on how long permit-holders actually stay in the UK, since the UK does not currently conduct exit checks.

11 2.2.2 Other pre-PBS Labour Immigration Schemes

During the period covered by this report, before the introduction of the PBS in 2008, the Government operated several schemes, which supplemented the WPS and were designed to manage labour immigration for other skilled and less skilled workers. There was some overlap between the schemes and the PBS as the PBS tiers were introduced gradually between 2008 and 2009.

Highly Skilled Migrants Programme (HSMP) (2002 to 2008) This was launched in January 2002 as a new initiative to allow individuals with exceptional skills and experience to come to the UK to find work or self-employment. Unlike the main work permit scheme, no prior offer of employment was necessary and permission was granted to the individual worker and not tied to a post offered by an employer. It was therefore novel in not being directly related to a perception of labour shortage. Furthermore, for the first time, from 2006 a UK scheme used a points-based system similar to those in Australia and Canada. To make a successful application, individuals needed to demonstrate that they would be able to continue their chosen career in the UK and also provide evidence that they scored 75 points or more in five areas: educational qualifications; work experience; past earnings; achievement in the chosen field; and HSMP priority applications.7 In the new PBS, HSMP was replaced by Tier 1 from February 2008.

Science and Engineering Graduates Scheme (SEGS) (2004 to 2008) and International Graduate Scheme (IGS) (2007 to 2008) The SEGS was launched in 2004. It allowed third country nationals who had graduated from UK higher or further education establishments in certain mathematics, physical sciences and engineering subjects with a 2.2 degree or higher to remain in the UK for 12 months after their studies in order to pursue a career. An amendment to the scheme in 2006 allowed all international students with a higher degree gained from a recognised UK higher education institution to be eligible for SEGS if they commenced their study on or after 1 May 2006.

In March 2007, SEGS was replaced by the IGS, under which any pass degree class was eligible; those with post-graduate certificates and diplomas, such as a Post Graduate Certificate in Education (PGCE), were also eligible to apply. IGS was meant to be a transitional route from studying in the UK to skilled employment. To stay in the UK after the 12 month IGS period, migrants needed to apply for and meet the criteria of another immigration scheme. In 2008, IGS was replaced by the post-study work route (PSWR) under Tier 1 of the PBS.

Seasonal Agricultural Workers (SAWS) (1946 to present) This originated in the years following the Second World War. It was designed to facilitate the movement of young people from across Europe to work in agriculture, particularly in peak seasons. Participants were mainly students aged between 18 and 25. The scheme is managed

7 This category allowed the government to encourage people with a particular skill or profession to move to work in the UK. The category was largely restricted to general medical practitioners. 12 by nine approved operators on behalf of the UK Border Agency which issues a fixed number of work cards to them each year. Operators recruit participants, allocate them to farms and ensure they receive the appropriate wages and conditions, including suitable accommodation. Quotas are used to manage the numbers participating in the scheme. Over the last decade or so, the principal nationalities involved have been from Eastern Europe and the former USSR (especially Ukraine). However, since their accession to the EU in 2007 the scheme has been reserved for nationals from Bulgaria and Romania.

Sectors Based Scheme (SBS) (2003 to 2008) This was introduced in May 2003 to address shortages in lower skilled occupations, initially in food processing and hospitality. Permits were available where there were shortages of resident workers in certain positions requiring qualifications below National Vocational Qualification (NVQ) level 3 and were issued for overseas employees aged 18 to 30 to work for up to 12 months. Permit holders were not allowed to bring their spouses or dependants and had to leave the country when the permit expired. Employers applied for SBS permits on a first come, first served basis.The scheme operated on a quota system of 10,000 per year initially but this was reduced as a result of the accession of new EU member states. In July 2005 the hospitality sector was withdrawn from the scheme except for extensions and changes of employment. From 2007 to 2008 the SBS was reserved for nationals of Bulgaria and Romania.

Workers Registration Scheme (WRS) (2004 to 2011) The UK was one of three countries, with Ireland and Sweden, which allowed nationals of the acceding countries to the EU in May 2004 free access to its labour market. To monitor the flow the WRS was introduced. Nationals of the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia or Slovenia (A8) who want to work for one month or more for a UK employer must register under the WRS. However, although registration is compulsory, in practice many workers do not register. Once they have been working in the UK for 12 months without a break they have full rights of free movement and no longer need to register on the WRS. They can then get a registration certificate confirming their right to live and work in the UK. Self-employed workers are exempted from this scheme under the right of establishment provisions of the EU Treaties. Hence, although WRS data provide an important indication of the trend in recruitment from the A8 countries, they do not provide a definitive account of numbers. The scheme ceased to operate from April 2011.

Working Holidaymaker Scheme (WHS) (1950s to 2008) This was designed after the Second World War to allow young people from Commonwealth countries to come to the UK for up to two years and was known as the Commonwealth Working Holidaymaker Scheme. The scheme brought in a significant, temporary, flexible workforce and allowed them to experience life in the UK, although it is not possible to know how many were working at any time. It underwent the several changes before becoming the WHS. Participants in the WHS scheme were allowed to work for 12 months out of their two-year holiday.

13 The WHS was abolished along with a number of other youth cultural provisions when the new, single Tier 5 Youth Mobility Scheme (YMS) under the PBS was introduced in November 2008. When the WHS was first created, it was primarily used by applicants from Australia, New Zealand, Canada and South Africa. By the time the scheme ended, it had expanded and was open to applicants from all Commonwealth countries. The Tier 5 YMS is open to applicants from Australia, Canada, Japan, New Zealand and Monaco,8 and reciprocal arrangements exist with these countries.

2.2.3 Points-Based System (2008-present)

This was introduced in 2008 and has gradually replaced the WPS, although work permits are still used for economic migration from the EU2 countries. The PBS replaces over 80 separate routes of entry for work and study. Like its predecessor, there is no overt element of return, beyond the assumption that at the conclusion of the visa period the migrant will return home, especially in cases where extensions and switching between tiers are not permitted. Of course, PBS migrants are still subject to enforced return action if applicable.

Between 2004 and 2009, the then UK Government’s approach was dominated by the transition to, and implementation of, this system. The current Government, elected in 2010, is consulting on and implementing changes to the PBS that will modify some of its original intended purposes. For example, the proposed closure of the Tier 1 Post Study Work Route (see 2.2.4). Under the PBS, third country labour migrants (and students) seeking entry to the UK must accumulate a certain number of points, based on attributes which include age, language ability, experience, qualifications and former salary.9 The number of points accorded to each attribute varies between its five tiers. The number of points may be adjusted to labour market conditions at the time and to the broader strategic migration objectives of the Government.

The PBS comprises five tiers.

Tier 1 Highly Skilled Workers, Investors and Entrepreneurs (commenced February 2008) This category allowed migrants from third countries to enter the UK without a job offer. There were four routes.

●● General (highly skilled workers) – for people who wish to obtain highly skilled employment in the UK (closed to all new applicants since April 2011).

●● Investor – for high net worth individuals making a substantial financial investment in the UK. They might not seek work nor are they under any obligation to do so.

8 The YMS is also open to British Overseas Citizens, British Overseas Territories Citizens or British Nationals (Overseas) as defined by the British Nationality Act 1981. 9 Details of the points requirements can be found at http://ukba.homeoffice.gov.uk/sitecontent/newsarticles/2010/nov/58-mac-report-annual-limit 14 ●● Entrepreneur – for those wishing to invest in the UK by setting up or taking over, and being actively involved in the running of a business.

●● Post-study work (PSWR) – for international graduates who have studied in the UK.

Tier 2 Sponsored Skilled Workers (commenced November 2008) These must be sponsored by an employer who must be licensed by the UK Border Agency. There are four categories.

●● General – for people coming to the UK with a job offer to fill a gap that cannot be filled from within the resident labour force. This category is also for applicants coming to fill shortage occupations.

●● Intra-company transfer (ICT) – for employees of multi-national companies who are being transferred by an overseas employer to a skilled job in a UK-based branch of the company.

●● Sportsperson – for elite sportspeople and coaches whose employment will make a significant contribution to the development of their sport at the highest level.

●● Minister of religion – for those people coming to fill a vacancy as a minister of religion, missionary, or member of a religious order.

Employers receive a Certificate of Sponsorship (CoS) for each worker. This is an electronic document including a unique reference number provided to the migrant by the employer which allows the migrant to complete their visa (or extension) application to the UK Border Agency. A third country worker needs a CoS when applying for a visa to come to, or stay in, the UK. They also need to pass a points-based assessment.

Tier 3 Low-skilled Workers (suspended) This is for low-skilled workers but is currently suspended as it is considered that there is sufficient labour available from within the EU in this category.

Tier 4 Students (commenced March 2009) Under the PBS, educational institutions must apply to be sponsors in order to recruit third country students to study in the UK under Tier 4. The student must also apply to the UK Border Agency to be granted permission to come to the UK once they have been issued with a Confirmation of Acceptance of Studies (CAS) by a Tier 4-licensed sponsoring institution.

Tier 5 Temporary Workers and Youth Mobility (commenced November 2008) Tier 5 is for temporary workers and is not intended to fill labour shortages. There are six categories.

15 ●● Creative and sporting – for people coming to the UK to work or perform as sportspeople, entertainers or creative artists for up to 24 months.

●● Charity worker – for people coming to the UK to do voluntary, unpaid work for a charity.

●● Religious worker – for people coming to the UK to work as religious workers. Duties may include preaching, pastoral and non-pastoral work.

●● Government authorised exchange – for people coming to the UK through approved schemes that aim to share knowledge, experience and best practice.

●● International agreement – for people coming to the UK under contract to provide a service that is covered under international law.

●● Youth Mobility Scheme (YMS) – young people are sponsored by sending governments and are able to come to the UK for up to two years and are allowed to work during their stay. Currently only Australia, New Zealand, Canada, Japan and Monaco participate.

The operation of the PBS requires the active participation of employers and educational institutions that wish to sponsor workers or students from third countries (except under Tier 1). They need first to obtain a sponsor licence from the UK Border Agency. Once licensed, they must undertake a number of sponsorship duties, including having good human resource systems in place to monitor and keep records of the migrants they employ, report any who cease to attend for work, and ensure that none are working illegally.

Employers and educational institutions therefore now have a greater role in ensuring compliance by migrant workers. Failure to follow correct procedures may result in the loss of their right to sponsorship and, in certain circumstances, employers may also face criminal action and fines. Sponsoring employers must ensure that a migrant worker is legally allowed to do the job and has the right registration or professional accreditation where needed by law. They must also keep a copy of the registration document or certificate and make it available to the UK BorderAgency on demand.

2.2.4 Limiting third country migration

In 2010, the Coalition Government outlined its intention that annual net immigration should be reduced in the Coalition Agreement: “We will introduce an annual limit on the number of non-EU economic migrants admitted into the UK to live and work. We will consider jointly the mechanism for implementing the limit” (Cabinet Office, 2010).Towards achieving this, several interim measures were introduced: the number of points required under Tier 1 of the PBS was increased; in June 2010 an interim limit was introduced on the number of Tier 1 (general) migrants entering the UK; and a temporary limit was imposed on Tier 2 labour migrants from outside the EEA. The interim limit

16 was designed to reduce numbers across the route by five per cent, before a permanent limit to be introduced from April 2011, following a public consultation.

On 23 November 2010 the Home Secretary announced the level of the first annual limit for third country workers, to take effect from April 2011. This included:

●● introducing an annual limit of 21,700 for those coming into the UK under the skilled and highly skilled routes – 20,700 under Tier 2 (General) and 1,000 under a new Tier 1 ‘Exceptional talent’ route;

●● raising to £40,000 the minimum salary for those coming as ICTs under the Tier 2 route for more than 12 months;

●● restricting Tier 1 to all but entrepreneurs, investors and the exceptionally talented;

●● requiring occupations in Tier 2 (general) to be at graduate level.

Additionally for ICTs, stays are restricted to five years and there is a lower salary limit (£24,000) for workers coming as ICTs for up to a year.

For Tier 1, where an employer sponsor is not required, there will be no restriction on the numbers entering as entrepreneurs or investors as the Government wishes to attract more people in these categories. The new category for exceptional talent will be limited to 1,000 places in its first year of operation. In 2011, the Government consulted on the functioning of the student immigration system.10 This included consulting on whether the Tier 1 Post Study Work Route should remain open. Following the public consultation, it has been announced that the PSWR will be closed from April 2012.

2.3 Political Debate and Involvement of Other Stakeholders

During the period under review there has been general agreement among the major UK political parties about the need for reform of what had become a complex management system with many separate routes of entry. The new PBS was put through Parliament by the Labour Government and accepted by the new Coalition Government in 2010. The main debates on immigration have focused on the large- scale movements from Eastern Europe after the 2004 enlargement of the EU. Particular attention continues to be paid to the local implications for the provision of housing, education and health services.

10 For more information on the public consultation see http://www.ukba.homeoffice.gov.uk/sitecontent/documents/policyandlaw/consultations/students/ 17 Migration policy has not been devolved to the Scottish Parliament, Welsh and Northern Ireland Assemblies and remains the business of the UK Government. Frequent concerns are expressed about migration levels and impacts on services by the Local Government Association (LGA), an umbrella body which includes all local authorities. By commissioning research and encouraging debate through workshops and conferences, the LGA lobbies for medium- and longer-term strategies based on better local population estimates, to take account of rapid changes due to international migration. It also researches the impact of international migration on services and the community infrastructure, including the costs and how they are covered.

Public consultations about the introduction of the PBS were also carried out by the Government with a wide range of stakeholders, including employers, education providers, trades unions and individuals. Initial consultations were wide ranging, including whether quotas were appropriate, what fees might be charged and how points might be allocated.

A Migration Advisory Committee has been established to guide immigration policy and steer its implementation. There is a continuing dialogue between the MAC and stakeholders with respect to the identification of labour shortages. For instance, in its analysis of the operation ofTier 2 of the PBS (Migration Advisory Committee, 2009a), the MAC listed a number of themes emerging from its consultations with stakeholders.11

●● The resident labour market test route was seen by many stakeholders as essential in terms of bringing much needed skills into occupations not experiencing a national labour shortage but perhaps a more localised shortage.

●● Stakeholders recognised the need to compete with other countries engaged in attracting global talent.

●● Stakeholders argued that ICTs were essential to the successful operation of UK businesses but that there was a need to protect the jobs and pay of UK workers.

●● Stakeholders said that there was a need for flexibility in how the PBS is operated by government.

●● Stakeholders argued that dependants of PBS immigrants who work in the UK make a direct positive economic contribution, and that, furthermore, it was important for dependants’ dignity that they had the right to work in the UK.

11 Consultations included a call for evidence and a comprehensive programme of face-to-face engagement with key individuals, employers, and public and private sector bodies. 18 3. APPROACH TO IMPLEMENTING ECONOMIC MIGRATION POLICY

3.1 Implementation of Economic Migration Policy/Legislation

The UK Border Agency, an Executive Agency of the Home Office, is responsible for the formulation and implementation of the UK’s migration policy. Two bodies were established in 2007 by the Government at the time – the Migration Advisory Committee to advise on economic and labour market issues related to migration, and the Migration Impacts Forum, which is no longer active, to advise on the local impacts of migration.

The MAC is an independent body funded by the UK Border Agency and comprised of economists and migration experts. It was set up primarily to advise the UK Government on where immigration from outside the EEA might fill shortages of skilled labour in the UK economy The MAC’s approach to recommending occupations and job titles for inclusion on, or removal from, the shortage occupation list includes taking account of possible alternative strategies to filling labour shortages such as removing obstacles to home recruitment, improving training regimes or changing production methods.

3.1.1 Shortage occupation list and the role of the MAC

The shortage occupation list is owned by the Government and it is ultimately for the Government to decide whether or not an occupation or job title is included. The MAC recommends the composition of the list, but the Government is under no obligation to accept the MAC’s recommendations. The shortage occupation route is one of the points of entry under the Tier 2 (General) category of the PBS (see 2.2.3).

The MAC uses a three-stage approach to drawing up the shortage occupation list for the UK as a whole but it also draws up a separate list for Scotland. It considers whether:

●● individual occupations or jobs are sufficiently skilled to be included on the shortage occupation list;

●● there is a shortage of labour within each skilled occupation or job; and

●● it is sensible for immigrant labour from outside the EEA to be used to fill these shortages.

The full methodology is described in the Migration Advisory Committee, (2008).12

12 To produce the shortage occupation list, a hybrid method is used which combines the consistency and comprehensiveness of a ‘top-down’ approach, using national data from the LFS, Annual Survey of Hours and Earnings, National Employers Skills Survey and JobCentre Plus. These are combined with the context of fine-grained detail and contextualisation of a ‘bottom-up’ method using evidence submitted by employers, trades unions and other stakeholders. Bottom-up evidence is derived from an examination of individual occupations and job titles. 19 Either occupations or job titles may be placed on the shortage occupation list. The basis of the work is the UK’s SOC2000 classification of occupations (Office for National Statistics, 2000). In some cases labour shortages occur at the sub-occupational or job title level. In that event, bottom-up evidence is particularly important.

The first full recommended occupation shortage list for Tier 2 of the PBS was published in autumn 2008, just before the recent economic downturn began (Migration Advisory Committee, 2008). The list was to be subject to partial review every six months, with a complete review every two years. Three partial reviews of the shortage list have been published, in April and October 2009 and March 2010 (Migration Advisory Committee, 2009a; 2009b; 2010a). The autumn 2010 full review was delayed as a result of the general election of May 2010. The advantage of partial reviews is that the MAC is able to look at key occupations it selects, or is requested to review by the Government, in much greater detail than in a full review. The Government has so far accepted almost in full the shortage occupation lists that the MAC has recommended. The shortage occupation list can be seen on the UK Border Agency website.13

There is no universal definition or measure of ‘shortage’. Consequently, a range of indicators is used with careful attention paid to the bottom-up evidence on shortage. The main evidence is from price and wage signals, along with employment trends and vacancy levels. This is complemented by information from employer surveys on where they think there are shortages. Overall, four basic sets of indicators of shortages are identified:

●● price-based indicators (e.g. earnings growth);

●● other indicators of imbalance based on administrative data (e.g. vacancy duration or vacancy/ unemployment ratios);

●● volume-based indicators (e.g. employment or unemployment); and

●● employer-based indicators (e.g. reports of shortage).

The MAC’s research programme is directed to improving its methodology for identifying occupational shortages. Recent projects include: estimating potential labour shortages and supply in the EEA; a review of theoretical approaches to estimating skill needs; and refining the top-down methodology for identifying shortages in skilled occupations.14 Analyses have also been carried out of employer use of immigrant labour in selected occupations; the relationship between production technology, skills and migration; and the factors associated with employer use of migrant labour (Migration Advisory Committee, 2010b).

13 http://www.ukba.homeoffice.gov.uk/sitecontent/documents/workingintheuk/shortageoccupationlist.pdf 14 Details of these studies may be found at http://www.ukba.homeoffice.gov.uk/aboutus/workingwithus/indbodies/mac/. Last accessed 7/3/11 20 3.1.2 Other MAC activities

Although the original remit for the MAC was principally to advise on labour shortages, it has been asked to perform other, related tasks. These have included assessing the likely labour market impact of relaxing transitional measures for A2 and A8 nationals, and reviews of Tiers 1 and 2 of the PBS. In late 2010, it was asked by the Coalition Government to advise on the level of the first annual limit to Tiers 1 and 2. It reported on 18 November and its main recommendation on the required reduction in the overall number of Tier 1 and Tier 2 visas was accepted by the Government. The Government subsequently set the limit on Tier 2 at 20,700 (excluding ICTs) and the Tier 1 limit at 1,000. Setting the Tier 1 limit at 1,000 (13,000 fewer than the previous year), has allowed the Tier 2 limit to be set at 20,700, an increase of nearly 7,000. Overall therefore, economic migration through Tiers 1 and 2 will be reduced from 28,000 in 2010 to 21,700 in 2011.

3.1.3 Job-matching

A threefold approach is adopted under Tier 2 of the PBS to matching immigrant skills to UK labour market need. First, an employer has to be accepted by the UK Border Agency as a sponsor. Second, the prospective migrant must obtain a visa. To do so, they must have the requisite qualifications and the pay offered must be sufficient to meet the PBS points criteria. Finally, if the occupation is not on the shortage list, or is not an ICT, the employer must have carried out a resident labour market test15 to determine that a suitably qualified EEA worker is not available.

3.1.4 Skills assessment and recognition

The prime responsibility for recognising skills and qualifications within occupations and jobs lies with the Sector Skills Councils (SSCs). These are independent, employer-led, UK-wide organisations designed to build a skills system that is driven by employer demand. There are currently 23 SSCs covering over 90 per cent of the economy and they work towards the following four key goals: reducing skills gaps and shortages; improving productivity, business and public service performance; increasing opportunities to boost the skills and productivity of everyone in the sector’s workforce; improving learning supply through national occupational standards and apprenticeships; and further and higher education.16 The SSCs have no specific remit for assessing the skills of migrants but they provide the yardstick against which those skills can be measured. Except for employers, the main mechanisms for assessing the skills and qualifications of migrants are in the local knowledge of visa offices in origin countries.

The National Recognition Information Centre for the UK (UK NARIC), is the national agency responsible for the provision of comparative information and advice on international education and training systems and overseas skills and qualifications. It helps both organisations and individuals by 15 For details of the resident labour market test process see http://www.ukba.homeoffice.gov.uk/employers/points/sponsoringmigrants/employingmigrants/residentlabourmarkettest/ 16 Details of the SSCs can be found at http://www.ukces.org.uk/sector-skills-councils/about-sscs/the-list-of-sscs/ 21 relating overseas to UK qualifications and is the organisation that provides ‘translations’ of overseas qualifications when supplied as part of a visa application.

The MAC argues that there is no unique, objectively defined measure of skill. For the purpose of Tier 2 of the PBS it is for the UK Border Agency, not the MAC, to determine the requirement for an occupation to be considered ‘skilled’. The UK Border Agency initially defined a skilled occupation as one that is skilled to at least level 3 of the National Qualifications Framework (NQF).The MAC used three top-down indicators to identify occupations skilled to this level:

●● the Standard Occupational Classification (SOC) level;

●● the qualifications of the workforce; and

●● the median hourly earnings of all employees.

Additionally, it used two indicators of skill that were primarily available from bottom-up evidence:

●● the level of innate ability; and

●● the required on-the-job training or experience.

3.1.5 Integration measures

There are no compulsory integration measures in place for all migrants. For skilled migrants, there is an expectation of reasonable fluency in the English language. Under PBSTier 2, language capability is included in the calculation of points for an individual migrant and the requirements can be changed in order to alter the focus of the Tier. For example, in 2011, as part of the effort to re-focus the Tier 2 General route as one for skilled employment with graduate occupations only, the English language requirement for applicants under this category was also raised.

The UK utilises the European Integration Fund (EIF) to promote third country legal migrant integration. EIF-funded projects include the delivery of English for Speakers of Other Languages (ESOL) and life skills courses, IT training, introductory programmes informing new arrivals of the systems and norms of the UK, mentoring schemes, ‘Life in the UK’ classes and community events to promote contact and understanding between communities. Information about work, housing, access to healthcare, education and other topics that migrants might need to assist their early integration is available on the UK Border Agency website.

The Government has supported local authorities and voluntary organisations in various ways to manage migration impacts, described in a report by the Department of Communities and Local Government (2009). A Migration Impacts Fund was launched in 2009 to assist public service providers to deal with transitional pressures of immigration, intended for English language teaching and other support. The Fund ended in October 2010.

22 3.2 Statistics and Trends

This section provides a summary analysis of the current situation and recent trends revealed by the main stock and flow tables (Annex D). The analysis indicates the degree to which migrant workers from selected origin areas are employed in certain sectors of the labour market. The scope of the analysis, especially of flows, is limited by small sample size. Therefore, government administrative data are used to supplement the main analysis. In this way, links are drawn between the basic trends identified and the various government schemes designed to compensate for shortages in particular occupations. There are no statistics on rates of return by third country nationals because exit checks are not conducted in the UK.

There are also no statistics on the numbers of irregular workers. Woodbridge (2005) estimated that in 2001 the number of people living illegally in the UK was in the range of 310,000 to 570,000. The most recent estimate of the total irregular population is contained in a report by researchers at the London School of Economics. They estimated that the number living illegally was in the range of 417,000 to 863,000 (Gordon et al., 2009).

3.2.1 Statistics on the labour market and migration

3.2.1.1 The stock of workers by skill levels, 2004 to 2009 (Table D1) This section presents an analysis of change in UK stocks of workers by the main skills categories between 2004 and 2009 (see Table D1). The analysis should be seen in the context of a steady increase in the total foreign population of the UK between 2004 and 2009, from 2.857 million to 4.348 million.

Because of sampling error, cell sizes of less then 10,000 are unreliable and are not included in the following analysis. Data refer to nationality (not country of birth) unless otherwise specified. Workers are categorised by the jobs they are doing, not by any skills or qualifications they may possess. Some highly qualified workers may be in low-skilled jobs.

Between 2004 and 2009 some striking changes took place in the UK labour force and the role of foreign workers in different categories of employment.

●● The total workforce grew by 443,000, two per cent, reaching 28.9 million.

●● The proportion of the workforce of UK origin fell from 95 per cent to 92 per cent, whilst the number of foreign nationals in the workforce rose by almost 50 per cent, from 1.526 to 2.285 million.

●● The growth in foreign nationals in the workforce was dominated by EU10 nationals, whose numbers expanded from 85,000 to over 500,000 (including 369,000 Poles). In addition, EU2 numbers rose from 12,000 to 61,000, mainly after 2007. Together these accounted for over 24 per cent of the total foreign workforce in 2009 compared with under six per cent in 2004.

23 ●● Nevertheless, just over half of foreign workers in 2009 were third country nationals, and their numbers also expanded by over 30 per cent in the period. A more detailed analysis of their origins is presented in the next section.

●● Together, the EU10, EU2 and third country groups, which had accounted for 66 per cent of foreign workers in 2004, had risen to a 76 per cent share by 2009. Among the remaining one quarter who were EU15 nationals, the Irish were the leading national group.

These changes affected the skills composition of the workforce in various ways.

Highly skilled

●● On aggregate, there was a shift to the highly skilled, whose numbers grew by nine per cent, from 11.63 to 12.65 million between 2004 and 2009, while those in skilled and low-skilled categories fell, from 16.79 to 16.25 million. As a result, the proportion of highly skilled in the UK workforce rose from 41 to 44 per cent.

●● Among the extra one million highly skilled workers, 787,000 were UK nationals, an increase of seven per cent.

●● The contribution of foreign nationals was numerically greatest from third countries whose numbers in highly skilled jobs expanded by almost 131,000, 30 per cent.

●● The highest increase, however, was among EU10 nationals, whose highly skilled numbers more than trebled, to over 54,000. The EU15 accounted for a more modest expansion of 16 per cent, an extra 43,000.

Skilled

●● Skilled occupations showed a fall in stocks of 467,000, from 48 per cent to 45 per cent of the total workforce.

●● The shift was dominated by a balance between losses of 812,000 (6%) among UK nationals and gains of 206,000 from the EU10 (a more than fivefold increase, from 45,000 in 2004), 28,000 from the EU2, and 117,000 from third countries.

●● The third country and EU10 stocks both reached a peak in 2008 and fell back in 2009.

Low skilled

●● The decline in low-skilled workers was 81,000, (3%), and the proportion in the UK workforce remained broadly the same at 11 to 12 per cent.

24 ●● This apparent stability again disguised a significant shift from UK nationals, whose numbers fell by over 290,000 (9%) to EU10 and EU2 sources. Together, these increased from 30,000 to almost 200,000 in the five years.

●● The third country contribution also grew, but by a more modest 27 per cent to almost 39,000. This may reflect a proportion of third country nationals employed in lower skilled jobs than their qualifications might allow as well as dependants of some of the more skilled.

3.2.1.2 The stock of workers by country of nationality, gender, and skill level, 2004 to 2009 (Table D3) Table D3 offers a breakdown of third country nationals in the UK workforce according to their nationality and skill levels. In line with total employment, the increase in foreign workers generally reached a peak in 2008, with a downturn in 2009, except in the numbers of EU2 nationals.

In the following analysis, skill levels are derived from the LFS (see section 1.2.3 or Annex C for further details). They relate to the job being done by the individual rather than their qualifications. The skill levels described here do not directly correspond to those used in the UK immigration system. For example, ‘skilled workers’ does not necessarily refer to PBS Tier 2 workers.

The main third country sources Among third countries, seven were consistently in the top ten as sources of workers between 2004 and 2009: India, Australia, South Africa, United States, Philippines, Zimbabwe and Pakistan. The skill contributions of these national stocks between 2004 and 2009 are outlined in the following section. The other source countries appearing in the top ten were: Columbia (2004), Iran (2004), New Zealand (2004 to 2007, 2009), Ghana (2005 to 2008), Nigeria (2005 to 2009) and Hong Kong and China (2008 to 2009).

India The workforce of Indian nationality was consistently the largest immigrant group, and the only one to achieve six figure numbers, rising from 77,000 in 2004 to a peak of 171,000 in 2008, falling back to 157,000 in 2009. The proportion of highly skilled Indian workers rose from 49 to 60 per cent. Skilled numbers also rose by 22,000, although fluctuated from year to year. The number of low- skilled Indian workers in 2009 remained low, at under 13,000.

Australia The Australian workforce, the second largest group for most of the period, reached 77,000 in 2007 although then declined to 61,000 by 2009. The highly skilled grew from 67 to 74 per cent of the total by 2009. The numbers of skilled workers fluctuated around 15,000, with a high in 2008, and relatively few Australians were in low skill occupations.

25 South Africa South Africans vied with Australians to be the second largest group, rising overall from 56,000 in 2004 to 81,000 in 2009. The proportion of highly skilled was generally lower, but rose from 52 per cent in 2004 to 60 per cent in 2009, an increase of 19,000. Skilled numbers meanwhile increased by 22 per cent to 25,000.

The United States US nationals in the UK workforce rose from 56,000 in 2004 to 82,000 in 2006, then fluctuated and fell sharply to 66,000 by 2009. The share of highly skilled was generally high, at around 70 per cent, but rose to as high as 80 per cent in 2006. Skilled numbers varied, but rose overall by under 3,000, to 16,000.

Philippines The numbers of Filipinos rose steadily, from 31,000 in 2004 to 58,000 in 2009. This was accompanied by a fall in the share of highly skilled, from 51 per cent to 34 per cent, largely replaced by skilled workers who expanded from 34 per cent to 52 per cent, growing by 20,000. Low-skilled numbers remained at below 10,000.

Zimbabwe Zimbabwean numbers grew from 28,000 in 2004 to 44,000 in 2006, levelling out at 40,000 by 2009. The proportion of highly skilled was consistently around 40 per cent. Skilled numbers rose by 9,000, to 22,000, while the numbers of low skilled remained small.

Pakistan The Pakistani workforce rose rapidly from 26,000 in 2004 to reach 67,000 in 2008, then fell back to 63,000 in 2009. The share of highly skilled was comparatively low, at around 30 per cent. Skilled numbers, however, increased continuously, by 16,000, to 28,000, in the period, and especially in 2006–07, making up over half the Pakistani stock in some years. The numbers of low-skilled workers rose over the period to almost 17,000 in 2009.

Overall gender trends The proportion of males in the total workforce remained close to 54 per cent throughout the period, with a very slight increase in the female proportion towards 2009. The share of males was higher among third country migrants and, in most years, in EU10 migrants, though never more than 60 per cent from any country. The balance varied considerably, however, between individual third countries. For example, among those from the largest third country, India, about 60 per cent were males in 2004, but this rose to nearly two-thirds after 2007. The Australian, South African and US stocks tended to have a more balanced gender division with females in the majority in some years. In the Pakistani stock, the proportion of women was notably small, while among Filipino and Zimbabwean workers, more than half were female.

26 3.2.1.3 The occupation of workers, by nationality 2004 to 2009 (Table D5) Table D5 provides data on stocks of workers in a range of occupations by nationality. They are listed according to size of stock, with teaching personnel the largest group.

Overview

●● Overall, the numbers of UK nationals in the range of selected occupations grew by only five per cent (213,000) to 4.6 million. The most rapid growth was in numbers of medical doctors (29% increase), personal care workers (17%) and architects/engineers (10%), while labourers and catering staff numbers fell (by 4%).

●● Third countries contributed the largest group of foreign workers to the stock of the selected occupations, amounting to 322,000 in 2009. Their numbers had expanded by 91,000, almost 40 per cent, since 2004, and they were strongly represented among medical doctors (mainly male), nurses (female), personal care workers (female) and cooks (male).

●● As might be expected from Tables D1 and D3, EU10 nationals showed the fastest rates of growth in the selected occupations, from under 22,000 to more than 73,000 during the period. The largest numbers went into catering, as cooks (mainly male) and waiters (mainly female), childcare and personal care (mainly female), and labouring (mainly male).

●● Workers from the EU2 countries have increased substantially to nearly 15,000, the largest group being labourers, with child and personal care workers and medical doctors also comparatively well represented. The main increase occurred after 2007 following accession to the EU.

●● The numbers from EU15 countries showed little change. Compared with the UK occupational profile in 2009, they were well represented in catering (mainly males), childcare (mainly female) and nursing (mainly female).

Trends in the specified occupations Trends in the specified occupational groups for which data are available, in descending size order, include the following.

Teaching personnel There was a stock of teaching personnel of 1.344 million in 2009, an increase of 62,000 (5%) since 2004. UK teachers made up around 95 per cent of the total, contributing 56,000 to growth. In 2009 these were augmented by 39,000 teaching personnel from third countries and 34,000 from the EU15. By 2009, fewer than 1,000 came from EU10 countries. Females comprised about two-thirds of the total stock throughout, but only 40 to 50 per cent of third country nationals during the period.

27 Personal care and related workers The number in personal care and related work in 2009 was 728,000 and had grown by 145,000 (25%) since 2004. While UK workers grew by 91,000, their share of the total fell from 94 to 87 per cent. This reflected a rise in third country stock by 42,000 (over 150%, 30,000 females) between 2004 and 2009. EU10 numbers also rose rapidly to 11,000 in 2008, but fell back in 2009. EU15 numbers also showed some increase, to over 12,000, but again fell in 2009. Females made up between 85 and 88 per cent of the total workforce throughout, although the proportion among third country nationals was lower, at between 67 and 80 per cent.

Nursing and midwifery professionals Total employment in nursing and midwifery was 775,000 in 2009, and had grown by 17,000 (2%) since 2004. The UK stock remained around 87 to 89 per cent of the total, while third country nationals made up around ten per cent. EU15 numbers fell after 2007 to fewer than 15,000, while EU10 numbers appeared to rise sharply from 2005, although remained at fewer than 10,000. Almost 90 per cent of nursing and midwifery workers were female, reflecting their predominance in the UK and EU15 stock. Males constituted 20 to 25 per cent of the third country workforce.

Architects, engineers and related professionals The total stock of architects and engineers in 2009 was 656,000, 73,000 (13%) higher than in 2004, though lower than 2008. UK nationals comprised 93 to 95 per cent of the total and grew by 55,000. The third country numbers rose sharply, by 11,000, to over 27,000, mainly before 2007. EU15 numbers fluctuated but grew by only 4,000 to almost 16,000. 90 per cent or more of the workforce was male throughout the period, but female numbers increased by 61 per cent, including among third country and EU15 workers.

Waiters, waitresses and bartenders There were 422,000 working as waiters, waitresses and bartenders in 2009, an increase of 12,000 (3%) since 2004. The numbers of UK nationals fell by 4,000 with their share falling from 91 to 85 per cent. To replace these, the steepest increase was in EU10 numbers to over 16,000. The third country stock fluctuated for most of the period, reaching a peak of 30,000 in 2008.The EU15 stock also varied around 13,000 through the period, peaking at 16,000 in 2008. About two-thirds of the workers were females, although they made up less than half of the third country and EU15 numbers.

Skilled agricultural and fishery workers Agricultural and fishery workers increased by 25,000, reaching 339,000 in 2009. UK nationals made up around 98 per cent of the total, and contributed 21,000 of this growth. Third country and EU15 numbers seemed to fluctuate through the period, but remained at fewer than 10,000. Most were males, although the female proportion rose from 10 per cent to 14 per cent in the latter part of the period.

28 Cooks There were 259,000 cooks in the UK in 2009, with only a small increase of 5,000 since 2004. The UK stock fell by 14,000, however, and its share fell from 85 to 78 per cent. EU15 numbers also decreased to under 10,000. In contrast, the number of third country nationals increased by 13,000, to 36,000, and they were the largest foreign group. The share of female cooks fell from 44 to 38 per cent. Although they remained nearly half of UK stocks, they fell from 17 to 9 per cent of third country nationals.

Labourers in mining, construction, manufacturing and transport The total stock of labourers in 2009, over 96 per cent male, was 233,000, falling by 25,000 since 2004. A decline of 39,000 in UK numbers was partially balanced by growth in the EU10, and especially the EU2 stocks, which together became the largest foreign group, with almost 16,000 in 2009, although fewer than a year before. Third country numbers remained at under 10,000.

Medical doctors The stock of medical doctors in 2009 was 226,000, increasing by 55,000 (32 per cent) since 2004. UK nationals contributed around 80 per cent of the total and 40,000 to this growth. Third country nationals accounted for most of the remaining increase, rising by 11,000 to almost 36,000. The number of female doctors grew by 46 per cent. By 2009, nearly half of UK national doctors were female, but males constituted around two-thirds of third country doctors.

Child-care workers The numbers of child-care workers fell between 2004 and 2009 by almost 3,000 to 131,000. UK nationals comprised around 85 per cent of the total. Stocks of EU15, EU10 and third country nationals all fluctuated through the period, with none exceeding 10,000.The high proportion of females declined slightly to 95 per cent, largely reflecting a trend in stocks.

Housekeepers and related workers The total stock of housekeepers and related workers, around 90 per cent female, increased by 7,000 after 2004, to 58,000 in 2009. The UK national stock increased by 4,000, but fell from 88 per cent of the total to around 82 per cent. The non-UK national groups each comprised fewer than 10,000.

3.2.1.4 Inflows and outflows of workers by main category of employment and nationality, 2004 to 2009 (Table D2) Stock data show the accumulated results of long-term change, including recent net migration, on the structure of the working population at particular dates (see sections 3.2.1.1; 3.2.1.3). The flow data presented here, based on the IPS, highlight the recent marginal developments in labour migration patterns. Its analysis should be seen in the context of total population flows in and out of the UK. Between 2004 and 2009 the estimated annual Long Term International Migration17 inflow varied

17 For more details on the Long Term International Migration statistics see http://www.statistics.gov.uk/statbase/Product.asp?vlnk=507 29 from 567,000 to 596,000, with an outflow between 361,000 to 427,000.There was thus a net annual inflow in the range 163,000 to 245,000. The population flows recorded by the IPS, the main source of data employed below, were smaller than these figures although the differences were not great.

Worker migration into and out of the UK: occupational skills and nationality Table D2 provides an overview of worker inflows and outflows during the period, according to the main occupational skill categories.18

Inflows of UK nationals to work grew from 25,000 in 2006 to 41,000 in 2009. Between 60 and 80 per cent were highly skilled, with their numbers varying from 16,000 in 2006 to 31,900 in 2008. The numbers of third-country workers fell significantly, but they remained overwhelmingly highly skilled or skilled. After peaking at 69,000 in 2004, they had declined to 36,000 by 2009.

Outflows of the highly skilled showed similar patterns to inflows.About two thirds of both the UK and third country nationals leaving were highly skilled. The former peaked in 2008, at 55,000, while the latter were highest in 2005 and 2009, at 33,000. Skilled UK national workers left at a rate of around 20,000 per year, while the skilled third country outflow varied around 12,000, rising to 17,000 in 2006, amounting to between a quarter and one third of their total.

In terms of national groupings, as anticipated by the stock data evidence, the greatest inflows came from third countries in 2004 (103,000), although inflows from third countries halved to only 50,000 by 2009. The next largest inflow source was the EU10 in 2007 (63,000), which again had fallen to 34,000 in 2009.

The out-movement of workers formed a steadier flow from year to year, with third country outflows rising from 42,000 in 2004 to 51,000 in 2006, and again in 2009. The out-movement of UK nationals also rose from 57,000 in 2004 to a peak of 84,000 in 2008. Most other outflow figures are unreliable, although EU15 numbers appear to have grown from 11,000 to 28,000 between 2007 and 2009.

3.2.1.5 Inflows and outflows of workers by country of nationality, main skills categories and gender, 2004 to 2009 (Table D4) Table D2 shows the movement into and out of the UK of UK, EU15, EU10, EU2 and aggregate third country nationals. Table D4 offers more detail of trends within the last group of third country sources, although reliable data are available only for the two major sources of such labour over the period: India and Australia.

Total Indian inflows to the UK peaked at 18,000 in 2006 and declined thereafter, to 11,000 in 2009. Around 75 per cent of these were highly skilled, a significantly higher proportion than in the resident Indian workforce, as the stock tables show. As in this total workforce, there was a substantial male 18 The disaggregated data in the table include substantial standard errors. The authors’ discussion therefore refers only to those cells where the standard error is 20 per cent or less, in line with ONS practice. These restrict comment to the highly skilled and, to a more limited extent, the skilled categories, and only for UK and third country nationals. 30 bias. Total Australian figures are only reliable for 2005, 2006, and 2008 when, respectively, 10,000, 14,000 and 6,000 entered the UK. In 2006 and 2008, when occupational data are comparatively reliable, about two-thirds were highly skilled, around the same as in the resident stock.

Evidence for those leaving the country shows that Australians make up the largest number of 19third country nationals leaving the UK, at between 12,000 and 15,000 per year. Most (between 50 and 70 per cent) were highly skilled. The only other statistically reliable evidence shows that 7,000 Indian nationals left the UK in 2008 and 10,000 in 2009, over 80 per cent highly skilled, and again predominantly male.

3.2.1.6 Inflows and outflows by usual occupation and nationality (Table D6) To complete the presentation of available data, Table D6 shows separate ONS estimates of the flows of gainfully employed UK and non-UK workers for 2004 to 2009.This uses an alternative measurement of labour flows, based on those in the IPS who are in the labour market before and after moving. Because of small sample size, only two classifications of migrant workers are used by ONS: professional and managerial; and manual and clerical. The former includes administrators, managers and people with professional and technological qualifications; the latter includes all other workers.

For the UK in 2009, 52 per cent (59% in 2004) of the inflow and 71 per cent (60% in 2004) of the outflow were people who had been in employment prior to entry or leaving (Table D6). This implies that more of the inflow was coming into the UK to work, not having worked before entering, than was the case with the outflow. It is consistent with the pattern of young people coming for training or to learn English, gain employment and eventually leave.

In 2009, 212,000 non-UK and 60,000 UK nationals entered the UK, having been in employment elsewhere before moving. The equivalent figures for 2004 were 254,000 non-UK and 52,000 UK nationals. In 2009, the number of non-UK nationals entering the UK having been in employment elsewhere before moving was 48,000 lower than the year before but the number of UK workers entering the UK increased by 7,000. Overall, the evidence suggests that, while the recent economic downturn had not impacted greatly on labour immigration into the UK in 2008, by 2009 it had reduced non-UK national labour immigration considerably. However, the trends may also have been affected by the introduction of the PBS.

Professional and managerial workers have traditionally accounted for the majority of employed migrants moving to and from the UK. The number of non-UK immigrants in this group fluctuated around 130,000 to 140,000 after 2004, rising to 147,000 in 2008 and falling back to 115,000 in 2009. In contrast, the numbers of UK national professionals and managers entering or re-entering the country rose only from 36,000 in 2004 to 40,000 in 2008 before returning to 36,000 in 2009. Thus, in the first year of the economic downturn (2008–09), the number of skilled migrant workers entering the UK fell, especially the non-UK nationals. The numbers of UK national professionals and managers leaving

19 The figures for all other countries have a standard error of 20 per cent or more and are not reliable. 31 in 2009 however, at 54,000, was well down on 74,000 in 2004. Meanwhile the equivalent non-UK national numbers leaving rose steadily from 40,000 to 74,000. On balance, in 2009 the UK lost 18,000 skilled UK workers and gained 41,000 non-UK workers, so that the foreign inflow still more than made up the domestic loss, supporting an aggregate gain of 23,000 (compared with 61,000 in 2004).

There were also marked differences according to origin and destination, which generally confirm the impression gained from the analysis of stock data. Traditionally, the richer countries (Europe, Old Commonwealth and Other Developed Regions)20 have tended to act as ‘turnover regions’. Professional and managerial workers who come to the UK from these countries are more likely to return there and those from elsewhere are more likely to stay. In 2009 the more developed countries accounted for 54 per cent of the inflow of non-UK national professional and managerial workers, but 80 per cent of the outflow. They were thus responsible for only seven per cent of the net gain, leaving the lesser developed regions (Indian Sub-continent, ‘Rest of World’) to account for the majority. The net balance of professional and managerial migration was thus a gain of 6,000 from more developed regions and 35,000 from the less developed ones.

The situation for manual and clerical workers is less clear. Since 2004 there has been no particular inflow trend by UK workers and, while outflow has tended to rise, this is not as marked as among professional and managerial workers. Like their professional and managerial counterparts, the numbers of non-UK national manual and clerical immigrants have risen steadily, while the outflow has fluctuated at a relatively low level. By 2009, 35,000 departing UK national manual and clerical workers were more than compensated for by the arrival of 72,000 foreign nationals (although well down on 121,000 in 2007). This translates into a net loss of 15,000 UK manual and clerical workers, a net gain of 25,000 non-UK nationals and an overall net gain of 10,000. In consequence, the less developed regions (including EU10 and EU2 countries, as the stock data suggest) supplied most of the increase in manual and clerical immigrant workers

In sum, the UK has seen rising inflows of non-UK workers, which more than made up for the rising outflows of the home population. However, the balance between skill levels has fluctuated. In 2004 professional and managerial workers accounted for 53 per cent of the net gain of non-UK workers; by 2006 this proportion had risen to 60 per cent and to 62 per cent in 2009. Hence, labour immigration has been adding to the quality of the UK workforce.

3.2.2 Analysis of trends and relevant developments

The trends discussed in the previous section have illustrated some significant shifts in the UK labour force and the role of immigrants in bringing these about. Underlying the changing flow patterns for third country workers has been government action to address specific labour shortages and to increase the supply of high level talent in the UK economy. To complement the trends revealed, 20 Broadly speaking, Old Commonwealth refers to Australia, Canada, New Zealand and South Africa. Other developed regions are the Organisation for Economic Co-operation and Development (OECD) member states excluding European. 32 administrative data from various past and current schemes are presented here to indicate the scale and nature of official attempts to regulate UK labour immigration.This is followed by a discussion of the nature of the labour shortages identified within the UK labour market.

3.2.2.1 Government schemes to manage migration For much of the period under consideration, the UK economy prospered and attracted immigrant workers, many of them through designated schemes. This section reviews the scale of these schemes in attracting and managing the flow of third country workers as well as those from new member states. It reviews first those schemes described in Section 2 designed to ease shortages in skilled occupations then turns to those for less skilled occupations.

Work permits The work permit data for 2004 to 2008 includes approvals of applications already submitted when Tier 2 began. Figure 3.1 shows how the four main elements of the system fluctuated after 2004. Work permits (for third country workers currently living outside the UK) and first permissions (for third country workers currently living in the UK) were requested by employers for foreign workers newly entering the UK labour market, as distinct from those already working in the UK for whom extensions or changes of employment might be sought. The numbers of work permits and first permissions approved peaked at 96,740 in 2006 before falling to 77,660 in 2008 as the effects of economic downturn on companies began to be felt.

Figure 3.1 Work permit applications approved by type, 2004 to 2008 150 Other 120 Changes of Employment 90 Extensions

First Permissions

Thousands 60

Work Permits 30

0 2004 2005 2006 2007 2008 Source: UK Border Agency.

Work permits and first permissions were concentrated in certain occupational groups. For example, in 2008 55 per cent were in professional occupations (mostly in science and technology). Around one in six were managers and senior officials. Only small numbers were in skilled trades, manufacturing and semi- and unskilled occupations. Comparison with 2004 shows some notable recent shifts, especially a decline in numbers of health and social welfare associate professionals (mainly nurses, senior carers and therapists) from 23,762 to 4,149 (from 27% to 5% of the total). In contrast,

33 numbers of information, communications and technology professionals went up from 10,082 in 2004 to 23,356 in 2008 (i.e. from 11% to 30%).

There were also shifts in the national origins of migrants granted work permits. As earlier data suggest, the largest group was Indian, with 41 per cent of issues in 2008 (30% in 2004). Numbers from the USA, the second largest source, also rose from 11 to 13 per cent of the total. The data also confirm that in 2008 the occupational relationship between the UK and origin countries varied. For example, compared with the average, Indians were more likely to be science and technology professionals, Nigerians and Filipinos health and social welfare associate professionals, while many Japanese and Americans were managers or senior officials. Any changes in demand therefore impact more on some nationalities than others. The decline in permits in the health sector particularly affected flows from the Philippines while the growing IT sector accounts for the large rise in entrants from India.

The Points-Based System Tier 2 (November 2008-present) Statistics for Tier 2 are available from two sources. The first is visas issued from embassies, consulates and High Commissions abroad (i.e. ‘out-of-country’) and extensions or new visas issued ‘in-country’. This does not necessarily mean that every visa was used to come to the UK, but it is a useful indication of the numbers of migrants that may arrive in the UK.

The second source of data comes through the issue of CoS to employers. Data thus flow from the issuing of CoS by employers through an online sponsor management system. These data do not necessarily correspond with those of approved applications, since they do not record whether a migrant’s application has been approved. As with visas, there is no guarantee that an individual issued a certificate actually arrives in the UK and takes up work.Thus the data may exaggerate the level of labour immigration, although it is generally assumed that this effect is marginal.

Table 3.1 Granted Main Applications for Tier 2 and Work Permits 2009 Out-of-country In-country Percentage in-country Work Permit Holders 5,160 7,285 59 Tier 2 – General (RLMT and Shortage) 8,555 12,900 60 Tier 2 – Intra-company transfers 22,030 6,625 23 Tier 2 – Ministers of Religion 370 610 49 Tier 2 – Elite sports people 265 Total Tier 2 and Work Permits 36,380 27,420 43 Source: Migration Advisory Committee, 2010b Table 3.2., p 79.

The data on CoS, however, do contain information about the job into which an immigrant is recruited, including the industry of the employer and occupational group. This information is provided by employers when they assign a CoS and its accuracy is checked by the UK Border Agency during consideration of a migrant’s application. Certificate data may be used to categorise migrants according to their route of entry, relationship to shortage occupations, other occupations requiring a resident labour market test (RLMT), and ICTs.

34 Visa issues for Tier 2 for principal applicants (excluding dependants) in 2009 are summarised in Table 3.1. Of the total of 63,800, the Tier 2 General category accounted for 34 per cent, ICTs 45 per cent and work permits (a hangover from the previous system) 20 per cent. Principal applicants were accompanied by 49,670 dependants.

Almost half principal applicants (47%) were Indian, followed by Americans (11%). Proportions varied by route of entry. Two-thirds of ICTs were Indian, around one in eight Americans. Japanese were also proportionately more numerous among ICTs than in the other routes. Although still the largest group, Indians were much less numerous in the Tier 2 General. Filipinos were relatively over- represented in shortage occupations, as were Chinese; Americans were the reverse. Only a fifth of the RLMT route were Indians, followed by Americans and Chinese. A more detailed breakdown is in the Migration Advisory Committee report (2010, 84). The degree of selectivity in flows becomes apparent when industrial sector, nationality and ICTs are interlinked. This especially applies in computer services. Three-quarters of Indians coming as ICTs worked in the sector and no less than 93 per cent of all such transfers in this sector were Indians (who also comprised 81 per cent of transferees in telecommunications). More detail of the characteristics of Tier 2 migrants may also be derived from data on CoS issued to companies.

Table 3.2 Top 10 Tier 2 jobs (4-digit SOC occupation) by total Tier 2 jobs, July 2009 to June 2010 Total Tier 2 Tier 2 jobs as jobs percentage of UK full-time employment IT, software professionals (2132) 16,839 5.7 Nurses (3211) 3,689 1.1 Medical practitioners e.g. doctors and surgeons (2211) 2,434 1.7 Chefs, cooks (5434) 2,412 1.3 Managers, information and communication technology (1136) 2,020 0.8 Finance and investment analysts/advisers (3534) 1,920 1.7 Care assistants and home carers (6115) 1,844 0.5 Consultants, actuaries, economists, statisticians (2423) 1,744 1.5 Managers, marketing and sales (1132) 1,589 0.3 Researchers, scientific (2321) 1,476 11.4 Source: Migration Advisory Committee, 2010b, Table 3.11, p 92. Notes: Total Tier 2 jobs are calculated as the sum of used Certificates of Sponsorship for Tier 2 between July 2009 and June 2010, which includes the Resident Labour Market Test, shortage and intra-company transfer routes. The Tier 2 jobs as a percentage of UK full-time employment is calculated by dividing the number of total Tier 2 jobs by the level of UK occupation-specific full-time employment by 4-digit Standard Occupational Classification.

Table 3.2 shows the top ten Tier 2 jobs (by 4-digit SOC occupation) indicated by the number of CoS used as a proportion of UK sector specific employment in the year to June 2010. By far the largest number was awarded to information technology and software professionals, which accounted for 27 per cent of total CoS and 48 per cent of the ICTs. They were followed by nurses and medical practitioners, and chefs and cooks, with senior commercial managers and analysts also prominent.

35 Scientific research occupations, however, had the highest share of Tier 2 jobs in relation to their UK full-time employment, suggesting that these were most dependent on Tier 2 permission, followed by information technology and software professionals.

Highly Skilled Migrant Programme and Tier 1 The number of HSMP approvals increased rapidly from 2004 to peak at 28,090 in 2007, falling to 17,760 in 2008 as the scheme was replaced by Tier 1 (Figure 3.2). Between 2004 and 2008, nationals of 74 countries made use of the scheme but only half a dozen countries provided the majority of applicants. Indians were the largest group each year since its inception and accounted for 36 per cent of the 2008 total, followed by Pakistanis (13%). Unfortunately, there are no statistics on what occupations HSMP entrants took up, although a small sample survey found that 29 per cent of Tier 1 visa holders in this sample were employed in unskilled roles (UK Border Agency, 2010). This is consistent with another larger survey of Tier 1 applicants which found that 30 per cent of applicants were in low-skilled employment or unemployed at the time of the survey (UK Border Agency, 2011).

The numbers of main applications granted in-country and out-of-country for Tier 1 are shown in Table 3.3. Over three-quarters of grants were to those already in the country, including the great majority of PSWR. For those coming through the General migrant route, India (41%), Pakistan (13%) and Nigeria (9%) were the top national groups. There was a slightly different distribution for the PSWR route. India (31%) was again the lead country, followed by China (16%) then Pakistan (15%) and Nigeria (10%). The small number of investors granted permission was dominated by Russia (38%) followed by China (12%). The biggest source of entrepreneurs was the USA (18%), followed by India and Pakistan (12% each).

Table 3.3 Granted Main Applications for Tier 1 and HSMP 2009 Out-of-country In-country % in-country HSMP 335 31,485 69 Tier 1 – General 13,930 Tier 1 – Investors 155 235 46 Tier 1 – Entrepreneurs 120 Tier 1 – Post-Study 4,245 34,180 89 Total Tier 1 and HSMP 18,780 65,900 78 Source: Migration Advisory Committee, 2010 Table 3.2., p 79.

International Graduates Scheme In 2008, 16,171 students were approved for the scheme. Indians were the largest group with just over a quarter of the total, with Pakistan and China the other two major nationalities.

36 Figure 3.2 Highly Skilled Migrant Programme applications approved, 2004 to 2008 30

Thousands 10

0 2004 2005 2006 2007 2008 Note: Reduction in 2008 affected by introduction of PBS. Source: UK Border Agency.

Worker Registration Scheme Quarterly registration data for A8 migrants show that the number registering and approved for the scheme has generally followed a seasonal pattern, mainly reflecting the cycle of demand, with a steady decline since 2007 (Figure 3.3). The nationalities of those registering each year are shown in Table 3.4, although they underestimate the total work-related immigration from the A8 countries due to under-registration of eligible migrants (see section 2.2.2 above). The fall from late 2007 onwards reflects a combination of seasonal effects, a slowdown in the UK economy and a smaller supply from the sending countries. The total since 2004 was 1.03 million, some two-thirds of whom were Polish nationals. Between 2008 and 2009, nationality trends varied. Numbers of Latvians and Lithuanians increased sharply while those of other nationalities fell. The majority of those registering over the whole period, 56 per cent, were male.

Figure 3.3 Total approved applicants for Worker Registration Scheme, by quarter and year of application, Q2 2004 to Q4 2009 70

30 Thousands 20

0 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 2004 2005 2006 2007 2008 2009 Source: UK Border Agency. 37 Table 3.4 Worker Registration Scheme applications approved, 2004 to 2009 Nationality 2004 2005 2006 2007 2008 2009 Total Czech Rep 7,701 10,507 8,424 7,687 6,247 4,789 45,355 Estonia 1,742 2,525 1,549 998 865 1,232 8,911 Hungary 3,382 6,176 6,968 8,918 9,841 9,602 44,887 Latvia 8,080 12,865 9,755 6,444 6,058 16,094 59,296 Lithuania 18,110 22,789 17,463 14,578 10,633 15,887 99,460 Poland 66,047 122,313 160,112 155,432 101,436 62,510 667,850 Slovakia 12,054 21,522 21,808 22,680 18,008 9,192 105,264 Slovenia 148 166 187 188 188 154 1,031 Source: UK Border Agency.

Detailed data on the main occupations taken up by registrants for the period May 2004–March 2009 are in Table 3.5. The largest group were process operatives in factories, followed by warehouse workers. There is evidence from studies in both the UK and sending countries that many WRS workers were over-qualified in terms of skill and educational levels for the jobs taken up (see, for example, Anderson et al., 2006; Kaczmarcyk and Okolski, 2008; Sumption and Somerville, 2009).

Table 3.5 Worker Registration Scheme for top 20 occupations in which registered workers are employed, May 2004 to March 2009 Sector Number Percentagea Process operative (other factory worker) 253,130 33.4 Warehouse Operative 76,580 10.1 Packer 53,860 7.1 Kitchen and catering assistant 52,765 7.0 Cleaner, domestic staff 51,110 6.7 Farm worker/Farm hand 39,680 5.2 Waiter, waitress 32,110 4.2 Maid/Room attendant (hotel) 32,050 4.2 Sales and retail assistants 25,705 3.4 Labourer, building 24,930 3.3 Care assistants and home carers 23,655 3.1 Crop harvester 15,155 2.0 Bar staff 11,530 1.5 Not stated 11,190 1.5 Food processing operative (fruit/veg) 10,900 1.4 Food processing operative (meat) 10,645 1.4 Chef, other 10,240 1.4 Administrator, general 7,585 1.0 Fruit picker (farming) 7,440 1.0 Driver, HGV (Heavy Goods Vehicle) 7,020 0.9 Total in the top 20 occupations 757,265 100.0 Other/Not stated 153,045 a Percentages rounded to whole numbers in the text Source: UK Border Agency.

38 Comparison of the occupational distributions of those obtaining work permits with those registering under WRS shows a high level of complementarity. In 2007, 91 per cent of work permit holders were managers and senior officials or in professional or associate professional and technical positions, compared with only three per cent of WRS registrants. In contrast, elementary occupations accounted for 72 per cent of WRS registrants but only two per cent of work permit holders (Salt, 2009). Thus, while the work permit system responded to shortages of highly skilled workers, the WRS responded mainly to demand for the lower skilled.

Sectors Based Scheme Figure 3.4 shows the trend in numbers of foreign workers recruited through the SBS. From May 2004 until 2007 the scheme was for third country workers, but from 2007 it was confined to Bulgarians and Romanians. The number employed through the scheme fell during the period, from 16,864 in 2004 to 1,570 in 2008. In the early years of the scheme a large number of countries were involved, although most people were from Eastern Europe and the former Soviet Union. By 2008, almost nine in ten approvals went to Bulgarians. In the beginning the focus was on two sectors of the economy, food processing and hospitality, each with a quota of 10,000. From May 2004, these quotas were reduced by 25 per cent as a result of the accession of new EU member states. In 2005 the hospitality sector was withdrawn from the scheme because of the influx of A8 nationals. After this, SBS workers were allowed only within the food manufacturing industry, specifically in fish processing, meat processing or mushroom processing. From 2009 the scheme was restricted to Bulgarians and Romanians.

Figure 3.4 All Sectors Based Scheme work permits approved by industry, 2004 to 2008 20

10 Thousands

0 2004 2005 2006 2007 2008 Source: UK Border Agency.

Seasonal Agricultural Workers Scheme Figure 3.5 shows the trend in numbers of foreign workers recruited through SAWS. The total number of approved applications peaked at 16,444 in 2004, the final year thatA8 nationals were included in the scheme. After the accession of the A8 countries, the number of SAWS applications granted fell to 9,746 in 2005 and remained at around this level for the following three years. Before 2007, Ukraine provided substantial numbers of applicants as did Russia, Belarus, and Moldova. Bulgarians

39 and Romanian workers only made up 17 per cent of total flows. From 2008 the Scheme has only been open to Bulgarian and Romanian workers and in 2009 12,025 applications were successful. The gender composition of the successful SAWS applications, however, has been fairly constant, with 60 to 65 per cent of the applicants being male.

Figure 3.5 Seasonal Agricultural Workers Scheme applications approved 2004 to 2009 20

10 Thousands

0 2004 2005 2006 2007 2008 2009 Source: UK Border Agency.

Working Holidaymaker Scheme Figure 3.6 shows the trends in numbers of people admitted into the UK under the WHS. The number admitted to the UK under the scheme peaked in 2004 at 62,390 and declined every year thereafter; in 2008, there were 32,725 admissions. While little is known about the characteristics of working holidaymakers, it is reasonable to assume that they were generally well educated and adaptable. There is also no regional breakdown in the statistics for working holidaymakers. The lack of information about the working holidaymakers makes it unclear why the numbers fluctuated in recent years, although administrative changes may have been responsible.

Figure 3.6 Working Holidaymakers admitted to the UK, 2004 to 2008 70

30 Thousands 20

0 2004 2005 2006 2007 2008 Source: UK Border Agency. 40 The countries sending the largest number were Australia, South Africa, New Zealand, Canada, and India. With the exception of India, these countries also experienced the largest numerical declines over the period. The biggest decline was from South Africa (down from 21,125 to 6,765), followed by Australia (20,265 to 13,500), New Zealand (5,325 to 3,810), Canada (4,120 to 2,815), and Namibia (1,290 to 70).

Youth Mobility Scheme In November 2008 the YMS replaced the WHMS. Its introduction has substantially altered the composition of migrants using this route. As of November 2010 only Australia, Canada, Japan, New Zealand, and Monaco had bilateral agreements with the UK.

In 2009, Australian nationals (7,940) were the largest group to enter the UK under the YMS, followed by New Zealand nationals (2,095), Canadian nationals (2,055) and Japanese nationals (760). There were no admissions of nationals from Monaco under the route in 2009.

3.2.2.2 Impact of intra-EU mobility Some indication of the overall impact of intra-EU mobility may be gleaned from data on the numbers of foreign nationals entering the UK and allocated a national insurance number (NINo). Most allocations are for work purposes. Even so, it cannot be assumed that all of those allocated a number for work purposes actually take up a job and many will work part-time or casually.

There was a fall from the peak of 733,100 in 2007/08 to 572,740 in 2009/10 (Table 3.6). This may be an effect of the economic downturn. What is particularly striking at this time is the changing balance between those from other EU countries and from third countries. Between 2007 and 2010, inflows from the EU (27) fell sharply, while those from third countries rose slightly. In consequence, the proportion of the latter rose from 40 to 52 per cent of the total.

Table 3.6 Overseas nationals entering the UK and allocated a National Insurance Number 2004/05 2005/06 2006/07 2007/08 2008/09 2009/10 EU - 27 197,600 374,500 420,300 439,900 377,100 274,100 Third countries 237,800 288,500 285,600 293,200 309,000 298,600 Total 435,400 663,100 705,800 733,100 686,100 572,700 Note: EU- 27 nationals include all existing member states of the EU at the time of writing except UK nationals (country of interest). Years are fiscal years. Source: Department for Work and Pensions, National Insurance Number registrations.

3.2.2.3 Shortage occupations and the economic downturn In autumn 2008, the MAC produced its first complete shortage list forTier 2 of the PBS. The occupations on the list were estimated to account for approximately 700,000 employees in the UK compared with over one million in the pre-PBS list supporting the work permit system. It included a range of specialist roles in engineering, quantity surveyors, ship and hovercraft officers, medical consultants/veterinary surgeons and some associated specialists in medical practice, senior and specialist registered nurses, senior care workers, secondary teachers of mathematics and science, internationally recognised dancers, skilled chefs and cooks and skilled sheep shearers.

41 The first partial review in April 2009 (Migration Advisory Committee, 2009a) added skilled orchestral musicians and some experts in visual effects and computer animation, as well as skilled contemporary dancers. The list was also amended, however, to include only those social workers working with children and in family services, and a revised range of medical and health related occupations. Changes in skills criteria were also proposed for care assistants and home workers, chefs and sheep shearers. The only specific response to the recent economic downturn, related to the construction industry, appeared to be the suspension of quantity surveyors and property/construction managers from the list. Overall, these revisions reduced employment in the list categories to around 530,000.

The second review, published in October 2009 (Migration Advisory Committee, 2009b), made some additions and removals in the list of engineering and healthcare occupations, adding special needs teachers and skilled meat boners and trimmers, and removing ship and hovercraft officers. It was estimated that these changes further reduced employment in the shortage occupations list to fewer than 500,000, less than half the pre-PBS level. The review also noted that most of the top ten occupations using the shortage occupation route in the first nine months of theTier 2 scheme had been concerned with healthcare and related activities. Nursing occupations made the greatest use, followed by chefs and cooks, care assistants and home carers, and secondary teachers. These patterns of use might guide future consideration by the MAC of occupations for review. The shortage route nevertheless still accounted for a small proportion – around nine per cent – of Tier 2 permissions,

The third partial review, in March 2010 (Migration Advisory Committee, 2010a), focussed on musicians, fishermen, metal working fitters and, at the Government’s request, pharmacists and engineering technicians. The number of pharmacists/pharmacologists was increased by including those outside the NHS or hospitals, but designated orchestral musicians were confined to leaders and principals only.

These reviews of job title designation on a rolling six-monthly basis during the recent economic downturn have allowed the MAC to consider changing economic and labour market conditions when recommending additions to, or removals from, the shortage occupation list. It has also enabled refinement of its methodology, especially through improvements in bottom-up evidence. The result has been a progressive reduction in the employment coverage of the list, but only two cases where the effect of the economic downturn has been specified, both in the construction industry.

Some effects of the economic downturn may be seen in the fall in job vacancies. The latest survey from the National Employer Skills Survey for England suggests that between 2007 and 2009 the number of employment establishments with hard-to-fill vacancies fell from 183,000 to 85,000, those with skill shortage vacancies from 130,000 to 63,000, with total vacancies as a proportion of employment in the firms down from three to two per cent (Shuryet al., 2010).21

21 The survey is representative of all employers, at unit level, across England with a headcount of at least two. 42 At a more general level, the effects of economic downturn could also be seen in the changing numbers of people in employment, which have varied by sector. Figure 3.7 shows seasonally adjusted estimates of UK employment change for the major sectors in the two years before and after the onset of the economic downturn in 2008. Although initiated by the financial services, the main impact of the ensuing loss of investment and market confidence was to accentuate the established pattern of decline in UK manufacturing employment, which more than doubled to over 350,000 jobs lost between June 2008 and 2010. Marked reversals were also experienced in distribution activities (380,000) and construction (200,000). Growth nevertheless continued up to June 2010 in the mainly public sector healthcare, social work and education activities.

Figure 3.7 UK employment change (seasonally adjusted) June 2006 to 2008, 2008 to 2010 Thousands -400 -300 -200 -100 0 100 200 A: Agriculture, etc. B: Mining C: Manufacturing D: Energy utilities E: Water supply; etc. F: Construction G: Wholesale/retail H: Transportation I: Accommodation and food J: Communications K: Financial L: Real estate M: Professional, scientific N: Administrative activities O: Public admin and defence P: Education Q: Health and social work R: Arts, entertainment S: Other service activities

June 2006-08 June 2008-10 Source: Workforce Jobs (WFJ) seasonally adjusted quarterly estimates issued by the ONS via NOMIS.

43 4. CO-OPERATION WITH THIRD COUNTRIES FOR ECONOMIC MIGRATION

The main developments are briefly described below. Fuller details are in the UK EMN Temporary and Circular Migration report (Wiese and Thorpe, 2011).

4.1 Co-operation with particular third countries

4.1.1 Health Worker Recruitment Agreements

In 2004, the UK Government instituted a Code of Practice for the Active Recruitment of Healthcare Professionals, which is to be followed by the National Health Service (NHS). The Code applies specifically to the recruitment of professional health-care workers, limiting recruitment by the NHS unless approved by the source country. While the code has improved the recruitment practices of overseas health-care professionals, it is not comprehensive – it is voluntary for the private sector and does not prohibit direct recruitment of health-care professionals.

The Department of Health worked with the Department for International Development (DfID) to produce a definitive list of developing countries from which the UK health sector should not actively recruit. The basis of this list is the list of aid recipients produced by the Organisation for Economic Co-operation and Development’s (OECD) Development and Assistance Committee. The list is derived from the economic status of the countries and their relative position with regards to numbers of health personnel. Countries come on and off the list depending on DfID in-country expertise, independent review from partners such as the World Health Organisation, and requests from individual countries. Many of the basic provisions are included in the Commonwealth Code of Practice for international recruitment of health workers that governs recruitment from other Commonwealth countries. At the time of writing, 152 countries are on the list. China has asked to be removed from the list but requested that no recruitment should take place in small rural areas. Agencies may recruit in India except for four states: Andrah Pradesh, Madhya Pradesh, Orissa and West Bengal. The UK and Philippine Governments have signed a Memorandum of Understanding to enable the UK to recruit nurses and some health care professionals.

4.1.2 Migration for Development in Africa Programme

This is a global capacity-building approach placing African expatriate professionals in public and private institutions to work in key development sectors in countries of origin. Since its introduction it has shifted away from focussing on permanent return towards encouraging qualified professionals to return to countries of origin on a short-term, circular or virtual basis, with the aim of attracting some of the highly qualified expatriates for whom a prolonged or permanent return was not a practical option.

44 4.2 Combating brain drain

There are two main initiatives.

4.2.1 Medical Training Initiative (MTI) The UK acknowledges that migration can represent a way for individuals outside the OECD to acquire capital and skills that would otherwise be impossible to attain, and which under the right circumstances improve the lives of family members in the home country as well as their own lives upon return. In 2008 the MTI route became part of the PBS Tier 5 Government Authorised Exchange Category, which allows doctors from third countries to train in the UK for a fixed period of time. Rather than being centrally regulated these types of movements are managed through partnerships between the UK’s medical Royal Colleges and their counterparts in countries of origin. It is a strictly temporary route that seeks to promote circular migration so that participants in a particular scheme can return to their home country and apply the skills and knowledge developed during their time in the UK.

4.2.2 Diaspora Involvement This is seen as another way to promote development in countries of origin, and the temporary migration of members of the diaspora to their home country is supported by UK policy. In 2008, DfID assigned £3 billion over three years to its Voluntary Services Overseas programme, which helps diaspora organisations plan their own volunteering programmes in an attempt to increase awareness of and support for global poverty reduction for the volunteer and the communities to which they belong. Furthermore, DfID remains supportive of the diaspora initiatives managed by the UK Office of the International Organization for Migration (IOM) which, together with its counterparts in countries of origin, are implementing a variety of small-scale projects.

4.3 Stockholm Programme22

In line with the recommendations made in the Stockholm Programme, most initiatives are trying to link migration and development in the UK focus on facilitating the flow of migrant remittances23 and preventing brain drain in countries outside the OECD.

4.4. Youth Mobility Scheme

The youth mobility scheme is for young people from participating countries who would like to come and experience life, including working, in the UK. The scheme encourages co-operation with third countries as it allows young people from participating countries to come to the UK for tourism and work. See section 2.2.3 for more details. 22 The Stockholm Programme sets out the European Union’s priorities for the area of justice, freedom and security for the period 2010-14. See http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:C:2010:115:0001:0038:EN:PDF 23 There is no official definition of remittances. According to the International Monetary Fund (IMF), which provides the most widely used standard for the presentation of international statistics, remittances are international transfers of funds sent by migrant workers from the country where they are working to people (typically family members) in the country from which they came. 45 5. ANALYSIS AND CONCLUSIONS

Assessment of the effectiveness of past labour immigration policies, directed largely to non-EU third countries, is made difficult by the changing conditions created by the inflow of workers from EU10 and EU2 countries since May 2004. Many of these filled previously hidden supply deficiencies in low-skilled occupations. As the evidence of the stocks and flows demonstrates, however, they have also made valuable contributions to UK resources of higher skilled workers. Skilled immigration from outside the EU continues to be regulated, with conditions of entry being tightened further in 2011. The impacts of the economic downturn, however, may make it difficult to assess the independent effects of these measures.

The economic downturn has also had limited effect so far on the identification of shortage occupations by the MAC during its brief operating period. While total employment in the designated Tier 2 occupations has been halved compared with the former work permit regime, this appears to reflect refinement of methodology rather than the effects of the economic downturn, which has been specified only in excluding two construction industry categories.

This section addresses the links at macro- and micro-levels between policy measures and their outcomes, including the degree to which policies designed to alleviate labour shortages affect the economy. However, the fundamental shift in policy and its application brought about by the introduction of the PBS in 2008 makes it difficult to distinguish between the effects of this process change and economic forces, including the economic downturn.

5.1 Linking overall labour migration policy and outcomes

One of the main criteria for assessing the success of policy in addressing labour shortages is the overall benefit to the economy. On the one hand it might reasonably be expected that measures to reduce bottlenecks in the labour market through the managed recruitment of foreign workers would bring overall economic benefits to the country. On the other, there is a danger that labour immigration might have an adverse effect on domestic labour if not properly targeted. In essence, the Government seeks to maximise the first of these outcomes while minimising the second.The rest of this section reviews some of the key findings from the research literature on the fiscal and labour market fectsef of immigration.

5.1.1 Fiscal effects

The first attempt to assess the net fiscal contribution of first generation immigrants to the UK was made by Gott and Johnston (2002) for the period 1999 to 2000. They estimated that migrants in the UK made a net contribution to the economy of £2.5 billion. Critics argued that the estimated net fiscal gain was meaningless unless seen in the context of the overall budgetary position and that as migrants age and retire they will become net recipients (Lilley, 2005). Meanwhile, Rowthorne (2004)

46 and Coleman and Rowthorne (2004) argued that any assessment of the fiscal contribution of migrants should take account of the cost of administering the immigration programme and providing for the special needs of immigrants so that a focus on net fiscal change could be misleading.

Sriskandarajah et al. (2005) revisited the Gott and Johnston study, extending it to cover the period from 1999 to 2004, using the same basic methodology to achieve comparability but making changes to deal with some of the criticisms, including the treatment of dependants. They concluded that the contribution of immigrants to public finances was growing and was likely to continue to do so in the near future. Total revenue from immigrants grew in real terms from £33.8 billion in 1999/2000 to £41.2 billion in 2003/04, a 22 per cent increase compared with the six per cent increase for the UK-born. Sriskandarajah et al. found that migrants in the UK are positive net fiscal contributors in upturns of the economy but negative net fiscal contributors in downturns. Nevertheless, migrants are found to be greater net fiscal contributors than natives in both upturns and downturns.

A different view came from the major report by the House of Lords Select Committee on Economic Affairs (2008) on the economic impact of immigration. It concluded that the main beneficiaries of migration were immigrants and their families; immigration had a very small impact on the Gross Domestic Product (GDP) per capita; it was unlikely to create significant benefits for the resident UK population; and that while the overall fiscal impact of immigration was small, significant variations across different immigrant groups were masked. Further, an inter-departmental paper presented to the House of Lords Select Committee on Economic Affairs stressed the need for both a short- and long-term view and concluded that “in the long run, it is likely that the net fiscal contribution of an immigrant will be greater than that of a non-immigrant” (Home Office and Department ofWork and Pensions, 2007, 9).

Dustmann et al. (2009) assessed the fiscal consequences of migration to the UK from the A8 countries during the period 2004 to 2008. Their analysis showed that in each fiscal year since enlargement in 2004, A8 immigrants made a positive contribution to public finance because they had a higher labour force participation rate, paid proportionately more in indirect taxes and made much lower use of benefits and public services. In the longer term, the authors suggested that the balance might change as migrants settled in the UK and raised families, thus consuming more benefits. On the other hand, their overall better education than UK residents implied that they might pay more in taxes.

In general the net fiscal contributions of migrants differ significantly between different groups. For example, on average migrants contribute more in taxes than they receive in public spending; however, low-skill migrants may nevertheless be negative net fiscal contributors (Gott and Johnston, 2002).

47 5.1.2 Labour market effects

Third country immigrants to the UK have historically experienced higher unemployment rates than the domestic population (Dobson et al., 2001) and a lower employment rate (Sumption, 2010). With the onset of economic downturn from 2008, unemployment rose and employment fell among both groups, although the size of the gap between them was more or less unchanged (Ibid.). However, aggregate trends are not replicated among all immigrant groups. Sumption’s analysis shows that unemployment rose more for immigrants from Africa and Pakistan/Bangladesh, to reach 14 and 17 per cent respectively by mid-2009. By comparison, recent A8 immigrants as well as those from the EU15 and North America fared as well or better than the UK-born. The reasons for these differences are unclear, although suggested factors include education, minority status,24 age, gender, the level of economic development in source countries and the sectors in which different immigrant groups work. Migrants who became unemployed may have opted to return to their home country, reducing the unemployment rate. Hence, it is impossible to generalise for all migrants even though, as one sees below, much of the economic effort to explain the impact of migration is made at a fairly aggregate level (Ibid.).

Most research on the effects of immigration on wages and the employment prospects of domestic workers find them to be small or absent, although there is some evidence from the USA that displacement is more likely during a downturn when competition for jobs is higher (Peri, 2010).

The overall evidence suggests that any effects seen are not evenly spread across the wage distribution. Ruhs concludes that “UK research suggests that immigration has a small impact on average wages of existing workers but more significant effects along the wage distribution: low-wage workers lose while medium and high-paid workers gain” (2011).

The first empirical study of the effects of immigration in the UK on local labour markets, wages and employment was by Dustmann et al. (2003). They looked at immigrants as a whole, without regard to skill or education levels. Their main finding was that “if there is an impact of immigration on employment then it is statistically poorly determined and probably small in size” and that “higher immigration appears to be associated with higher wage growth in the currently resident population” (Ibid, p4).

In a second study (2005), the same authors disaggregated the labour force by education and skill level. Results showed unclear effects of immigration, depending on the skill mix of the resident population and the way the economy may adjust to changes in the skill mix. They concluded that there was little evidence of overall adverse effects of immigration on native outcomes with the possibility of a small positive effect upon wages for low-skilled natives.

24 In this instance, ‘minority status’ refers primarily to non-white ethnic groups. However, the analysis here also relates ethnicity to birthplace, educational and income levels and generational effects. See Sumption (2010) for a complete explanation. 48 In a further study, Dustmann et al. (2008) estimated that a one percentage point increase in the migrant share in the working age population increased non-migrant wages on average by between +0.3 and +0.4 per cent. A study by Lemos and Portes (2008) supported this conclusion with a similar estimate, although it was statistically insignificant.

These studies found that although some workers may see a positive impact on wages from immigration, the evidence for low-paid workers was less clear. For example, Dustmann et al. (2008) found that the impact of an increase in the migrant share on low paid non-migrants was negative, with those in the first decile experiencing a wage reduction of 0.5 per cent. However, Lemos and Portes (2008) found a small positive impact, although this was not statistically significant.

Reviewing the period 2001 to 2007, Reed and Latorre (2009) also found that overall the effects of immigration on wages were small. A one percentage point increase in the share of migrants in the UK population would reduce wages by 0.3 per cent.

Evidence also suggests that the impact of A8 migration has not had a statistically significant impact on wages or employment. Blanchflower et al (2007) concluded that A8 immigration had tended to increase supply by more than it increased demand in the UK in the short run and thereby acted to reduce inflationary pressures. Lemos and Portes (2008) found no statistically significant impact of A8 migration on claimant unemployment, either overall or for any identifiable subgroup. In particular they found no adverse impacts on the young or low-skilled nor was there a statistically significant impact on wages, either on average or at any point in the wage distribution, although the evidence here was less complete.

This brief review of research evidence for the labour market effects of migration suggests that the scale and nature of economic benefits to the economy are unclear, and the aggregate impact of labour migration policies is uncertain. Most studies of the effects of immigration on the wages and the employment prospects of domestic workers find them to be small or absent. There is some evidence of negative employment effects (e.g. reduced wages or increased unemployment) for those with intermediate levels of education, but this is offset by positive effects on the better qualified. No statistically significant impact of A8 migration has been detected on claimant unemployment, either in total or for any identifiable subgroup. In particular, there has been no adverse impact on the young or low-skilled, nor was a significant impact detected on wages, either on average or at any point in the wage distribution.

5.2 Effectiveness of specific government policies

5.2.1 Low-skilled workers

Any assessment of the success of government schemes in managing inflows of low-skilled labour must take into account the very large flows of A8 workers to the UK after May 2004. As seen above, most of these initially went into low-skilled jobs, although there is plenty of evidence that many of those arriving were well-educated and qualified. To a significant extent, therefore, the market

49 prevailed since it was discovered that the UK economy had large numbers of jobs which had been hidden. The relatively modest number of migrants previously coming through SAWS and SBS was dwarfed by the new EU nationals after 2004. Since 2007, the quotas for low- skilled workers in these schemes have been filled by Bulgarian and Romanian workers with no need for low-skilled labour recruitment from third countries.

5.2.2 Skilled workers

The evidence presented for the nationality and occupations of third country nationals earlier in this report suggests the UK policy of confining them to skilled workers, including those in shortage occupations, has generally been successful. The UK Government has sought to manage the entry of skilled workers from third country sources mainly through work permits, HSMP and PBS. Until very recently there has been no set limit and the government immigration system has been largely responsive to the requirements of employers. The main constraints on employers have been the degree to which skills are in shortage and the requirement to carry out a resident labour market test. Such tests are always difficult to monitor, especially in respect of advertising within the broader EU.

However, sample surveys of PBS applicants have queried the assumption that all highly skilled or skilled individuals coming to the UK are actually working in highly skilled or skilled employment (UK Border Agency, 2011). Of 1,286 Tier 1 applicants surveyed, almost 30 per cent were in low-skilled employment or not in employment at all at the time of the survey. Similar findings were found in a separate analysis of Tier 1 applicants, where 29 per cent were assessed to be in unskilled employment (UK Border Agency 2010).25 Of 895 Tier 2 applicants surveyed, 17 per cent were also found to be either unemployed, in low-skilled jobs or had not stated the skilled level of their job. So whilst talented individuals may be coming to the UK, they may not always be making the best use of their skills.

ICTs present particular difficulties when managing migration. Economic globalisation means that the UK is in competition with other countries for foreign direct investment and the attraction of highly skilled workers. The nature of their operations means that large international firms need to attract the best recruits and move their staff around the globe. A problem for governments is how and at what level to set minimum salary and other conditions as a prerequisite for entry. In November 2010, the UK Government announced it will tighten up on these conditions by raising the required salary level from April 2011. However, it is significant that ICTs were exempted from the limit on other Tier 2 routes of entry.

5.3 Issues for future policy

In the decade prior to the recent economic downturn the strength of the UK economy pulled in highly skilled workers from third countries. However, it is not clear how the labour demands of the economy will shift in the coming years. The migration patterns observed between 2004 and 2009 are unlikely to be a sound guide to the near future. The combination of the introduction of the PBS, economic downturn

25 Note: the methodology and definitions used in these two reports differed. 50 and the policies of the new Coalition Government has already created a new set of conditions and a tightening of control over labour from third countries. Skilled immigration from third countries has, on the whole, occurred within a limited number of sectors, notably IT, health, education and finance. The future labour demands of each of these are uncertain. The public sector labour force is likely to fall in the next few years and it is unclear how much slack will be taken up by the private sector. Furthermore, trends in the economy as a whole may not be replicated regionally, so that requirements for foreign labour will probably have a strong geographical dimension. Policies towards family migration and the employment of the dependants of labour immigrants will also have some effect.

In addition to the behaviour of the economy, other key elements underlie the nature of future migration policy, notably changing demography, the importance of language and government policies in related areas. These are briefly discussed below.

5.3.1 Demographic change

At the time of writing there was no requirement to encourage labour migration to the UK for demographic reasons.

The latest (2008-based) population projections for the UK (Office for National Statistics, 2009) suggest that both the total population and that of working age will continue to increase for the foreseeable future. In the ten years 2008 to 2018 the working age population is projected to rise from 38.1 to 40.8 million, with another million added by 2023. However, the old age support ratio (working age/pensionable age) is projected to increase slightly, from 3.23 to 3.25 in 2018, before deteriorating thereafter to 3.11 in 2023. These figures suggest that in the immediate future the UK workforce will be relatively stable, but in the more distant future additional labour to that in the resident labour force will be needed to provide the services required by an ageing population. What is unclear is how far increased productivity will affect labour-intensive service provision.

5.3.2 Language and the assimilation of European migrants

Much recruitment of third country nationals during past decades has been to fill highly skilled occupations in spheres where fluency in English is essential, such as health, social care and education. This is one reason why many well-qualified East Europeans have tended to work in low-skill jobs, and also suggests why their assimilation into the UK workforce may increase in future. Research in the UK and other Western Europe countries (see, for example, den Adel et al., 2005; Organisation for Economic Co- operation and Development, 2007) has found health services frequently citing language as an advantage of recruiting from former colonies, while a recent study of migrant care workers in English-speaking countries also emphasises that language skills are fundamental to the provision of care to older people (Spencer et al., 2010). The supply of skilled European workers into UK shortage occupations may therefore increase once they, or the next generation, come to master the language. On the other hand, of course, such migration will also depend on economic conditions in their home countries.

51 5.3.3 Government policies

Migration policy will continue to be affected by other government policies, for instance with respect to health, education and business. As an example, for the past decade or more strategies have been employed in the UK to expand the supply of UK professionals entering the health workforce, including new medical schools, financial incentives, more flexible working conditions and new career paths. There have also been strategies to improve the recruitment and retention of teachers. The LFS evidence reported in section 3.2.1 shows that the numbers of UK doctors, nurses and teachers were higher in 2009 than in 2004 (32% higher in the case of doctors), but that this did not lead to a reduction in third country professionals in these occupations. According to the LFS statistics, there was a total increase of 55,000 doctors in the labour force between 2004 and 2009, of whom 15,000 were non-UK nationals. These trends reflect a period of rapid expansion of investment in these public services, but they also indicate that more training will not necessarily eliminate the demand for migrant labour.

To predict future demand for labour in these types of employment and the extent to which migrant labour will be required therefore involves assumptions about future policy and investment in health and care services (public and private sectors), in the school system and in higher education and training, including support to students and trainees. Assumptions are also required about the availability of alternative employment and types of unemployment which might, for instance, result in unemployed graduates retraining as teachers in shortage subjects. Furthermore, there are also policy specifics which could ease or exacerbate recruitment in different professions (see, for example, Department for Education, 2010). An extension of the private sector into public services could bring in more foreign providers, particularly from the USA, expanding this strand of migration.

Government business policy is another significant variable. The encouragement and retention of foreign investment may be at odds with a policy to reduce labour immigration from third countries. The growing level of skilled migration reported in section 3.2.1 is a product of changes in both the indigenous and foreign workforce. As the exchange of migrants with desirable skills, including ICTs, develops in an increasingly global labour market, pressure is on the Government to ensure that the UK is able to retain its position as a global skills hub. Taken together, all these issues present a prospect of substantial change whose consequences are hard to assess at the present time.

52 ANNEX A: BIBLIOGRAPHY

Achato, L., Eaton, M. and Jones, C. (2010) The Migrant Journey. Research Report 43, Home Office, London.

Anderson, B., Ruhs, M., Rogaly, B. and Spencer, S. (2006) Fair enough? Central and East European migrants in low-wage employment in the UK. COMPAS, Oxford.

Blanchflower, D.G., Saleheen, J. and Shadforth, C. (2007) The Impact of the Recent Migration from Eastern Europe on the UK Economy. Bank of England, London.

Cabinet Office (2010) The Coalition: our programme for government. Cabinet Office, London.

Coleman, D. and Rowthorne, R. (2004) ‘The economic effects of immigration into the United Kingdom’ Population and Development Review, vol.30 (4), pp 579–624.

Department for Education (2010) The Importance of Teaching. Department for Education, London.

Department of Communities and Local Government (2009) Managing the Impacts of Migration: Improvements and Innovations. Department of Communities and Local Government. London. den Adel, M., Blauw, W., Dobson, J., Hoesch, K. and Salt, J. (2004) ‘Recruitment and the migration of foreign workers in health and social care,’ IMIS- Beiträge, vol. 25, pp 201–230.

Dobson, J., Koser, K., McLaughlan, G. and Salt, J. (2001) International Migration and the United Kingdom. RDS Occasional Paper 75, Home Office, London.

Dustmann, C., Fabbri, F., Preston, I. And Wadsworth, J. (2003) The Local Labour Market Effects of Immigration in the UK. Home Office Online Report 06/03. London.

Dustmann, C., Fabbri, F. and Preston, I. (2005) The Impact of Immigration on the UK Labour Market. Discussion paper Series 01/05, Centre for Research and Analysis of Migration, UCL, London.

Dustmann, C., Frattini T. and Preston I. (2008), The Effect of Immigration Along the Distribution of Wages, CReAm Discussion Paper Series 03/08, Centre for Research and Analysis of Migration, UCL.

Dustmann, C., Frattini, T. and Halls, C. (2009) Assessing the Fiscal Costs and Benefits of A8 Migration to the UK. Discussion Paper 19/09, Centre for Research and Analysis of Migration, UCL.

53 Gordon, I., Scanlon, K., Travers T. and Whitehead, C. (2009). Economic impact on London and the UK of an Earned Regularisation of Irregular Migrants in the UK. GLA Economics, London.

Gott, C. and Johnston, K. (2002) The Migrant Population in the UK: Fiscal Effects. RDS Occasional Paper 77. Home Office, London.

Green, D. (2010) Speech to the Royal Commonwealth Society, September 6th.

H.M. Treasury (1999) Stability and Steady Growth for Britain Pre-Budget Report. HMSO, London.

Home Office and Department of Work and Pensions (2007) The Economic and fiscal Impact of Immigration. A Cross-Departmental Submission to the House of Lords Select Committee on Economic Affairs. Home Office, London.

House of Lords, Select Committee on Economic Affairs (2008) The Economic Impact of Immigration. London.

Kaczmarczyk, P. and Okolski, M. (2008) ‘Demographic and labour market impacts of migration on Poland’, Oxford Review of Economic Policy, 24 (3), 599–624.

Lemos, S. and Portes, J. (2008) The Impact of Migration from the New Eastern Union Member States on Native Workers. Department of Work and Pensions, London.

Lilley, P. (2005) Too Much of a Good Thing? Towards a Balanced Approach to Immigration. Centre for Policy Studies, London.

May, T. (2010) Speech at London’s Policy Exchange, 5 November .

Migration Advisory Committee (2008) Skilled, Shortage, Sensible: the Recommended Shortage Occupation Lists for the UK and Scotland. Migration Advisory Committee, London.

Migration Advisory Committee (2009a) Skilled, Shortage, Sensible: First Review of the Recommended Shortage Occupation Lists for the UK and Scotland. Migration Advisory Committee, London.

Migration Advisory Committee (2009b) Skilled, Shortage, Sensible: Second Review of the Recommended Shortage Occupation Lists for the UK and Scotland. Migration Advisory Committee, London.

Migration Advisory Committee (2010a) Skilled, Shortage, Sensible: Third Review of the Recommended Shortage Occupation Lists for the UK and Scotland. Migration Advisory Committee, London.

54 Migration Advisory Committee (2010b) Limits on Migration: Limits on Tier 1 and Tier 2 for 2011/12 and Supporting Policies. Migration Advisory Committee, London.

Nickell, S. and Salaheen, J. (2008). The Impact of Immigration on Occupational Wages: Evidence from Britain. Federal Reserve Bank of Boston Working Paper No. 08-6. Boston.

Office for National Statistics (2001) Standard Occupational Classification 2000. Office for National Statistics, London.

Office for National Statistics (2009) National Population Projections 2008-Based. Office for National Statistics, London.

Organisation for Economic Cooperation and Development (2007) Immigrant health workers in OECD countries in the broader context of highly skilled migration. Part III in International Migration Outlook, OECD, Paris.

Peri, G. (2010) The Impact of Immigrants in Recession and Economic Expansion. Migration Policy Institute, Washington DC.

Reed, H. and Latorre, M. (2009) The Economic Impacts of Migration on the UK Labour Market. Institute for public Policy Research, London.

Rowthorne, R. (2004) The Economic Impact of Immigration. Civitas Online Report. http://www.civitas.org.uk/pdf/Rowthorn_Immigration.pdf. Last accessed 07/03/11

Ruhs, M. (2011) The Labour Market Effects of Immigration: Briefing COMPAS, University of Oxford. Oxford. http://www.migrationobservatory.ox.ac.uk/sites/files/migobs/Briefing%20-%20 Labour%20Market%20Effects%20of%20Immigration.pdf

Salt, J. (2009): UK Data Sources on International Migration. Paper presented at Royal Statistical Society, Labour Market Statistics Users Group Meeting, 23 January , London.

Shury, J., Winterbotham, M., Davies, B. and Oldfield, K. (2010). National Employer Skills Survey for England, 2009. Evidence Report 13, UK Commission for Employment and Skills. London.

Spencer, S., Martin, S., Bourgeault, I.L. and O’Shea, E. (2010). The Role of Migrant Care Workers in Ageing Societies: Report on Research Findings in the United Kingdom, Ireland, Canada and the United States. IOM Research Series No. 41, International Organisation for Migration, Geneva.

Sriskandarajah, D., Cooley, L. and Reed, H. (2005) Paying Their Way: The Fiscal Contribution of Immigrants in the UK. Institute for Public Policy Research, London.

55 Sumption, M. (2010) Foreign workers and immigrant integration: emerging from recession in the United Kingdom. 47–65 in Papademetriou, D.G., Sumption, M. and Terrazas, A. Migration and Immigrants Two Years after the Financial Collapse: Where Do We Stand? Migration Policy Institute, Washington DC.

Sumption, M. and Somerville, W. (2009). The UK’s New Europeans: Progress and Challenges Five Years After Accession. Equality and Human Rights Commission, London.

UK Border Agency (2010) Points Based System Tier 1: an Operational Assessment. London, Home Office.

Home Office (2011) Users’ Views of the Points-Based System. Research Report 49 http://rds.homeoffice.gov.uk/rds/pdfs11/horr49c.pdf

Wiese, J. and Thorpe, K. (2011) Temporary and Circular Migration: empirical evidence, current policy practice and future options. http://emn.intrasoft-intl.com/Downloads/prepareShowFiles.do ;jsessionid=458529CC01650F16C36AB6E95BB6A095?entryTitle=02_Temporary%20and%20 CIRCULAR%20MIGRATION:%20empirical%20evidence,%20current%20policy%20practice%20 and%20future%20options

Woodbridge, J. (2005), Sizing the Unauthorised (Illegal) Migrant Population in the United Kingdom in 2001. Home Office, London.

56 ANNEX B: GLOSSARY - List of abbreviations used in the report

A8 countries Accession 8 countries. Member states of the European Union that joined in May 2004 except Cyprus and Malta (Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia) CoS Certificate of Sponsorship DFID Department for International Development EEA European Economic Area EFTA European Free Trade Association EMN European Migration Network EU European Union EU-10 The 10 Member States that joined the European Union in May 2004 (Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Slovakia, and Slovenia) EU-15 The 15 Member States of the European Union before expansion in May 2004 (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, Sweden and UK). EU-2 The two Member States of the European Union that joined in 2007 (Bulgaria, Romania) EU-27 All 27 Member States of the European Union from 2007 HM Her Majesty (acronym precedes some government department names) HSMP Highly Skilled Migrant Programme ICT Intra-Company Transfer IGS International Graduate Scheme IPS International Passenger Survey ISCO-88 International Standard Classification of Occupations IT Information Technology LFS Labour Force Survey LTIM Long-Term International Migration MAC Migration Advisory Committee NINo National Insurance Number OECD Organisation for Economic Cooperation and Development ONS Office for National Statistics PBS Points-Based System (UK) PSWR Post-Study Work Route RLMT Resident Labour Market Test SAWS Seasonal Agricultural Workers Scheme SBS Sectors Based Scheme SEGS Science and Engineering Graduates Scheme SOC2000 Standard Occupation Classification (UK) SSCs Sector Skills Councils Third Countries Non-EU countries UK United Kingdom USA United States of America WHMs Working Holiday Makers WHS Working Holidaymaker Scheme WRS Worker Registration Scheme YMS Youth Mobility Scheme WFJ Workforce Jobs NOMIS National On-Line Manpower Information System (Official UK Labour Statistics information service) (https://www.nomisweb.co.uk/) SOPEMI Système d’Observation Permanente des Migrations Internationales OECD Continuous Reporting System on International Migration 57 ANNEX C: METHODOLOGY AND DEFINITIONS

Data sources used

Labour Force Survey (LFS) The LFS is a quarterly sample survey of households living at private addresses in Great Britain. It is carried out under a European Union Directive and uses internationally agreed concepts and definitions. It seeks information on respondents’ personal circumstances and their labour market status during a specific reference period, normally a period of one week or four weeks (depending on the topic) immediately prior to the interview.

LFS data were used to compile the migrant stock tables (Tables D1, D3 and D5).

For LFS data from a single quarter, the sampling error relating to a cell size of 10,000 is about +/- 2,000. To compensate to some extent for sampling error and for seasonal variations, for each year the average of the four quarterly surveys was taken.

There are both practical and conceptual reasons for not using the LFS to produce migration flow tables. The practical issues are data availability and complexity of compilation and interpretation. The conceptual reasons are that LFS-based flow tables cannot provide information on entry and exit from both the labour market and the country. While the LFS can be used to identify immigrants, as those who were living outside the country a year ago, numbers are too small to provide reliable flow data.

International Passenger Survey (IPS) The IPS is a survey of a random sample of passengers entering and leaving the UK by air, sea or the Channel Tunnel. Over a quarter of a million face-to-face interviews are carried out each year. One of the main aims of the survey is to provide information on people migrating to and from the UK and in addition to the main fieldwork, special shifts are carried out to increase the number of migrants interviewed. The number interviewed is around 5,000 per year.

In the UK the only source providing data on entry and exit is the IPS which is used for Tables D2, D4 and D6. The survey includes only those entering or leaving for a year or more, thus excluding short-term movers. Although some preliminary work has been done by ONS on numbers of short- term movers, there are as yet no regular annual tables. The data also exclude migrants who leave the labour market but not the country, as well as asylum and other migration status switchers (see below) who cannot be allocated to IPS categories.

The small sample size prevents detailed breakdown by skill levels.

58 Tables D2 and D4 relate to all migrants who gave work as their principal reason for coming to or leaving the UK. Only the principal reason is included. Hence, someone entering or leaving initially for family or study reasons but who then takes up work is excluded.

In Table D6 an alternative measurement of labour flows, derived from the IPS, is employed because it allows a breakdown by major skill groups. In this measurement, a labour migrant is someone who was in the labour market before and after moving. The measure used in Table D6 excludes those, like students, who were not working prior to the move but entered the labour market immediately afterwards.

An adjustment to the IPS data is necessary to provide a better estimate of long-term international migration. The raw data include many people seeking asylum and dependants of asylum seekers. Further adjustments are made for other people who intend to be migrants but who in reality stay in the UK or abroad for less than a year and for those who state an initial intention to stay for more than a year but actually leave before this. Other adjustments are made for migration between Northern Ireland and the Republic of Ireland. These adjustments are used to produce Long-Term International Migration (LTIM) estimates. Unfortunately, it is not possible to break down LTIM data according to most migrant characteristics so raw IPS statistics are used in this report.

No additional filtering of short-term migrants was applied (i.e. those between 6 months and 1 year) since the combination of variables available would have reduced the resulting sample sizes even further.

UK Border Agency Administrative Datasets For the purposes of this study, the UK Border Agency provided administrative data (management information) on the operation of a series of schemes through which labour immigration has been managed during the period. The administrative datasets used are as follows.

●● Highly Skilled Migrants Programme

●● Work Permit Scheme

●● Points-Based System Tier 1 and Tier 2 applicants (excluding dependants)

●● Points-Based System Sponsorship (Tiers 2 and 5)

●● Worker Registration Scheme

●● Sectors Based Scheme

●● Science and Engineering Graduates Scheme

●● Seasonal Agricultural Workers Scheme

●● Landing Cards

●● Jobcentre Plus.

59 Both survey and administrative data present issues for analysis. The range of questions in surveys allows the relationship between variables to be examined but because data come from small samples, sampling error is a major problem when attempting analysis of migrant characteristics. Administrative data for the most part relate to individual entry streams. Their advantage is that because they record all events sampling error is not a problem. However, different administrative sources capture different migrant characteristics and may use different definitions from each other and from survey data. Also, because the data are collected for operational use, they do not always contain all the necessary or desirable data for research purposes.

Administrative datasets are used in Section 3.2 to supplement discussion based on the common tables of trends and general developments for each route of entry and world region. They provide additional detail on the ways in which labour needs in particular sections of the labour market have been targeted by the Government, the mechanisms used and the outcomes for specific national groups.

Documentary sources Documentary sources include academic literature and websites of relevant organisations, especially the UK Border Agency. Other documentation includes the series of reports relating to migration and the labour market produced by the Migration Advisory Committee, the recent UK Border Agency report ‘The Migrant Journey’ (Achato et al., 2010) and the annual reports to the OECD of the UK SOPEMI Correspondent.

Definitions

Nationality Migrant nationality information was retrieved from the LFS nationality variables for each quarter. The LFS collects two nationality variables, corresponding to two questions in the survey. Question 30 in the LFS questionnaire asks ‘What is your nationality?’ offering only five options: ‘UK, British’, ‘Irish Republic’, ‘Hong Kong’, ‘China’, and ‘Other’. This variable is stored under ‘Nation’ (2004–2005) or ‘Ntnlty’ (2006 onwards). If the respondent answers ‘Other’ s/he then specifies a particular nationality, stored in a separate variable, termed ‘Nato’ (2004–2005) or ‘Nato7’ (2006 onwards). For this analysis, a single nationality variable was constructed by amalgamating the answers to both questions.

Occupation This section describes the process of converting the UK’s occupational classification into the ISCO- 88 categories. The large majority of SOC occupations aligned well with ISCO-88. Where an exact match was not available, the best approximation between the variables has been used, based on the descriptions of the categories in both classifications.

60 A correspondence was created between the SOC2000 variables in the LFS (“sc2kmmj” and “sc2kmmn”) and the five EMN occupational groups. Table C1 details how the SOC2000 variables were aggregated into the EMN groups. A caveat must be noted that there is some variation of skill level within the 1-digit SOC groupings. For example, in Table C1, highly skilled occupations are classified as SOC groups one to three at the one digit level. However, within this grouping there are some occupations at SOC skill level 4 (e.g. 1,111 senior officials in national government) and some at SOC skill level 3 (e.g. 1,224 publicans and managers of licensed premises). However such differences in skill level were considered to fall within the EMN broad occupational groups rather than between them.

Table C1 Aggregation of LFS SOC2000 variables into EMN broad occupational groups EMN Occupational Grouping LFS SOC2000 categories Source: LFS variable Highly Skilled 1 Managers and Senior Officials, sc2kmmj 2 Professional occupations, 3 Associate Professional and Technical Skilled 4 Administrative and Secretarial, sc2kmmj 5 Skilled Trades Occupations, 6 Personal Service Occupations, 7 Sales and Customer Service Occupations, 8 Process, Plant and Machine Operatives Low Skilled 9 Elementary Occupations sc2kmmj Researchers 211 Science Professionals, 232 Research sc2kmmn Professionals

Table D5 offers a selection of more detailed ISCO-88 (4 digit) occupational categories, as specified by EMN. Because the LFS SOC2000 detailed categories (variable ‘soc2km’) do not exactly correspond with the ISCO-88 (4 digit) categories specified for Table D5, the closest variables were aggregated to the definitions given in the EMN specification.Table C2 shows details of the categories included.

61 Table C2 Aggregation of LFS SOC2000 detailed variables into EMN/ISCO-88 4 digit occupational groups used to produce Table D5 (Stocks of workers employed by specific occupations) ISCO-88 occupational category Constructed from the following categories in LFS variable “soc2km” (abbreviations used) Medical doctors (2221) 2211 Medical practitioners Architects, Engineers and related 2121 Civil engineers, professionals (214) 2122 Mechanical engineers, 2123 Electrical engineers, 2124 Electronics engineers, 2125 Chemical engineers, 2126 Design and development engineers, 2127 Production and process engineers, 2128 Planning and quality control engineers, 2129Engineering professionals not elsewhere classified. 2431 Architects, 2432 Town planners, 2433 Quantity surveyors, 2434 Chartered surveyors (not quantity surveyors), Teaching personnel (23) 2311 Higher education teaching professionals, 2312 Further education teaching professionals, 2313 Education officers, school inspectors, 2314 Secondary education teaching professionals, 2315 Primary & nursery education teaching professionals, 2316 Special needs education teaching professionals, 2317 Registrars & senior administrators in education establishments, 2319 Teaching professionals not elsewhere classified Nursing and midwifery 6111 Nursing auxiliaries and assistants, professionals (2230) 3211 Nurses, 3212 Midwives, Skilled Agricultural and Fishery 5111 Farmers, Workers (61) 5112 Horticultural trades, 5113 Gardeners and grounds(wo)men, 5119 Agriculture and fishing trades not elsewhere classified Cooks (5122) 5434 Chefs, cooks Personal care and related workers 6115 Care assistants and home carers not elsewhere classified (5139) Child-care workers (5131) 6122 Childminders and related occupations Housekeepers and related 6231 Housekeepers and related occupations workers (5121) Labourers In Mining, 9119 Fishing & agriculture related occupations not elsewhere Construction, Manufacturing And classified Transport (93) 9121 Labourers building & woodworking trades, 9129 Labourers other construction trades not elsewhere classified Waiters waitresses and bartenders 9224 Waiters, waitresses, (5123) 9225 Bar staff

62 Tables D2 and D4 were provided by the ONS Migration Statistics Unit according to a set of agreed equivalences between IPS detailed 30 occupational categories and the EMN five broad groups. Such equivalence was agreed in consultation with ONS Migration Statistics and follows the EMN recommendation for ISCO equivalent categories. The agreed equivalence is described in Table C3.

Table C3 Aggregation of IPS detailed occupations into EMN broad occupational groups used by ONS to produce Tables D2 and D4 (Flows of workers by main category of employment and nationality)

Table C3 Aggregation of IPS detailed occupations into EMN broad occupational groups used by ONS to produce Tables D2 and D4 (Flows of workers by main category of employment and nationality) EMN broad group IPS occupational category Highly Skilled 1 Managers & senior officials Highly Skilled 15 Corporate Managers Highly Skilled 16 Managers and proprietors in agriculture & services Highly Skilled 2 Professional Highly Skilled 21 Science and technology professionals Highly Skilled 22 Health professionals Highly Skilled 23 Teaching and research professionals Highly Skilled 24 Business and public services professionals Highly Skilled 3 Associate professional and technical Highly Skilled 31 Science and technology associate professionals Highly Skilled 32 Health and social welfare professionals Highly Skilled 34 Culture media and sports occupations Highly Skilled 35 Business and public service associate professionals Skilled 33 Protective service occupations Skilled 4 Administrative & secretarial Skilled 5 Skilled trade Skilled 51 Skilled agriculture trades Skilled 52 Skilled metal and electrical trades Skilled 53 Skilled construction and building trades Skilled 54 Textiles, printing and other skilled trades Skilled 6 Personal services Skilled 7 Sales and customer service Skilled 8 Process, plant and machine operatives Low Skilled 9 Elementary occupation Inactive/Other 10 Student Inactive/Other 11 Houseperson Inactive/Other 12 Retired/unoccupied Inactive/Other 99 Not stated Inactive/Other 999 Data not available Inactive/Other 9998 Not stated

63 ANNEX D: COMMON STOCK AND FLOW TABLES

Table D1 Stock of workers by main category of employment, 2004 - 2009 2004 1. Nationals 2. (Other) EU15 Nationals Main % of categorisation Male Female Total Male Female Total Total A. Highly 6,485,699 4,427,938 10,913,637 143,910 124,486 268,396 49.8 skilled B. Skilled 6,349,508 6,536,290 12,885,797 87,602 104,095 191,697 35.6 C. Low skilled 1,703,217 1,391,716 3,094,933 33,788 30,044 63,832 11.9 D. Researchers 110,464 72,934 183,398 5,095 9,532 14,627 2.7 Total 14,648,887 12,428,877 27,077,765 270,395 268,157 538,551 100.0

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main % of % of categorisation Male Female Total Total Male Female Total Total A. Highly 7,881 9,307 17,188 20.2 1,280 1,798 3,078 25.0 skilled B. Skilled 24,876 19,918 44,794 52.6 3,791 2,608 6,399 52.1 C. Low skilled 10,250 12,549 22,799 26.8 0 2,811 2,811 22.9 D. Researchers 268 133 401 0.5 0 0 0 0.0 Total 43,275 41,907 85,182 100.0 5,071 7,216 12,287 100.0

Main 5. Third-Country Nationals 6. No. of unfilled categorisation Male Female Total % of Total vacanciesa A. Highly 242,720 187,999 430,719 46.6 39,464 skilled B. Skilled 179,183 157,648 336,831 36.4 148,846 C. Low skilled 97,941 45,628 143,568 15.5 87,885 D. Researchers 7,601 5,925 13,526 1.5 - Total 527,445 397,200 924,644 100.0 276,195 Source: Labour Force Survey Notes: No data available for Seasonal workers. a Data relate to stock of June of each year.

64 Table D1 Stock of workers by main category of employment, 2004 - 2009 (continued) 2005 1. Nationals 2. (Other) EU15 Nationals Main % of categorisation Male Female Total Male Female Total Total A. Highly 6,061,907 4,253,119 10,315,026 151,949 130,895 282,843 53.0 skilled B. Skilled 5,919,483 6,033,047 11,952,529 79,052 94,481 173,533 32.5 C. Low skilled 1,537,143 1,260,909 2,798,051 33,957 30,094 64,050 12.0 D. Researchers 98,706 71,795 170,501 6,409 6,437 12,845 2.4 Total 13,617,237 11,618,869 25,236,107 271,365 261,906 533,271 100.0

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main % of % of categorisation Male Female Total Total Male Female Total Total A. Highly 10,150 11,822 21,971 13.2 1,404 1,687 3,090 15.3 skilled B. Skilled 49,163 34,670 83,833 50.3 5,683 6,287 11,970 59.2 C. Low skilled 31,864 28,298 60,162 36.1 1,884 3,269 5,153 25.5 D. Researchers 479 377 856 0.5 0 0 0 0.0 Total 91,655 75,166 166,821 100.0 8,970 11,243 20,213 100.0

Main 5. Third-Country Nationals 6. No. of unfilled categorisation Male Female Total % of Total vacanciesa A. Highly 247,831 203,962 451,793 46.4 31,839 skilled B. Skilled 190,288 166,566 356,853 36.6 138,727 C. Low skilled 99,355 50,838 150,192 15.4 66,237 D. Researchers 8,976 6,807 15,783 1.6 - Total 546,449 428,172 974,621 100.0 236,803 Source: Labour Force Survey Notes: No data available for Seasonal workers. a Data relate to stock of June of each year.

65 Table D1 Stock of workers by main category of employment, 2004 - 2009 (continued) 2006 1. Nationals 2. (Other) EU15 Nationals Main % of categorisation Male Female Total Male Female Total Total A. Highly 6,620,230 4,778,377 11,398,606 141,318 128,916 270,233 51.1 skilled B. Skilled 6,295,729 6,362,821 12,658,550 87,347 89,781 177,128 33.5 C. Low skilled 1,622,021 1,360,426 2,982,446 38,503 28,908 67,410 12.7 D. Researchers 99,048 76,978 176,027 9,886 4,557 14,443 2.7 Total 14,637,027 12,578,601 27,215,628 277,054 252,161 529,215 100.0

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main % of % of categorisation Male Female Total Total Male Female Total Total A. Highly 21,516 16,616 38,132 13.1 4,125 1,949 6,074 23.3 skilled B. Skilled 86,530 52,437 138,967 47.9 9,392 4,450 13,842 53.1 C. Low skilled 61,862 50,962 112,824 38.9 2,969 3,005 5,974 22.9 D. Researchers 0 272 272 0.1 171 0 171 0.7 Total 169,908 120,287 290,194 100.0 16,656 9,404 26,060 100.0

Main 5. Third-Country Nationals 6. No. of unfilled categorisation Male Female Total % of Total vacanciesa A. Highly 297,038 221,231 518,269 46.5 39,066 skilled B. Skilled 226,312 191,648 417,960 37.5 140,381 C. Low skilled 107,913 53,516 161,429 14.5 64,019 D. Researchers 10,033 6,469 16,502 1.5 - Total 641,296 472,864 1,114,160 100.0 243,466 Source: Labour Force Survey Notes: No data available for Seasonal workers. a Data relate to stock of June of each year.

66 Table D1 Stock of workers by main category of employment, 2004 - 2009 (continued) 2007 1. Nationals 2. (Other) EU15 Nationals Main % of categorisation Male Female Total Male Female Total Total A. Highly 6,623,091 4,843,704 11,466,795 177,703 135,542 313,245 55.0 skilled B. Skilled 6,251,151 6,305,363 12,556,513 86,583 91,115 177,698 31.2 C. Low skilled 1,649,001 1,323,256 2,972,257 33,497 29,646 63,143 11.1 D. Researchers 99,482 78,749 178,231 7,120 8,523 15,643 2.7 Total 14,622,725 12,551,071 27,173,796 304,902 264,826 569,728 100.0

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main % of % of categorisation Male Female Total Total Male Female Total Total A. Highly 21,285 27,249 48,534 11.3 4,382 3,947 8,329 30.3 skilled B. Skilled 149,451 76,702 226,153 52.6 8,245 4,757 13,002 47.3 C. Low skilled 86,052 68,304 154,356 35.9 2,630 3,499 6,129 22.3 D. Researchers 0 591 591 0.1 0 0 0 0.0 Total 256,788 172,846 429,634 100.0 15,257 12,203 27,460 100.0

Main 5. Third-Country Nationals 6. No. of unfilled categorisation Male Female Total % of Total vacanciesa A. Highly 318,679 216,060 534,740 46.2 60,070 skilled B. Skilled 235,802 185,189 420,991 36.4 231,259 C. Low skilled 124,812 60,117 184,929 16.0 98,631 D. Researchers 10,340 5,548 15,888 1.4 - Total 689,633 466,914 1,156,547 100.0 389,960 Source: Labour Force Survey Notes: No data available for Seasonal workers. a Data relate to stock of June of each year.

67 Table D1 Stock of workers by main category of employment, 2004 - 2009 (continued) 2008 1. Nationals 2. (Other) EU15 Nationals Main % of categorisation Male Female Total Male Female Total Total A. Highly 6,731,102 4,978,371 11,709,473 181,308 140,693 322,001 56.3 skilled B. Skilled 6,126,570 6,232,074 12,358,643 85,701 89,220 174,921 30.6 C. Low skilled 1,647,632 1,299,621 2,947,253 37,388 26,679 64,068 11.2 D. Researchers 109,348 82,785 192,133 4,838 5,993 10,832 1.9 Total 14,614,652 12,592,851 27,207,502 309,235 262,586 571,821 100.0

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main % of % of categorisation Male Female Total Total Male Female Total Total A. Highly 32,781 29,369 62,151 12.4 1,216 3,479 4,694 10.7 skilled B. Skilled 160,819 101,835 262,654 52.5 19,129 8,524 27,653 63.0 C. Low skilled 85,716 89,500 175,215 35.0 6,644 4,648 11,292 25.7 D. Researchers 170 572 741 0.1 0 284 284 0.6 Total 279,485 221,276 500,761 100.0 26,989 16,934 43,923 100.0

Main 5. Third-Country Nationals 6. No. of unfilled categorisation Male Female Total % of Total vacanciesa A. Highly 335,207 224,774 559,981 44.7 68,354 skilled B. Skilled 258,635 213,613 472,248 37.7 206,055 C. Low skilled 134,174 70,317 204,491 16.3 105,519 D. Researchers 11,082 5,245 16,327 1.3 - Total 739,097 513,949 1,253,046 100.0 379,928 Source: Labour Force Survey Notes: No data available for Seasonal workers. a Data relate to stock of June of each year.

68 Table D1 Stock of workers by main category of employment, 2004 - 2009 (continued) 2009 1. Nationals 2. (Other) EU15 Nationals Main % of categorisation Male Female Total Male Female Total Total A. Highly 6,702,082 4,998,508 11,700,590 167,171 143,732 310,903 54.0 skilled B. Skilled 5,920,426 6,153,143 12,073,569 89,332 96,155 185,487 32.2 C. Low skilled 1,528,839 1,275,930 2,804,768 34,679 29,086 63,765 11.1 D. Researchers 111,105 80,507 191,612 7,765 7,477 15,241 2.6 Total 14,262,452 12,508,088 26,770,539 298,947 276,449 575,396 100.0

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main % of % of categorisation Male Female Total Total Male Female Total Total A. Highly 35,251 36,201 71,452 14.2 3,549 4,092 7,641 12.5 skilled B. Skilled 154,687 96,317 251,003 50.0 25,456 9,181 34,637 56.5 C. Low skilled 82,041 95,291 177,332 35.3 9,742 8,524 18,266 29.8 D. Researchers 736 1,389 2,125 0.4 0 732 732 1.2 Total 272,715 229,197 501,912 100.0 38,746 22,529 61,275 100.0

Main 5. Third-Country Nationals 6. No. of unfilled categorisation Male Female Total % of Total vacanciesa A. Highly 335,377 225,892 561,270 46.1 56,688 skilled B. Skilled 243,256 210,938 454,194 37.3 140,519 C. Low skilled 114,098 68,326 182,424 15.0 56,156 D. Researchers 8,291 12,140 20,431 1.7 - Total 701,022 517,296 1,218,318 100.0 253,363 Source: Labour Force Survey Notes: No data available for Seasonal workers. a Data relate to stock of June of each year.

69 Table D2 Flows of workers by main category of employment, 2004 - 2009 2004 Inflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 18 16 6 21 24 13 10 26 7 46 16 24 14 skilled B. Skilled 5 25 4 47 9 24 2 44 4 72 7 49 11 C. Low skilled 2 51 - 72 2 48 1 63 - - 1 63 4 Total 25 13 10 22 35 11 13 22 11 40 24 22 12

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly 2 62 3 53 6 40 5 1 100 - 100 1 71 1 skilled B. Skilled 11 41 4 50 16 32 26 - - 1 95 1 95 1 C. Low skilled 3 69 3 68 6 49 43 1 100 - - 1 100 4 Total 17 31 10 32 27 23 14 1 71 1 69 2 50 1

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 38 19 31 17 69 13 60 39,464 B. Skilled 16 20 13 22 29 15 48 148,846 C. Low skilled 4 35 1 69 5 32 35 87,885 Total 58 14 45 13 103 10 54 276,195 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

70 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2004 Outflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 24 13 13 20 37 11 5 35 4 45 8 28 12 skilled B. Skilled 11 19 8 28 18 16 1 71 - 100 1 58 5 C. Low skilled 1 64 - - 1 64 - 100 - - - 100 8 Total 37 11 20 16 57 9 6 30 4 42 10 25 9

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 16 19 9 21 25 15 36 39,464 B. Skilled 7 21 6 24 13 16 39 148,846 C. Low skilled 1 43 3 43 4 32 68 87,885 Total 24 14 18 15 42 10 38 276,195 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

71 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2005 Inflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 18 20 9 22 26 15 8 37 7 36 14 26 14 skilled B. Skilled 8 35 7 30 15 23 5 38 3 57 8 32 11 C. Low skilled 4 80 2 59 5 58 1 94 - - 1 94 4 Total 29 18 17 17 46 13 14 26 9 31 23 20 11

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly 7 36 4 47 11 29 11 1 61 - - 1 61 1 skilled B. Skilled 20 26 3 58 23 24 32 ------C. Low skilled 10 43 5 48 15 33 58 ------Total 37 19 12 30 49 16 24 1 61 - - 1 61 -

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 31 11 21 15 52 9 50 31,839 B. Skilled 15 27 10 20 26 18 36 138,727 C. Low skilled 4 39 1 44 5 32 18 66,237 Total 50 11 33 12 83 8 41 236,803 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

72 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2005 Outflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 35 13 13 18 48 10 2 63 5 33 7 30 8 skilled B. Skilled 14 21 2 36 16 19 1 84 1 65 2 54 8 C. Low skilled 1 46 1 100 2 44 2 69 1 71 3 51 30 Total 50 11 16 16 65 9 5 41 7 27 13 23 10

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly - - - 100 - 100 ------skilled B. Skilled - - 2 100 2 100 8 ------C. Low skilled 1 100 - - 1 100 13 ------Total 1 100 3 94 4 71 3 ------

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 21 16 13 16 33 12 38 31,839 B. Skilled 5 23 6 21 12 16 36 138,727 C. Low skilled 3 43 1 51 4 34 39 66,237 Total 29 13 20 13 48 9 37 236,803 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

73 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2006 Inflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 7 16 9 22 16 14 8 30 3 39 11 24 12 skilled B. Skilled 6 30 3 56 9 27 4 42 1 64 6 36 8 C. Low skilled - 75 - 100 1 62 2 53 - - 2 53 9 Total 14 16 11 21 25 13 14 22 4 33 19 19 10

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly 5 48 2 66 7 40 7 - 100 1 71 1 68 1 skilled B. Skilled 16 29 13 35 29 23 41 - - 1 68 1 68 2 C. Low skilled 7 39 5 52 12 31 56 1 100 - - 1 100 7 Total 28 21 20 27 48 17 26 2 97 2 50 4 49 2

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 32 10 27 15 58 9 63 39,066 B. Skilled 16 16 10 32 26 16 37 140,381 C. Low skilled 3 41 2 67 5 36 25 64,019 Total 51 9 39 14 90 8 48 243,466 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

74 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2006 Outflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 34 13 18 17 52 10 5 41 2 51 7 33 7 skilled B. Skilled 15 18 6 29 21 16 2 51 - 100 3 46 5 C. Low skilled 4 75 1 63 5 61 ------Total 53 11 25 14 78 9 7 32 2 46 9 27 6

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly 1 100 - 100 1 77 2 ------skilled B. Skilled 3 72 1 72 5 56 10 - - 2 100 2 100 5 C. Low skilled 5 54 - - 5 54 37 ------Total 10 40 2 60 11 36 8 - - 2 100 2 100 2

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 20 15 9 20 29 12 33 39,066 B. Skilled 11 20 7 23 17 15 36 140,381 C. Low skilled 2 52 3 39 4 31 29 64,019 Total 32 12 18 14 51 9 33 243,466 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

75 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2007 Inflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 14 20 5 22 19 16 14 29 5 36 20 24 19 skilled B. Skilled 5 28 3 45 8 24 5 36 5 42 11 28 15 C. Low skilled 1 39 - 100 2 36 3 84 - - 3 84 17 Total 21 16 8 21 29 13 23 24 11 28 34 18 17

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly 10 41 4 42 14 33 13 - - - 100 - 100 - skilled B. Skilled 23 22 13 33 36 18 52 - 100 1 76 1 60 1 C. Low skilled 9 34 4 58 13 30 62 ------Total 42 17 21 25 63 14 32 - 100 1 64 1 54 1

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 35 10 18 16 52 9 50 60,070 B. Skilled 9 24 5 23 15 17 21 231,259 C. Low skilled 1 39 1 40 3 28 14 98,631 Total 45 9 24 13 70 8 36 389,960 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

76 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2007 Outflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 29 11 17 22 46 11 4 40 5 39 9 28 11 skilled B. Skilled 15 20 6 31 21 17 1 62 5 50 6 41 14 C. Low skilled 1 42 1 53 2 33 - - - 58 - 58 2 Total 45 10 24 18 69 9 5 34 10 31 15 23 11

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly - 71 2 73 2 66 3 ------skilled B. Skilled 5 51 1 94 6 45 12 - - - 100 - 100 - C. Low skilled - 100 1 90 2 85 16 ------Total 5 48 5 49 10 34 7 - - - 100 - 100 -

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 15 12 10 13 25 9 30 60,070 B. Skilled 6 14 6 15 12 10 27 231,259 C. Low skilled 3 20 3 32 6 19 62 98,631 Total 24 9 19 10 43 7 31 389,960 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

77 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2008 Inflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 20 18 11 28 31 15 8 25 8 36 15 22 14 skilled B. Skilled 4 29 3 33 7 22 5 53 2 43 7 41 12 C. Low skilled - 50 - 59 1 39 1 62 4 51 5 42 26 Total 24 16 14 23 38 13 14 24 14 26 28 18 15

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly 6 57 2 53 8 46 8 1 51 2 65 3 45 2 skilled B. Skilled 19 28 7 39 26 23 44 7 48 - - 7 48 12 C. Low skilled 5 37 4 48 9 30 43 2 80 2 100 4 63 18 Total 30 22 12 27 42 17 23 10 38 3 60 13 32 7

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 34 11 16 15 50 9 47 68,354 B. Skilled 7 19 5 35 11 18 20 206,055 C. Low skilled 2 36 - 86 2 34 10 105,519 Total 42 9 21 14 64 8 34 379,928 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

78 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2008 Outflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 42 16 13 17 55 13 5 26 6 33 12 22 11 skilled B. Skilled 14 21 12 39 26 21 2 56 3 45 5 35 8 C. Low skilled 3 43 1 37 3 37 3 86 1 100 4 69 17 Total 59 13 25 20 84 11 10 29 10 26 20 20 11

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly 5 51 1 96 6 45 6 - 100 - - - 100 - skilled B. Skilled 6 37 7 78 14 45 23 ------C. Low skilled 8 41 2 81 9 36 43 ------Total 19 24 10 58 29 26 16 - 100 - - - 100 -

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 16 9 12 14 29 8 28 68,354 B. Skilled 6 16 7 15 13 11 23 206,055 C. Low skilled 4 30 1 29 6 24 26 105,519 Total 27 8 21 10 48 6 26 379,928 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

79 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2009 Inflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 14 17 9 20 22 13 13 28 8 27 21 20 25 skilled B. Skilled 11 25 6 30 17 19 5 26 2 33 7 21 12 C. Low skilled 1 58 1 63 2 43 3 41 1 51 4 34 20 Total 25 14 16 16 41 11 21 19 11 21 32 14 20

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly 2 38 2 28 4 23 5 1 51 1 60 2 39 2 skilled B. Skilled 15 26 4 23 20 21 33 1 48 2 38 2 30 4 C. Low skilled 4 30 7 51 11 35 59 1 60 1 83 2 54 9 Total 21 20 14 28 34 16 21 2 30 3 33 5 23 3

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 23 12 13 15 36 9 42 56,688 B. Skilled 9 17 4 30 13 15 22 140,519 C. Low skilled - 59 - - - 59 2 56,156 Total 33 10 17 13 50 8 31 253,363 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

80 Table D2 Flows of workers by main category of employment, 2004 - 2009 (continued) 2009 Outflow 1. Nationals 2. (Other) EU15 Nationals Main Male Female Total Male Female Total % of categorisation Est SE% Est SE% Est SE% Est SE% Est SE% Est SE% Total A. Highly 25 10 11 12 37 8 16 28 4 25 21 23 22 skilled B. Skilled 14 17 7 32 21 16 3 51 3 31 5 30 10 C. Low skilled 1 34 1 43 2 27 1 37 1 100 3 49 15 Total 40 8 19 14 59 7 20 24 8 22 28 18 18

3. (Other) EU10 Nationals 4. (Other) EU2 Nationals Main Male Female Total % of Male Female Total % of categorisation Est SE% Est SE% Est SE% Total Est SE% Est SE% Est SE% Total A. Highly 1 51 1 52 2 39 2 - - - 100 - 100 - skilled B. Skilled 10 29 1 39 11 26 22 - 71 1 59 1 45 2 C. Low skilled 6 47 2 39 7 38 43 ------Total 17 23 3 25 21 20 13 - 71 1 51 1 41 1

5. Third-Country Nationals Male Female Total % of 6. No. of unfilled Main categorisation Est SE% Est SE% Est SE% Total vacanciesa A. Highly skilled 21 9 12 11 33 7 36 56,688 B. Skilled 7 14 6 16 13 11 26 140,519 C. Low skilled 3 27 2 25 5 19 29 56,156 Total 32 7 19 9 51 5 32 253,363 Source: Migration Statistics Unit, Office for National Statistics Notes: a Data relate to stock of June of each year. No data available for Researchers and for Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %.

81 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 2004 Country of Total Nationality Male Female Total Nationals 14,538,423 12,355,944 26,894,367 (Other) EU15 265,300 258,625 523,925 (Other) EU10 43,007 41,774 84,781 (Other) EU2 5,071 7,216 12,287 India 46,937 30,225 77,162 Australia 30,356 29,893 60,249 South Africa 30,122 26,293 56,415 United States 33,573 22,701 56,274 Philippines 13,443 17,686 31,129 Columbia 17,034 12,336 29,370 Zimbabwe 13,845 14,256 28,101 New Zealand 15,174 11,273 26,446 Pakistan 23,097 2,786 25,883 Iran 16,674 9,058 25,731 Other 277,023 211,343 488,366 Total 15,369,076 13,051,407 28,420,484

Main Categorisations Country of A. Highly Skilled Nationality Male Female Total Nationals 6,485,699 4,427,938 10,913,637 (Other) EU15 143,910 124,486 268,396 (Other) EU10 7,881 9,307 17,188 (Other) EU2 1,280 1,798 3,078 India 25,545 11,922 37,467 Australia 20,768 19,458 40,226 South Africa 15,910 13,686 29,596 United States 24,732 15,553 40,285 Philippines 4,583 11,447 16,029 Columbia 8,860 4,900 13,760 Zimbabwe 4,950 6,398 11,349 New Zealand 11,039 8,221 19,260 Pakistan 6,816 764 7,579 Iran 8,908 6,237 15,145 Other 107,036 89,175 196,211 Total 6,877,917 4,751,287 11,629,204 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

82 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2004 Country of B. Skilled Nationality Male Female Total Nationals 6,349,508 6,536,290 12,885,797 (Other) EU15 87,602 104,095 191,697 (Other) EU10 24,876 19,918 44,794 (Other) EU2 3,791 2,608 6,399 India 14,998 13,244 28,242 Australia 7,517 9,810 17,327 South Africa 9,509 10,834 20,344 United States 7,194 6,679 13,874 Philippines 5,380 5,054 10,435 Columbia 5,514 4,508 10,022 Zimbabwe 6,242 7,309 13,551 New Zealand 3,843 2,886 6,729 Pakistan 10,964 1,604 12,568 Iran 5,980 2,416 8,396 Other 100,755 92,142 192,898 Total 6,643,671 6,819,399 13,463,070

Country of C. Low Skilled Nationality Male Female Total Nationals 1,703,217 1,391,716 3,094,933 (Other) EU15 33,788 30,044 63,832 (Other) EU10 10,250 12,549 22,799 (Other) EU2 0 2,811 2,811 India 6,394 5,059 11,452 Australia 2,071 625 2,696 South Africa 4,702 1,774 6,476 United States 1,646 469 2,115 Philippines 3,480 1,186 4,665 Columbia 2,661 2,928 5,588 Zimbabwe 2,653 549 3,202 New Zealand 292 166 458 Pakistan 5,318 418 5,736 Iran 1,786 404 2,190 Other 69,232 30,026 99,257 Total 1,847,488 1,480,721 3,328,209 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

83 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2004 Country of D. Researchers Nationality Male Female Total Nationals 110,464 72,934 183,398 (Other) EU15 5,095 9,532 14,627 (Other) EU10 268 133 401 (Other) EU2 0 0 0 India 596 353 949 Australia 443 565 1,008 South Africa 0 157 157 United States 1,779 640 2,418 Philippines 0 0 0 Columbia 274 323 597 Zimbabwe 0 0 0 New Zealand 486 362 847 Pakistan 0 0 0 Iran 646 260 906 Other 2,702 3,265 5,968 Total 122,752 88,524 211,276 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

84 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2005 Country of Total Nationality Male Female Total Nationals 13,518,532 11,547,075 25,065,606 (Other) EU15 264,957 255,470 520,426 (Other) EU10 91,176 74,789 165,966 (Other) EU2 8,970 11,243 20,213 India 70,277 48,035 118,311 Australia 35,728 36,238 71,965 South Africa 36,671 33,206 69,877 United States 30,069 34,941 65,010 Philippines 14,579 28,284 42,863 Columbia 16,911 20,543 37,454 Zimbabwe 17,397 16,772 34,169 New Zealand 29,857 3,048 32,906 Pakistan 14,235 15,348 29,583 Iran 16,900 11,133 28,033 Other 246,048 163,624 409,672 Total 14,412,307 12,299,745 26,712,052

Main Categorisations Country of A. Highly Skilled Nationality Male Female Total Nationals 6,061,907 4,253,119 10,315,026 (Other) EU15 151,949 130,895 282,843 (Other) EU10 10,150 11,822 21,971 (Other) EU2 1,404 1,687 3,090 India 37,225 20,655 57,880 Australia 18,107 18,904 37,011 South Africa 29,775 22,992 52,767 United States 24,237 22,856 47,093 Philippines 5,100 16,003 21,102 Columbia 6,346 7,762 14,108 Zimbabwe 9,732 13,166 22,898 New Zealand 9,505 511 10,016 Pakistan 4,763 3,869 8,632 Iran 7,429 3,086 10,514 Other 89,883 69,804 159,686 Total 6,467,507 4,597,129 11,064,636 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

85 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2005 Country of B. Skilled Nationality Male Female Total Nationals 5,919,483 6,033,047 11,952,529 (Other) EU15 79,052 94,481 173,533 (Other) EU10 49,163 34,670 83,833 (Other) EU2 5,683 6,287 11,970 India 22,909 20,268 43,177 Australia 11,420 12,745 24,165 South Africa 3,687 9,802 13,489 United States 4,798 11,137 15,935 Philippines 6,467 10,216 16,682 Columbia 7,669 12,395 20,065 Zimbabwe 7,515 3,606 11,121 New Zealand 14,249 2,328 16,577 Pakistan 4,670 7,881 12,550 Iran 4,328 6,875 11,203 Other 100,303 65,749 166,052 Total 6,241,394 6,331,484 12,572,878

Country of C. Low Skilled Nationality Male Female Total Nationals 1,537,143 1,260,909 2,798,051 (Other) EU15 33,957 30,094 64,050 (Other) EU10 31,864 28,298 60,162 (Other) EU2 1,884 3,269 5,153 India 10,143 7,112 17,255 Australia 6,201 4,589 10,790 South Africa 3,210 411 3,621 United States 1,034 948 1,982 Philippines 3,013 2,065 5,079 Columbia 2,896 386 3,282 Zimbabwe 151 0 151 New Zealand 6,104 209 6,313 Pakistan 4,802 3,598 8,400 Iran 5,143 1,173 6,315 Other 55,863 28,071 83,934 Total 1,703,406 1,371,132 3,074,537 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

86 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2005 Country of D. Researchers Nationality Male Female Total Nationals 98,706 71,795 170,501 (Other) EU15 6,409 6,437 12,845 (Other) EU10 479 377 856 (Other) EU2 0 0 0 India 822 701 1,523 Australia 0 0 0 South Africa 971 416 1,387 United States 136 947 1,082 Philippines 0 0 0 Columbia 348 0 348 Zimbabwe 442 576 1,018 New Zealand 0 0 0 Pakistan 375 474 848 Iran 215 0 215 Other 3,003 2,889 5,893 Total 111,903 84,610 196,513 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

87 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2006 Country of Total Nationality Male Female Total Nationals 14,537,979 12,501,623 27,039,602 (Other) EU15 267,168 247,604 514,772 (Other) EU10 169,908 120,015 289,923 (Other) EU2 16,485 9,404 25,889 India 93,836 59,590 153,426 Australia 44,764 39,279 84,043 South Africa 43,116 38,949 82,065 United States 38,288 35,726 74,014 Philippines 18,926 28,011 46,937 Columbia 17,521 26,224 43,745 Zimbabwe 25,827 14,770 40,596 New Zealand 22,738 16,332 39,070 Pakistan 34,421 3,253 37,674 Iran 14,841 14,066 28,907 Other 270,306 175,861 446,167 Total 15,616,122 13,330,705 28,946,827

Main Categorisations Country of A. Highly Skilled Nationality Male Female Total Nationals 6,620,230 4,778,377 11,398,606 (Other) EU15 141,318 128,916 270,233 (Other) EU10 21,516 16,616 38,132 (Other) EU2 4,125 1,949 6,074 India 51,275 31,002 82,277 Australia 24,355 23,284 47,639 South Africa 36,900 29,083 65,983 United States 29,704 22,840 52,544 Philippines 5,332 10,697 16,029 Columbia 7,734 9,213 16,947 Zimbabwe 9,663 4,915 14,578 New Zealand 7,451 3,663 11,113 Pakistan 6,795 1,671 8,466 Iran 9,777 10,981 20,758 Other 104,264 68,747 173,011 Total 7,080,437 5,141,951 12,222,388 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

88 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2006 Country of B. Skilled Nationality Male Female Total Nationals 6,295,729 6,362,821 12,658,550 (Other) EU15 87,347 89,781 177,128 (Other) EU10 86,530 52,437 138,967 (Other) EU2 9,392 4,450 13,842 India 30,306 20,914 51,219 Australia 16,983 14,465 31,448 South Africa 4,746 8,943 13,688 United States 6,484 11,095 17,579 Philippines 10,857 13,185 24,042 Columbia 6,229 15,633 21,861 Zimbabwe 6,497 7,959 14,456 New Zealand 6,803 9,090 15,892 Pakistan 18,988 1,374 20,362 Iran 4,449 2,876 7,326 Other 111,058 79,705 190,763 Total 6,702,396 6,694,724 13,397,120

Country of C. Low Skilled Nationality Male Female Total Nationals 1,622,021 1,360,426 2,982,446 (Other) EU15 38,503 28,908 67,410 (Other) EU10 61,862 50,962 112,824 (Other) EU2 2,969 3,005 5,974 India 12,256 7,674 19,930 Australia 3,426 1,530 4,957 South Africa 1,471 923 2,394 United States 2,100 1,791 3,892 Philippines 2,737 4,129 6,865 Columbia 3,558 1,379 4,937 Zimbabwe 9,667 1,897 11,563 New Zealand 8,485 3,580 12,065 Pakistan 8,637 209 8,846 Iran 615 210 824 Other 54,985 27,409 82,393 Total 1,833,290 1,494,029 3,327,319 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

89 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2006 Country of D. Researchers Nationality Male Female Total Nationals 99,048 76,978 176,027 (Other) EU15 9,886 4,557 14,443 (Other) EU10 0 272 272 (Other) EU2 171 0 171 India 1,271 955 2,225 Australia 0 0 0 South Africa 755 544 1,299 United States 462 1,395 1,857 Philippines 0 0 0 Columbia 0 291 291 Zimbabwe 920 0 920 New Zealand 0 328 328 Pakistan 0 0 0 Iran 0 343 343 Other 4,795 1,854 6,648 Total 117,308 87,515 204,823 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

90 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2007 Country of Total Nationality Male Female Total Nationals 14,523,243 12,472,322 26,995,565 (Other) EU15 297,782 256,303 554,085 (Other) EU10 256,788 172,255 429,043 (Other) EU2 15,257 12,203 27,460 India 105,669 54,065 159,734 Australia 42,046 35,293 77,339 South Africa 33,967 31,502 65,468 United States 31,185 25,711 56,896 Philippines 47,995 6,073 54,068 Columbia 34,197 18,780 52,977 Zimbabwe 21,102 26,871 47,973 New Zealand 15,650 21,277 36,927 Pakistan 21,117 13,952 35,069 Iran 18,838 12,980 31,819 Other 307,528 214,863 522,391 Total 15,772,362 13,374,449 29,146,811

Main Categorisations Country of A. Highly Skilled Nationality Male Female Total Nationals 6,623,091 4,843,704 11,466,795 (Other) EU15 177,703 135,542 313,245 (Other) EU10 21,285 27,249 48,534 (Other) EU2 4,382 3,947 8,329 India 57,626 29,598 87,224 Australia 34,625 23,767 58,392 South Africa 22,560 17,367 39,927 United States 23,555 19,288 42,844 Philippines 8,286 2,086 10,371 Columbia 14,137 7,819 21,956 Zimbabwe 6,064 13,920 19,984 New Zealand 7,148 7,810 14,958 Pakistan 5,528 3,675 9,203 Iran 12,729 7,943 20,672 Other 126,422 82,789 209,211 Total 7,145,140 5,226,502 12,371,642 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

91 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2007 Country of B. Skilled Nationality Male Female Total Nationals 6,251,151 6,305,363 12,556,513 (Other) EU15 86,583 91,115 177,698 (Other) EU10 149,451 76,702 226,153 (Other) EU2 8,245 4,757 13,002 India 31,419 17,785 49,204 Australia 7,113 9,720 16,833 South Africa 7,114 13,369 20,483 United States 6,114 6,141 12,255 Philippines 27,318 3,662 30,979 Columbia 11,268 8,536 19,804 Zimbabwe 11,036 10,010 21,046 New Zealand 6,751 11,845 18,597 Pakistan 6,298 7,301 13,599 Iran 5,398 4,637 10,035 Other 115,974 92,183 208,157 Total 6,731,231 6,663,125 13,394,356

Country of C. Low Skilled Nationality Male Female Total Nationals 1,649,001 1,323,256 2,972,257 (Other) EU15 33,497 29,646 63,143 (Other) EU10 86,052 68,304 154,356 (Other) EU2 2,630 3,499 6,129 India 16,624 6,682 23,307 Australia 309 1,805 2,114 South Africa 4,292 766 5,058 United States 1,516 282 1,797 Philippines 12,392 326 12,717 Columbia 8,793 2,425 11,218 Zimbabwe 4,001 2,942 6,943 New Zealand 1,750 1,622 3,373 Pakistan 9,292 2,976 12,268 Iran 711 401 1,112 Other 65,133 39,890 105,023 Total 1,895,991 1,484,823 3,380,814 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

92 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2007 Country of D. Researchers Nationality Male Female Total Nationals 99,482 78,749 178,231 (Other) EU15 7,120 8,523 15,643 (Other) EU10 0 591 591 (Other) EU2 0 0 0 India 1,293 368 1,661 Australia 472 1,122 1,593 South Africa 543 188 731 United States 385 427 812 Philippines 0 0 0 Columbia 1,022 0 1,022 Zimbabwe 0 0 0 New Zealand 0 338 338 Pakistan 361 154 514 Iran 152 376 528 Other 6,114 2,577 8,691 Total 116,943 93,411 210,353 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

93 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2008 Country of Total Nationality Male Female Total Nationals 14,505,304 12,510,066 27,015,369 (Other) EU15 304,397 256,593 560,990 (Other) EU10 279,316 220,704 500,020 (Other) EU2 26,989 16,650 43,639 India 111,245 59,939 171,184 Australia 38,995 38,513 77,508 South Africa 37,378 33,265 70,643 United States 33,032 34,481 67,513 Philippines 59,698 7,664 67,362 Columbia 32,703 19,717 52,420 Zimbabwe 20,886 27,454 48,341 New Zealand 16,978 23,199 40,177 Pakistan 22,488 17,206 39,693 Iran 20,344 15,096 35,439 Other 311,782 214,965 526,747 Total 15,821,533 13,495,511 29,317,043

Main Categorisations Country of A. Highly Skilled Nationality Male Female Total Nationals 6,731,102 4,978,371 11,709,473 (Other) EU15 181,308 140,693 322,001 (Other) EU10 32,781 29,369 62,151 (Other) EU2 1,216 3,479 4,694 India 60,666 31,193 91,859 Australia 27,157 20,944 48,101 South Africa 27,460 21,252 48,713 United States 25,365 20,699 46,063 Philippines 15,295 2,335 17,629 Columbia 15,032 8,138 23,169 Zimbabwe 5,146 9,120 14,266 New Zealand 8,262 9,789 18,051 Pakistan 11,061 6,728 17,789 Iran 7,064 3,482 10,546 Other 121,641 84,368 206,009 Total 7,270,552 5,369,959 12,640,511 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

94 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2008 Country of B. Skilled Nationality Male Female Total Nationals 6,126,570 6,232,074 12,358,643 (Other) EU15 85,701 89,220 174,921 (Other) EU10 160,819 101,835 262,654 (Other) EU2 19,129 8,524 27,653 India 37,943 21,420 59,363 Australia 7,901 15,885 23,786 South Africa 9,334 9,914 19,248 United States 5,765 12,706 18,471 Philippines 30,193 3,710 33,903 Columbia 10,129 9,864 19,993 Zimbabwe 11,384 15,961 27,344 New Zealand 7,222 12,830 20,052 Pakistan 8,381 7,019 15,400 Iran 5,788 6,764 12,552 Other 116,215 90,522 206,737 Total 6,642,473 6,638,246 13,280,719

Country of C. Low Skilled Nationality Male Female Total Nationals 1,647,632 1,299,621 2,947,253 (Other) EU15 37,388 26,679 64,068 (Other) EU10 85,716 89,500 175,215 (Other) EU2 6,644 4,648 11,292 India 12,636 7,326 19,962 Australia 3,938 1,684 5,622 South Africa 584 2,099 2,682 United States 1,903 1,077 2,979 Philippines 14,211 1,620 15,830 Columbia 7,542 1,716 9,258 Zimbabwe 4,357 2,374 6,731 New Zealand 1,494 580 2,075 Pakistan 3,046 3,459 6,505 Iran 7,492 4,850 12,341 Other 73,927 40,075 114,001 Total 1,908,507 1,487,306 3,395,813 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

95 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2008 Country of D. Researchers Nationality Male Female Total Nationals 109,348 82,785 192,133 (Other) EU15 4,838 5,993 10,832 (Other) EU10 170 572 741 (Other) EU2 0 284 284 India 1,077 119 1,196 Australia 198 176 373 South Africa 461 1,310 1,771 United States 2,148 996 3,143 Philippines 0 136 136 Columbia 369 0 369 Zimbabwe 0 0 0 New Zealand 333 102 435 Pakistan 2,318 196 2,514 Iran 757 637 1,395 Other 1,106 1,378 2,483 Total 123,121 94,682 217,802 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

96 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2009 Country of Total Nationality Male Female Total Nationals 14,151,347 12,427,580 26,578,927 (Other) EU15 291,182 268,973 560,155 (Other) EU10 271,978 227,809 499,787 (Other) EU2 38,746 21,797 60,543 India 102,940 53,936 156,875 Australia 42,684 38,195 80,879 South Africa 31,644 34,716 66,359 United States 51,038 11,949 62,987 Philippines 32,721 28,551 61,271 Columbia 22,035 36,124 58,159 Zimbabwe 30,105 23,077 53,182 New Zealand 19,827 20,586 40,414 Pakistan 17,161 16,990 34,151 Iran 19,413 14,251 33,664 Other 306,004 209,793 515,796 Total 15,428,823 13,434,325 28,863,148

Main Categorisations Country of A. Highly Skilled Nationality Male Female Total Nationals 6,702,082 4,998,508 11,700,590 (Other) EU15 167,171 143,732 310,903 (Other) EU10 35,251 36,201 71,452 (Other) EU2 3,549 4,092 7,641 India 64,629 29,651 94,280 Australia 27,084 21,651 48,735 South Africa 23,781 22,196 45,976 United States 14,720 4,423 19,143 Philippines 25,865 19,436 45,301 Columbia 5,418 14,239 19,656 Zimbabwe 12,457 8,498 20,955 New Zealand 8,869 6,764 15,633 Pakistan 5,022 7,815 12,837 Iran 13,768 9,101 22,869 Other 128,745 74,305 203,050 Total 7,238,409 5,400,610 12,639,018 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

97 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2009 Country of B. Skilled Nationality Male Female Total Nationals 5,920,426 6,153,143 12,073,569 (Other) EU15 89,332 96,155 185,487 (Other) EU10 154,687 96,317 251,003 (Other) EU2 25,456 9,181 34,637 India 29,697 20,263 49,960 Australia 10,582 14,352 24,934 South Africa 6,603 9,881 16,484 United States 22,762 5,363 28,125 Philippines 5,813 6,634 12,447 Columbia 12,041 17,925 29,966 Zimbabwe 12,120 12,916 25,036 New Zealand 8,861 13,504 22,365 Pakistan 9,429 5,516 14,945 Iran 5,194 4,984 10,178 Other 110,724 94,086 204,810 Total 6,423,727 6,560,218 12,983,945

Country of C. Low Skilled Nationality Male Female Total Nationals 1,528,839 1,275,930 2,804,768 (Other) EU15 34,679 29,086 63,765 (Other) EU10 82,041 95,291 177,332 (Other) EU2 9,742 8,524 18,266 India 8,613 4,023 12,636 Australia 5,018 2,192 7,210 South Africa 1,260 2,639 3,900 United States 13,556 2,163 15,719 Philippines 1,042 2,481 3,523 Columbia 4,577 3,961 8,537 Zimbabwe 5,528 1,663 7,191 New Zealand 2,098 318 2,416 Pakistan 2,710 3,660 6,370 Iran 451 166 617 Other 66,535 41,402 107,937 Total 1,766,688 1,473,497 3,240,185 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

98 Table D3 Stock of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2009 Country of D. Researchers Nationality Male Female Total Nationals 111,105 80,507 191,612 (Other) EU15 7,765 7,477 15,241 (Other) EU10 736 1,389 2,125 (Other) EU2 0 732 732 India 825 862 1,686 Australia 0 127 127 South Africa 160 2,722 2,882 United States 316 567 883 Philippines 665 1,364 2,029 Columbia 157 427 583 Zimbabwe 0 0 0 New Zealand 249 486 734 Pakistan 1,236 176 1,412 Iran 0 0 0 Other 3,448 5,236 8,684 Total 126,661 102,069 228,730 Source: Labour Force Survey. Notes: No data available for Seasonal workers.

99 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 2004 Inflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 25 13 10 22 35 11 (Other) EU15 13 22 11 40 24 22 (Other) EU10 17 31 10 32 27 23 (Other) EU2 1 71 1 69 2 50 India 8 23 8 37 16 22 South Africa 9 28 5 32 14 21 Australia 7 42 6 17 13 24 Philippines 2 52 6 31 9 27 China (exc. Taiwan) 6 90 - 71 7 84 United States of America (USA) 2 31 4 64 7 43 New Zealand 3 47 2 48 5 34 Canada 1 47 2 63 3 51 United Arab Emirates 2 71 - - 2 71 Zimbabwe - - 2 71 2 71 Other 17 14 8 29 25 14

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 18 16 6 21 24 13 (Other) EU15 10 26 7 46 16 24 (Other) EU10 2 62 3 53 6 40 (Other) EU2 1 100 - 100 1 71 India 5 20 7 41 12 25 South Africa 6 35 3 38 10 26 Australia 5 56 3 21 9 36 Philippines 1 87 5 36 6 33 China (exc. Taiwan) 6 96 - 71 6 88 United States of America (USA) 2 34 4 65 6 44 New Zealand 1 30 1 35 2 24 Canada 1 51 1 93 1 63 United Arab Emirates 1 100 - - 1 100 Zimbabwe ------Other 9 17 6 35 16 18 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 100 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2004 Inflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 5 25 4 47 9 24 (Other) EU15 2 44 4 72 7 49 (Other) EU10 11 41 4 50 16 32 (Other) EU2 - - 1 95 1 95 India 3 60 1 75 3 48 South Africa 2 55 2 64 4 43 Australia 2 37 3 28 5 22 Philippines 1 68 1 41 2 42 China (exc. Taiwan) - 65 - - - 65 United States of America (USA) - 58 - 100 - 52 New Zealand 2 83 1 55 2 64 Canada - 100 1 86 1 82 United Arab Emirates 2 97 - - 2 97 Zimbabwe - - 2 71 2 71 Other 5 28 2 49 7 24

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 2 51 - 72 2 48 (Other) EU15 1 63 - - 1 63 (Other) EU10 3 69 3 68 6 49 (Other) EU2 1 100 - - 1 100 India - 53 - - - 53 South Africa 1 85 - 75 1 68 Australia - 100 - 71 - 59 Philippines - 71 - 100 - 59 China (exc. Taiwan) ------United States of America (USA) ------New Zealand - - 1 100 1 100 Canada ------United Arab Emirates ------Zimbabwe ------Other 3 46 - 100 3 45 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 101 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2004 Outflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 37 11 20 16 57 9 (Other) EU15 6 30 4 42 10 25 (Other) EU10 ------(Other) EU2 ------India 6 20 6 20 12 14 South Africa 5 48 1 62 6 41 Australia 2 37 3 30 5 23 Philippines 2 38 2 42 4 28 China (exc. Taiwan) 1 60 2 87 2 60 United States of America (USA) 1 58 1 61 2 46 New Zealand 2 58 - - 2 58 Canada 1 51 - 71 2 42 United Arab Emirates - 100 - 100 1 71 Zimbabwe 1 60 - - 1 60 Other 3 34 1 45 4 27

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 24 13 13 20 37 11 (Other) EU15 5 35 4 45 8 28 (Other) EU10 ------(Other) EU2 ------India 4 24 3 26 7 18 South Africa 4 53 - 100 5 49 Australia 1 60 1 41 2 34 Philippines 1 60 1 58 2 42 China (exc. Taiwan) 1 73 1 100 2 71 United States of America (USA) - 71 1 58 1 45 New Zealand 1 86 - - 1 86 Canada 1 51 - 71 2 42 United Arab Emirates - 100 - - - 100 Zimbabwe 1 74 - - 1 74 Other 1 46 1 71 2 39 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 102 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2004 Outflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 11 19 8 28 18 16 (Other) EU15 1 71 - 100 1 58 (Other) EU10 ------(Other) EU2 ------India 2 38 3 33 5 25 South Africa - 71 1 78 1 57 Australia 1 51 1 54 2 37 Philippines - 71 1 60 1 47 China (exc. Taiwan) - 100 - 100 - 71 United States of America (USA) ------New Zealand 1 61 - - 1 61 Canada ------United Arab Emirates ------Zimbabwe - 100 - - - 100 Other 1 51 - 100 1 46

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 1 64 - - 1 64 (Other) EU15 - 100 - - - 100 (Other) EU10 ------(Other) EU2 ------India - 100 - 100 - 71 South Africa ------Australia - 100 1 71 1 59 Philippines 1 62 - - 1 62 China (exc. Taiwan) ------United States of America (USA) - 100 1 100 1 82 New Zealand ------Canada ------United Arab Emirates - - - 100 - 100 Zimbabwe ------Other - - 1 71 1 71 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 103 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2005 Inflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 29 18 17 17 46 13 (Other) EU15 14 26 9 31 23 20 (Other) EU10 37 19 12 30 49 16 (Other) EU2 1 61 - - 1 61 India 11 14 5 28 15 13 South Africa 7 24 4 20 10 17 Australia 5 29 6 30 10 21 Philippines 1 31 6 39 7 34 China (exc. Taiwan) 3 24 3 39 5 22 United States of America (USA) 3 51 2 31 4 33 New Zealand 3 100 - 100 4 98 Canada 3 58 - 51 4 52 United Arab Emirates 1 48 2 62 3 46 Zimbabwe 2 78 - 86 2 69 Other 12 15 6 22 18 12

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 18 20 9 22 26 15 (Other) EU15 8 37 7 36 14 26 (Other) EU10 7 36 4 47 11 29 (Other) EU2 1 61 - - 1 61 India 8 15 3 25 11 13 South Africa 5 31 3 23 8 21 Australia 3 38 2 53 5 31 Philippines - 45 4 52 5 48 China (exc. Taiwan) 2 26 2 41 5 24 United States of America (USA) 1 34 1 36 2 27 New Zealand - - - 100 - 100 Canada 3 61 - 72 3 59 United Arab Emirates 1 53 1 52 2 37 Zimbabwe - 63 - - - 63 Other 7 19 4 29 11 16 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 104 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2005 Inflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 8 35 7 30 15 23 (Other) EU15 5 38 3 57 8 32 (Other) EU10 20 26 3 58 23 24 (Other) EU2 ------India 3 36 2 61 4 33 South Africa 1 37 1 34 2 26 Australia 1 48 3 38 4 31 Philippines 1 42 1 34 2 27 China (exc. Taiwan) - 58 - 75 1 48 United States of America (USA) 2 71 - 48 2 62 New Zealand 3 100 - - 3 100 Canada - 60 - 63 - 45 United Arab Emirates - 100 1 91 1 88 Zimbabwe - 100 - - - 100 Other 4 31 2 31 6 24

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 4 80 2 59 5 58 (Other) EU15 1 94 - - 1 94 (Other) EU10 10 43 5 48 15 33 (Other) EU2 ------India - 58 - 100 - 51 South Africa - 49 - 100 1 45 Australia - 59 - 90 1 52 Philippines - - - 62 - 62 China (exc. Taiwan) ------United States of America (USA) - 100 - - - 100 New Zealand ------Canada ------United Arab Emirates - 100 - - - 100 Zimbabwe 1 100 - 86 2 85 Other 1 28 - 100 1 27 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 105 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2005 Outflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 50 11 16 16 65 9 (Other) EU15 5 41 7 27 13 23 (Other) EU10 1 100 3 94 4 71 (Other) EU2 ------India 6 20 7 20 14 14 South Africa 5 32 3 31 8 23 Australia 3 32 2 31 5 22 Philippines 4 27 - 100 4 26 China (exc. Taiwan) 1 51 1 81 2 44 United States of America (USA) 1 60 1 46 2 37 New Zealand 1 100 1 50 2 57 Canada 1 100 - 71 2 79 United Arab Emirates 2 100 - - 2 100 Zimbabwe - 100 1 100 1 78 Other 4 28 2 36 6 22

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 35 13 13 18 48 10 (Other) EU15 2 63 5 33 7 30 (Other) EU10 - - - 100 - 100 (Other) EU2 ------India 4 25 5 23 10 17 South Africa 2 42 1 52 3 33 Australia 2 37 1 42 4 28 Philippines 3 32 - 100 3 30 China (exc. Taiwan) 1 58 1 81 2 49 United States of America (USA) 1 60 1 52 2 40 New Zealand 1 100 1 58 2 64 Canada 1 100 - - 1 100 United Arab Emirates 2 100 - - 2 100 Zimbabwe - 100 1 100 1 78 Other 2 35 1 51 3 29 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 106 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2005 Outflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 14 21 2 36 16 19 (Other) EU15 1 84 1 65 2 54 (Other) EU10 - - 2 100 2 100 (Other) EU2 ------India 2 35 2 40 4 27 South Africa 1 52 2 39 3 31 Australia 1 58 1 45 2 35 Philippines 1 73 - - 1 73 China (exc. Taiwan) ------United States of America (USA) ------New Zealand - - - 100 - 100 Canada - - - 100 - 100 United Arab Emirates ------Zimbabwe ------Other 1 54 1 59 2 40

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 1 46 1 100 2 44 (Other) EU15 2 69 1 71 3 51 (Other) EU10 1 100 - - 1 100 (Other) EU2 ------India - 100 - 100 - 71 South Africa 1 72 - - 1 72 Australia ------Philippines - 71 - - - 71 China (exc. Taiwan) - 100 - - - 100 United States of America (USA) - - - 100 - 100 New Zealand ------Canada - - - 100 - 100 United Arab Emirates ------Zimbabwe ------Other - 100 - 100 1 71 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 107 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2006 Inflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 14 16 11 21 25 13 (Other) EU15 14 22 4 33 19 19 (Other) EU10 28 21 20 27 48 17 (Other) EU2 2 97 2 50 4 49 India 12 11 5 32 18 13 South Africa 5 21 8 22 14 16 Australia 7 30 3 40 9 24 Philippines 2 24 5 47 8 34 China (exc. Taiwan) 1 33 6 54 7 46 United States of America (USA) 4 45 1 100 6 42 New Zealand 3 37 3 32 6 25 Canada 2 65 1 48 3 50 United Arab Emirates 2 46 - 46 3 40 Zimbabwe 2 34 - 53 2 32 Other 10 19 6 26 16 15

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 7 16 9 22 16 14 (Other) EU15 8 30 3 39 11 24 (Other) EU10 5 48 2 66 7 40 (Other) EU2 - 100 1 71 1 68 India 9 13 4 38 13 14 South Africa 3 31 6 23 8 19 Australia 4 42 2 47 6 32 Philippines 1 34 5 54 6 43 China (exc. Taiwan) 1 45 1 60 2 43 United States of America (USA) 1 32 1 100 2 75 New Zealand 3 40 2 35 5 27 Canada - 63 1 54 1 41 United Arab Emirates 2 62 - 81 2 56 Zimbabwe 1 42 - 62 2 39 Other 7 24 5 32 12 19 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 108 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2006 Inflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 6 30 3 56 9 27 (Other) EU15 4 42 1 64 6 36 (Other) EU10 16 29 13 35 29 23 (Other) EU2 - - 1 68 1 68 India 2 26 2 57 4 29 South Africa 2 33 1 33 3 24 Australia 2 38 - 42 2 33 Philippines 1 34 1 35 2 25 China (exc. Taiwan) - 51 4 70 5 65 United States of America (USA) 3 58 - - 3 58 New Zealand - 54 - 54 - 38 Canada 1 87 - 100 1 82 United Arab Emirates 1 43 - 51 1 35 Zimbabwe 1 55 - 100 1 52 Other 2 32 1 41 3 25

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals - 75 - 100 1 62 (Other) EU15 2 53 - - 2 53 (Other) EU10 7 39 5 52 12 31 (Other) EU2 1 100 - - 1 100 India 1 50 - - 1 50 South Africa - 65 2 80 2 65 Australia 1 100 - 100 1 94 Philippines - 100 - - - 100 China (exc. Taiwan) - 100 - 100 - 79 United States of America (USA) 1 100 - - 1 100 New Zealand ------Canada - 100 - - - 100 United Arab Emirates ------Zimbabwe ------Other - 44 - 100 1 40 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 109 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2006 Outflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 53 11 25 14 78 9 (Other) EU15 7 32 2 46 9 27 (Other) EU10 10 40 2 60 11 36 (Other) EU2 - - 2 100 2 100 India 5 26 7 22 12 17 South Africa 6 31 5 30 10 22 Australia 4 30 2 36 6 23 Philippines 4 32 1 52 5 27 China (exc. Taiwan) 3 35 1 58 4 30 United States of America (USA) 2 46 - - 2 46 New Zealand 1 71 1 100 1 61 Canada - 100 1 71 1 60 United Arab Emirates 1 100 - - 1 100 Zimbabwe 1 50 - - 1 50 Other 6 28 2 51 7 25

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 34 13 18 17 52 10 (Other) EU15 5 41 2 51 7 33 (Other) EU10 1 100 - 100 1 77 (Other) EU2 ------India 3 35 4 30 7 23 South Africa 2 47 2 52 3 35 Australia 2 42 2 42 4 30 Philippines 3 35 1 74 4 32 China (exc. Taiwan) 3 39 1 71 3 35 United States of America (USA) 1 52 - - 1 52 New Zealand - - 1 100 1 100 Canada - 100 - - - 100 United Arab Emirates 1 100 - - 1 100 Zimbabwe - 100 - - - 100 Other 5 31 - 100 5 30 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 110 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2006 Outflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 15 18 6 29 21 16 (Other) EU15 2 51 - 100 3 46 (Other) EU10 3 72 1 72 5 56 (Other) EU2 - - 2 100 2 100 India 2 39 2 35 5 26 South Africa 4 39 3 39 7 28 Australia 2 43 - 100 2 40 Philippines 1 100 1 71 1 61 China (exc. Taiwan) - 100 - 100 1 72 United States of America (USA) - 100 - - - 100 New Zealand 1 71 - - 1 71 Canada ------United Arab Emirates ------Zimbabwe - 71 - - - 71 Other - 71 1 100 1 63

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 4 75 1 63 5 61 (Other) EU15 ------(Other) EU10 5 54 - - 5 54 (Other) EU2 ------India - 100 - 100 1 72 South Africa - - - 100 - 100 Australia - - - 100 - 100 Philippines - 100 - - - 100 China (exc. Taiwan) - 100 - - - 100 United States of America (USA) ------New Zealand ------Canada - - 1 71 1 71 United Arab Emirates ------Zimbabwe - 100 - - - 100 Other 1 100 1 71 1 60 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 111 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2007 Inflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 21 16 8 21 29 13 (Other) EU15 23 24 11 28 34 18 (Other) EU10 42 17 21 25 63 14 (Other) EU2 - 100 1 64 1 54 India 14 16 2 19 16 14 South Africa 4 32 3 19 7 21 Australia 4 27 3 26 7 19 Philippines 4 35 2 32 6 25 China (exc. Taiwan) 4 46 - 64 5 44 United States of America (USA) 2 23 2 27 4 18 New Zealand 1 53 2 47 3 36 Canada 1 47 1 61 3 38 United Arab Emirates - - 2 79 2 79 Zimbabwe 2 48 - 53 2 41 Other 8 18 7 29 15 16

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 14 20 5 22 19 16 (Other) EU15 14 29 5 36 20 24 (Other) EU10 10 41 4 42 14 33 (Other) EU2 - - - 100 - 100 India 12 17 2 20 15 15 South Africa 3 40 2 24 5 28 Australia 4 29 2 28 6 21 Philippines 2 47 2 44 4 33 China (exc. Taiwan) 2 51 - 100 2 50 United States of America (USA) 1 28 1 28 3 20 New Zealand 1 67 - 50 1 51 Canada 1 52 1 63 2 41 United Arab Emirates - - 2 100 2 100 Zimbabwe 1 65 - 100 1 60 Other 6 20 5 34 11 19 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 112 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2007 Inflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 5 28 3 45 8 24 (Other) EU15 5 36 5 42 11 28 (Other) EU10 23 22 13 33 36 18 (Other) EU2 - 100 1 76 1 60 India 1 38 - 58 1 34 South Africa 1 39 1 37 2 27 Australia - 60 - 64 1 44 Philippines 1 52 1 36 2 37 China (exc. Taiwan) 2 76 - - 2 76 United States of America (USA) - 42 - 62 1 35 New Zealand - 47 1 67 2 57 Canada - 100 - 100 - 82 United Arab Emirates - - 1 100 1 100 Zimbabwe 1 83 - 61 1 63 Other 2 43 1 33 3 32

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 1 39 - 100 2 36 (Other) EU15 3 84 - - 3 84 (Other) EU10 9 34 4 58 13 30 (Other) EU2 ------India 1 78 - - 1 78 South Africa - 59 - 64 - 43 Australia ------Philippines - 100 - - - 100 China (exc. Taiwan) - - - 79 - 79 United States of America (USA) - 100 - 100 - 82 New Zealand - - - 79 - 79 Canada ------United Arab Emirates ------Zimbabwe - 63 - - - 63 Other - 72 - 91 1 61 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 113 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2007 Outflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 45 10 24 18 69 9 (Other) EU15 5 34 10 31 15 23 (Other) EU10 5 48 5 49 10 34 (Other) EU2 - - - 100 - 100 India 6 13 6 11 12 8 South Africa 4 22 3 24 7 16 Australia 6 27 1 33 6 25 Philippines 2 17 2 37 4 20 China (exc. Taiwan) 1 30 1 27 2 20 United States of America (USA) - 41 1 43 1 33 New Zealand - 45 1 75 1 62 Canada - 46 1 72 1 53 United Arab Emirates 1 24 - 77 1 24 Zimbabwe - 55 - 51 1 40 Other 4 19 2 32 6 17

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 29 11 17 22 46 11 (Other) EU15 4 40 5 39 9 28 (Other) EU10 - 71 2 73 2 66 (Other) EU2 ------India 3 16 3 16 6 11 South Africa 1 40 1 40 2 29 Australia 5 29 1 36 6 26 Philippines 1 22 2 46 3 28 China (exc. Taiwan) 1 33 1 33 2 23 United States of America (USA) - 50 - 40 1 31 New Zealand - 45 - 71 - 38 Canada - 51 1 85 1 65 United Arab Emirates 1 26 - 100 1 26 Zimbabwe - - - 71 - 71 Other 2 25 1 32 3 20 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 114 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2007 Outflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 15 20 6 31 21 17 (Other) EU15 1 62 5 50 6 41 (Other) EU10 5 51 1 94 6 45 (Other) EU2 - - - 100 - 100 India 2 26 2 17 4 15 South Africa 2 32 1 36 3 24 Australia - 41 - - - 41 Philippines 1 30 - 38 1 24 China (exc. Taiwan) - 100 - 51 - 45 United States of America (USA) - 72 - 88 1 70 New Zealand - - - 58 - 58 Canada - 100 - 100 - 73 United Arab Emirates - 71 - 100 - 58 Zimbabwe - 59 - - - 59 Other 1 42 - 53 1 33

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 1 42 1 53 2 33 (Other) EU15 - - - 58 - 58 (Other) EU10 - 100 1 90 2 85 (Other) EU2 ------India 1 29 1 37 1 23 South Africa 1 41 1 55 2 33 Australia - - - 71 - 71 Philippines - 59 - 100 - 52 China (exc. Taiwan) - 100 - 100 - 73 United States of America (USA) - - - 72 - 72 New Zealand - - 1 100 1 100 Canada ------United Arab Emirates ------Zimbabwe - 100 - 71 - 66 Other 1 39 1 70 2 38 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 115 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2008 Inflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 24 16 14 23 38 13 (Other) EU15 14 24 14 26 28 18 (Other) EU10 30 22 12 27 42 17 (Other) EU2 10 38 3 60 13 32 India 12 15 1 41 13 14 South Africa 4 29 2 31 6 22 Australia 2 24 4 24 6 18 Philippines 3 27 2 32 5 21 China (exc. Taiwan) 2 34 2 28 4 22 United States of America (USA) 1 63 1 46 2 44 New Zealand - 100 2 76 2 73 Canada 2 55 - 58 2 49 United Arab Emirates 2 60 - - 2 60 Zimbabwe 2 89 - - 2 89 Other 12 17 8 27 19 15

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 20 18 11 28 31 15 (Other) EU15 8 25 8 36 15 22 (Other) EU10 6 57 2 53 8 46 (Other) EU2 1 51 2 65 3 45 India 11 16 1 43 12 15 South Africa 4 31 1 38 5 26 Australia 1 26 3 27 4 20 Philippines 3 28 2 32 4 21 China (exc. Taiwan) 1 54 1 29 2 28 United States of America (USA) 1 65 - 41 2 51 New Zealand - - 1 100 1 100 Canada 2 60 - 77 2 55 United Arab Emirates 1 78 - - 1 78 Zimbabwe 2 100 - - 2 100 Other 8 21 7 30 15 18 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 116 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2008 Inflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 4 29 3 33 7 22 (Other) EU15 5 53 2 43 7 41 (Other) EU10 19 28 7 39 26 23 (Other) EU2 7 48 - - 7 48 India 1 35 - 100 1 33 South Africa - 44 1 54 1 38 Australia 1 49 1 57 1 37 Philippines - 51 - 100 - 47 China (exc. Taiwan) 1 40 1 61 1 36 United States of America (USA) - 100 - 90 - 80 New Zealand - 100 1 100 2 94 Canada - 100 - 72 - 60 United Arab Emirates - 61 - - - 61 Zimbabwe - 100 - - - 100 Other 3 32 1 42 4 27

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals - 50 - 59 1 39 (Other) EU15 1 62 4 51 5 42 (Other) EU10 5 37 4 48 9 30 (Other) EU2 2 80 2 100 4 63 India - 61 - 100 - 56 South Africa ------Australia - 100 - 100 - 71 Philippines - - - 100 - 100 China (exc. Taiwan) 1 78 - - 1 78 United States of America (USA) ------New Zealand ------Canada - 100 - - - 100 United Arab Emirates - 100 - - - 100 Zimbabwe ------Other - 52 - - - 52 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 117 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2008 Outflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 59 13 25 20 84 11 (Other) EU15 10 29 10 26 20 20 (Other) EU10 19 24 10 58 29 26 (Other) EU2 - 100 - - - 100 India 6 13 9 14 15 10 South Africa 6 16 1 31 7 14 Australia 2 23 3 28 4 19 Philippines 2 37 2 34 4 25 China (exc. Taiwan) 2 25 1 45 3 22 United States of America (USA) 1 41 1 60 2 36 New Zealand 1 37 - 64 1 32 Canada 1 35 - 100 1 34 United Arab Emirates 1 29 - 71 1 27 Zimbabwe - 58 1 67 1 55 Other 5 27 3 31 8 20

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 42 16 13 17 55 13 (Other) EU15 5 26 6 33 12 22 (Other) EU10 5 51 1 96 6 45 (Other) EU2 - 100 - - - 100 India 4 17 6 19 10 13 South Africa 5 17 1 36 6 15 Australia 1 30 1 43 2 27 Philippines 1 47 - 58 1 39 China (exc. Taiwan) 2 29 1 50 2 25 United States of America (USA) 1 36 1 76 1 44 New Zealand 1 45 - 64 1 37 Canada - 60 - - - 60 United Arab Emirates 1 32 - 71 1 29 Zimbabwe - 100 1 81 1 75 Other 1 32 2 46 3 32 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 118 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2008 Outflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 14 21 12 39 26 21 (Other) EU15 2 56 3 45 5 35 (Other) EU10 6 37 7 78 14 45 (Other) EU2 ------India 2 23 3 20 4 15 South Africa - 38 - 71 1 34 Australia - 41 1 40 2 32 Philippines 1 59 1 41 2 34 China (exc. Taiwan) - 45 - 100 - 41 United States of America (USA) - 100 - 71 - 72 New Zealand - 68 - - - 68 Canada - 67 - 100 - 58 United Arab Emirates - 71 - - - 71 Zimbabwe - - - 71 - 71 Other 2 35 1 41 3 27

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 3 43 1 37 3 37 (Other) EU15 3 86 1 100 4 69 (Other) EU10 8 41 2 81 9 36 (Other) EU2 ------India - 41 - 38 1 28 South Africa - 100 - 100 - 71 Australia - 58 - 58 - 41 Philippines 1 72 - 85 1 57 China (exc. Taiwan) - 75 - - - 75 United States of America (USA) - - - 100 - 100 New Zealand ------Canada - 55 - - - 55 United Arab Emirates ------Zimbabwe - 71 - - - 71 Other 2 52 - 50 2 47 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 119 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2009 Inflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 25 14 16 16 41 11 (Other) EU15 21 19 11 21 32 14 (Other) EU10 21 20 14 28 34 16 (Other) EU2 2 30 3 33 5 23 India 9 16 2 34 11 15 South Africa 2 33 4 28 6 22 Australia 2 47 1 42 4 33 Philippines 3 26 - 100 3 25 China (exc. Taiwan) 2 47 1 45 3 39 United States of America (USA) 2 27 - 71 2 26 New Zealand 1 91 1 76 2 58 Canada - 71 1 37 2 33 United Arab Emirates 1 40 - 100 1 37 Zimbabwe 1 85 - 88 1 63 Other 9 18 5 22 14 14

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 14 17 9 20 22 13 (Other) EU15 13 28 8 27 21 20 (Other) EU10 2 38 2 28 4 23 (Other) EU2 1 51 1 60 2 39 India 7 19 2 38 8 17 South Africa 1 46 2 37 3 29 Australia 2 52 1 45 3 37 Philippines 1 34 - - 1 34 China (exc. Taiwan) 2 58 1 45 2 44 United States of America (USA) 2 31 - 100 2 30 New Zealand 1 91 - 100 1 71 Canada - 100 1 47 1 43 United Arab Emirates 1 43 - 100 1 40 Zimbabwe 1 85 - 88 1 63 Other 6 22 5 24 11 16 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 120 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2009 Inflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 11 25 6 30 17 19 (Other) EU15 5 26 2 33 7 21 (Other) EU10 15 26 4 23 20 21 (Other) EU2 1 48 2 38 2 30 India 3 34 - 74 3 32 South Africa 1 50 1 45 2 34 Australia - 71 - 100 - 58 Philippines 2 39 - 100 2 36 China (exc. Taiwan) 1 78 - - 1 78 United States of America (USA) - 52 - 100 1 46 New Zealand - - 1 100 1 100 Canada - 100 - 59 1 51 United Arab Emirates - 100 - - - 100 Zimbabwe ------Other 2 35 1 61 3 31

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 1 58 1 63 2 43 (Other) EU15 3 41 1 51 4 34 (Other) EU10 4 30 7 51 11 35 (Other) EU2 1 60 1 83 2 54 India ------South Africa - 100 - - - 100 Australia ------Philippines ------China (exc. Taiwan) ------United States of America (USA) ------New Zealand ------Canada ------United Arab Emirates ------Zimbabwe ------Other - 72 - - - 72 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 121 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2009 Outflow Total Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 40 8 19 14 59 7 (Other) EU15 20 24 8 22 28 18 (Other) EU10 17 23 3 25 21 20 (Other) EU2 - 71 1 51 1 41 India 7 14 6 16 13 11 South Africa 8 14 1 32 10 13 Australia 3 24 2 25 6 17 Philippines 3 24 2 25 5 18 China (exc. Taiwan) 1 32 1 31 3 22 United States of America (USA) 1 44 1 34 2 27 New Zealand 1 45 1 33 2 27 Canada 1 32 - 100 1 31 United Arab Emirates 1 46 - 71 1 39 Zimbabwe 1 63 - - 1 63 Other 5 18 4 21 9 14

A. Highly skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 25 10 11 12 37 8 (Other) EU15 16 28 4 25 21 23 (Other) EU10 1 51 1 52 2 39 (Other) EU2 - - - 100 - 100 India 4 19 3 21 7 14 South Africa 7 15 1 33 8 14 Australia 3 27 2 28 5 19 Philippines 1 33 1 31 3 22 China (exc. Taiwan) 1 48 1 38 1 30 United States of America (USA) 1 44 1 43 2 31 New Zealand - 58 1 42 1 34 Canada 1 35 - 100 1 33 United Arab Emirates - 58 - 100 1 51 Zimbabwe - 100 - - - 100 Other 3 22 1 34 4 19 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 122 Table D4 Flows of workers by country of nationality and by main categorisation (EU groupings and top 10 third countries), 2004 - 2009 (continued) 2009 Outflow B. Skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 14 17 7 32 21 16 (Other) EU15 3 51 3 31 5 30 (Other) EU10 10 29 1 39 11 26 (Other) EU2 - 71 1 59 1 45 India 2 23 2 29 4 18 South Africa 1 32 - 100 1 30 Australia - 59 - 71 1 46 Philippines 1 49 1 61 2 38 China (exc. Taiwan) - 50 - 59 1 38 United States of America (USA) - - - 53 - 53 New Zealand - 71 - 58 - 45 Canada - 77 - - - 77 United Arab Emirates - 100 - - - 100 Zimbabwe - 71 - - - 71 Other 1 32 2 28 3 21

C. Low skilled Male Female Total Country of Nationality Est SE% Est SE% Est SE% Nationals 1 34 1 43 2 27 (Other) EU15 1 37 1 100 3 49 (Other) EU10 6 47 2 39 7 38 (Other) EU2 ------India 1 51 1 38 2 33 South Africa - 72 - - - 72 Australia - 100 - 100 - 71 Philippines 1 45 - 59 1 36 China (exc. Taiwan) - 72 - 100 - 58 United States of America (USA) ------New Zealand - - - 100 - 100 Canada ------United Arab Emirates - 100 - 100 - 71 Zimbabwe ------Other 1 60 - 59 1 46 Source: Migration Statistics Unit, Office for National Statistics. Notes: No data available for Researchers and Seasonal workers. Standard error percentages (SE%) indicate the robustness of each estimate and conditional formatting has been applied to them. A standard error of <=20% has a white background, >20% but <=25% has a light yellow background and >25% is in italics and has an orange background. A migration figure with a standard error of >25% is not considered to be reliable. Where the standard error is >30% the associated estimate is in italics. For any given estimate there is a 95% probability that the true figure lies in the range: estimate +/- 0.0196 x estimate x standard error %. 123 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 2004 1.Nationals Specific occupations Male Female Total Housekeepers and related workers (5121) 1,650 42,450 44,100 Cooks (5122) 111,099 104,408 215,507 Waiters, waitresses and bartenders (5123) 119,147 254,551 373,698 Child-care workers (5131) 2,613 111,495 114,108 Personal care and related workers not elsewhere 65,769 479,873 545,642 classified (5139) Medical doctors (2221) 77,325 61,415 138,740 Nursing and midwifery professionals (2230) 72,983 601,750 674,733 Skilled Agricultural and Fishery Workers (61) 279,441 29,107 308,548 Architects, Engineers and related professionals (214) 519,411 35,168 554,579 Teaching personnel (23) 412,095 797,975 1,210,070 Labourers In Mining, Construction, Manufacturing 239,315 7,210 246,525 And Transport (93) Total 1,900,848 2,525,402 4,426,249

2. (Other) EU15 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 560 2,065 2,625 2.2 Cooks (5122) 9,646 3,853 13,499 11.5 Waiters, waitresses and bartenders (5123) 5,583 7,344 12,927 11.0 Child-care workers (5131) 0 4,344 4,344 3.7 Personal care and related workers not elsewhere 1,639 8,473 10,112 8.6 classified (5139) Medical doctors (2221) 4,213 2,462 6,674 5.7 Nursing and midwifery professionals (2230) 628 16,744 17,372 14.8 Skilled Agricultural and Fishery Workers (61) 3,441 440 3,881 3.3 Architects, Engineers and related professionals (214) 9,122 2,566 11,687 10.0 Teaching personnel (23) 10,568 19,884 30,452 26.0 Labourers In Mining, Construction, Manufacturing 3,702 0 3,702 3.2 And Transport (93) Total 49,100 68,174 117,275 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

124 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2004 3. (Other) EU10 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 633 633 2.9 Cooks (5122) 2,051 252 2,302 10.6 Waiters, waitresses and bartenders (5123) 873 2,862 3,735 17.2 Child-care workers (5131) 148 7,409 7,557 34.9 Personal care and related workers not elsewhere 138 445 584 2.7 classified (5139) Medical doctors (2221) 223 695 917 4.2 Nursing and midwifery professionals (2230) 0 408 408 1.9 Skilled Agricultural and Fishery Workers (61) 161 0 161 0.7 Architects, Engineers and related professionals (214) 342 0 342 1.6 Teaching personnel (23) 1,260 1,641 2,901 13.4 Labourers In Mining, Construction, Manufacturing 2,119 0 2,119 9.8 And Transport (93) Total 7,313 14,344 21,657 100.0

4. (Other) EU2 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 0 0 0.0 Cooks (5122) 0 0 0 0.0 Waiters, waitresses and bartenders (5123) 0 0 0 0.0 Child-care workers (5131) 0 593 593 29.5 Personal care and related workers not elsewhere 0 0 0 0.0 classified (5139) Medical doctors (2221) 114 0 114 5.6 Nursing and midwifery professionals (2230) 166 128 294 14.6 Skilled Agricultural and Fishery Workers (61) 0 0 0 0.0 Architects, Engineers and related professionals (214) 0 194 194 9.6 Teaching personnel (23) 442 377 819 40.7 Labourers In Mining, Construction, Manufacturing 0 0 0 0.0 And Transport (93) Total 721 1,292 2,013 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

125 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2004 5. Third-Country Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 294 2,734 3,028 1.3 Cooks (5122) 18,922 3,946 22,868 9.9 Waiters, waitresses and bartenders (5123) 10,402 8,921 19,322 8.4 Child-care workers (5131) 0 6,892 6,892 3.0 Personal care and related workers not elsewhere 5,457 21,223 26,680 11.5 classified (5139) Medical doctors (2221) 19,574 4,862 24,436 10.6 Nursing and midwifery professionals (2230) 14,508 51,201 65,709 28.4 Skilled Agricultural and Fishery Workers (61) 1,315 457 1,772 0.8 Architects, Engineers and related professionals (214) 13,415 3,025 16,440 7.1 Teaching personnel (23) 15,120 22,994 38,114 16.5 Labourers In Mining, Construction, Manufacturing 5,979 0 5,979 2.6 And Transport (93) Total 104,985 126,254 231,239 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

126 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2005 1.Nationals Specific occupations Male Female Total Housekeepers and related workers (5121) 1,821 41,601 43,422 Cooks (5122) 101,025 91,517 192,543 Waiters, waitresses and bartenders (5123) 112,616 223,714 336,330 Child-care workers (5131) 1,943 88,091 90,034 Personal care and related workers not elsewhere 67,768 463,564 531,332 classified (5139) Medical doctors (2221) 83,862 57,451 141,312 Nursing and midwifery professionals (2230) 64,592 547,580 612,172 Skilled Agricultural and Fishery Workers (61) 256,017 28,731 284,749 Architects, Engineers and related professionals (214) 496,902 38,249 535,151 Teaching personnel (23) 386,611 753,507 1,140,118 Labourers In Mining, Construction, Manufacturing 218,661 8,177 226,838 And Transport (93) Total 1,791,817 2,342,182 4,133,999

2. (Other) EU15 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 2,903 2,903 2.3 Cooks (5122) 9,526 2,099 11,625 9.3 Waiters, waitresses and bartenders (5123) 5,847 6,400 12,246 9.8 Child-care workers (5131) 0 3,521 3,521 2.8 Personal care and related workers not elsewhere 699 9,245 9,944 7.9 classified (5139) Medical doctors (2221) 6,508 3,331 9,839 7.8 Nursing and midwifery professionals (2230) 2,871 16,971 19,841 15.8 Skilled Agricultural and Fishery Workers (61) 1,947 254 2,201 1.8 Architects, Engineers and related professionals (214) 11,029 3,705 14,734 11.7 Teaching personnel (23) 12,611 22,110 34,721 27.7 Labourers In Mining, Construction, Manufacturing 3,889 0 3,889 3.1 And Transport (93) Total 54,926 70,537 125,463 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

127 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2005 3. (Other) EU10 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 595 595 1.8 Cooks (5122) 3,649 1,078 4,727 14.1 Waiters, waitresses and bartenders (5123) 3,912 5,908 9,820 29.4 Child-care workers (5131) 0 6,408 6,408 19.2 Personal care and related workers not elsewhere 1,080 2,002 3,082 9.2 classified (5139) Medical doctors (2221) 377 121 498 1.5 Nursing and midwifery professionals (2230) 667 1,467 2,133 6.4 Skilled Agricultural and Fishery Workers (61) 662 204 866 2.6 Architects, Engineers and related professionals (214) 1,270 0 1,270 3.8 Teaching personnel (23) 488 1,017 1,505 4.5 Labourers In Mining, Construction, Manufacturing 2,703 403 3,106 9.3 And Transport (93) Total 14,807 19,202 33,413 100.0

4. (Other) EU2 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 0 0 0.0 Cooks (5122) 0 0 0 0.0 Waiters, waitresses and bartenders (5123) 347 226 573 11.6 Child-care workers (5131) 0 627 627 12.7 Personal care and related workers not elsewhere 131 491 622 12.6 classified (5139) Medical doctors (2221) 390 0 390 7.9 Nursing and midwifery professionals (2230) 0 964 964 19.5 Skilled Agricultural and Fishery Workers (61) 0 0 0 0.0 Architects, Engineers and related professionals (214) 0 302 302 6.1 Teaching personnel (23) 0 278 278 5.6 Labourers In Mining, Construction, Manufacturing 1,185 0 1,185 24.0 And Transport (93) Total 2,052 2,888 4,939 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

128 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2005 5. Third-Country Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 553 1,290 1,843 0.7 Cooks (5122) 21,896 3,265 25,160 10.0 Waiters, waitresses and bartenders (5123) 10,595 9,345 19,941 7.9 Child-care workers (5131) 274 5,779 6,053 2.4 Personal care and related workers not elsewhere 9,619 26,746 36,365 14.4 classified (5139) Medical doctors (2221) 20,903 10,811 31,714 12.6 Nursing and midwifery professionals (2230) 13,967 56,878 70,845 28.1 Skilled Agricultural and Fishery Workers (61) 1,667 640 2,308 0.9 Architects, Engineers and related professionals (214) 13,099 3,588 16,686 6.6 Teaching personnel (23) 15,971 20,699 36,670 14.5 Labourers In Mining, Construction, Manufacturing 4,632 0 4,632 1.8 And Transport (93) Total 113,174 139,040 252,214 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

129 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2006 1.Nationals Specific occupations Male Female Total Housekeepers and related workers (5121) 2,693 38,509 41,202 Cooks (5122) 102,045 99,219 201,264 Waiters, waitresses and bartenders (5123) 121,585 249,194 370,779 Child-care workers (5131) 2,121 102,056 104,177 Personal care and related workers not elsewhere 69,861 509,709 579,571 classified (5139) Medical doctors (2221) 93,440 66,221 159,661 Nursing and midwifery professionals (2230) 74,787 614,160 688,948 Skilled Agricultural and Fishery Workers (61) 270,466 30,370 300,837 Architects, Engineers and related professionals (214) 568,177 42,888 611,065 Teaching personnel (23) 411,990 843,514 1,255,504 Labourers In Mining, Construction, Manufacturing 238,089 8,149 246,238 And Transport (93) Total 1,955,254 2,603,989 4,559,242

2. (Other) EU15 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 1,858 1,858 1.7 Cooks (5122) 8,516 2,158 10,674 9.7 Waiters, waitresses and bartenders (5123) 6,155 4,638 10,793 9.8 Child-care workers (5131) 0 4,226 4,226 3.8 Personal care and related workers not elsewhere 1,005 11,290 12,295 11.1 classified (5139) Medical doctors (2221) 4,830 2,267 7,097 6.4 Nursing and midwifery professionals (2230) 1,242 16,610 17,851 16.2 Skilled Agricultural and Fishery Workers (61) 1,281 320 1,600 1.4 Architects, Engineers and related professionals (214) 5,065 2,613 7,678 6.9 Teaching personnel (23) 10,294 21,301 31,595 28.6 Labourers In Mining, Construction, Manufacturing 4,838 0 4,838 4.4 And Transport (93) Total 43,224 67,280 110,504 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

130 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2006 3. (Other) EU10 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 185 1,234 1,419 2.7 Cooks (5122) 3,906 1,127 5,033 9.5 Waiters, waitresses and bartenders (5123) 4,075 7,104 11,179 21.1 Child-care workers (5131) 0 7,099 7,099 13.4 Personal care and related workers not elsewhere 3,715 5,370 9,085 17.2 classified (5139) Medical doctors (2221) 768 0 768 1.5 Nursing and midwifery professionals (2230) 892 2,059 2,951 5.6 Skilled Agricultural and Fishery Workers (61) 847 124 971 1.8 Architects, Engineers and related professionals (214) 3,011 0 3,011 5.7 Teaching personnel (23) 919 1,316 2,234 4.2 Labourers In Mining, Construction, Manufacturing 8,713 416 9,129 17.3 And Transport (93) Total 27,031 25,848 52,879 100.0

4. (Other) EU2 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 0 0 0.0 Cooks (5122) 0 0 0 0.0 Waiters, waitresses and bartenders (5123) 237 259 496 12.1 Child-care workers (5131) 0 1,364 1,364 33.4 Personal care and related workers not elsewhere 0 61 61 1.5 classified (5139) Medical doctors (2221) 0 112 112 2.7 Nursing and midwifery professionals (2230) 0 551 551 13.5 Skilled Agricultural and Fishery Workers (61) 0 0 0 0.0 Architects, Engineers and related professionals (214) 382 0 382 9.4 Teaching personnel (23) 0 0 0 0.0 Labourers In Mining, Construction, Manufacturing 981 137 1,117 27.4 And Transport (93) Total 1,600 2,483 4,083 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

131 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2006 5. Third-Country Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 282 2,760 3,042 1.0 Cooks (5122) 28,978 3,611 32,589 11.2 Waiters, waitresses and bartenders (5123) 13,961 9,596 23,557 8.1 Child-care workers (5131) 0 7,514 7,514 2.6 Personal care and related workers not elsewhere 12,071 40,431 52,502 18.1 classified (5139) Medical doctors (2221) 26,305 13,927 40,231 13.9 Nursing and midwifery professionals (2230) 16,102 53,096 69,199 23.9 Skilled Agricultural and Fishery Workers (61) 2,236 431 2,667 0.9 Architects, Engineers and related professionals (214) 16,722 4,181 20,903 7.2 Teaching personnel (23) 11,942 20,070 32,011 11.0 Labourers In Mining, Construction, Manufacturing 5,747 0 5,747 2.0 And Transport (93) Total 134,345 155,616 289,961 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

132 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2007 1.Nationals Specific occupations Male Female Total Housekeepers and related workers (5121) 3,223 49,711 52,933 Cooks (5122) 109,211 96,998 206,209 Waiters, waitresses and bartenders (5123) 116,928 247,604 364,533 Child-care workers (5131) 3,187 109,492 112,679 Personal care and related workers not elsewhere 67,356 510,512 577,868 classified (5139) Medical doctors (2221) 88,781 66,740 155,521 Nursing and midwifery professionals (2230) 68,871 615,681 684,552 Skilled Agricultural and Fishery Workers (61) 263,703 41,501 305,203 Architects, Engineers and related professionals (214) 562,115 48,449 610,563 Teaching personnel (23) 400,059 829,549 1,229,608 Labourers In Mining, Construction, Manufacturing 247,602 4,678 252,281 And Transport (93) Total 1,931,035 2,620,915 4,551,950

2. (Other) EU15 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 1,221 1,980 3,201 2.8 Cooks (5122) 6,489 2,585 9,074 8.0 Waiters, waitresses and bartenders (5123) 6,468 4,490 10,958 9.7 Child-care workers (5131) 0 2,340 2,340 2.1 Personal care and related workers not elsewhere 2,053 8,590 10,643 9.4 classified (5139) Medical doctors (2221) 3,246 3,432 6,678 5.9 Nursing and midwifery professionals (2230) 1,619 18,531 20,150 17.8 Skilled Agricultural and Fishery Workers (61) 994 662 1,656 1.5 Architects, Engineers and related professionals (214) 9,133 2,091 11,224 9.9 Teaching personnel (23) 10,754 20,656 31,410 27.7 Labourers In Mining, Construction, Manufacturing 5,221 828 6,048 5.3 And Transport (93) Total 47,196 66,185 113,381 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

133 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2007 3. (Other) EU10 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 165 2,079 2,244 3.3 Cooks (5122) 6,084 2,002 8,086 11.9 Waiters, waitresses and bartenders (5123) 6,304 9,805 16,108 23.6 Child-care workers (5131) 491 5,502 5,992 8.8 Personal care and related workers not elsewhere 2,360 7,746 10,106 14.8 classified (5139) Medical doctors (2221) 1,589 862 2,451 3.6 Nursing and midwifery professionals (2230) 912 2,696 3,607 5.3 Skilled Agricultural and Fishery Workers (61) 2,763 117 2,881 4.2 Architects, Engineers and related professionals (214) 2,004 0 2,004 2.9 Teaching personnel (23) 2,041 2,583 4,624 6.8 Labourers In Mining, Construction, Manufacturing 9,144 891 10,034 14.7 And Transport (93) Total 33,857 34,281 68,137 100.0

4. (Other) EU2 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 0 0 0.0 Cooks (5122) 535 0 535 9.9 Waiters, waitresses and bartenders (5123) 0 921 921 17.0 Child-care workers (5131) 0 419 419 7.7 Personal care and related workers not elsewhere 0 1,107 1,107 20.4 classified (5139) Medical doctors (2221) 0 0 0 0.0 Nursing and midwifery professionals (2230) 0 292 292 5.4 Skilled Agricultural and Fishery Workers (61) 0 0 0 0.0 Architects, Engineers and related professionals (214) 592 121 713 13.2 Teaching personnel (23) 0 0 0 0.0 Labourers In Mining, Construction, Manufacturing 1,427 0 1,427 26.4 And Transport (93) Total 2,554 2,859 5,413 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

134 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2007 5. Third-Country Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 273 1,623 1,896 0.6 Cooks (5122) 27,411 3,155 30,566 10.3 Waiters, waitresses and bartenders (5123) 12,220 7,707 19,927 6.7 Child-care workers (5131) 180 7,391 7,571 2.6 Personal care and related workers not elsewhere 18,058 35,959 54,017 18.2 classified (5139) Medical doctors (2221) 20,610 11,917 32,526 11.0 Nursing and midwifery professionals (2230) 16,366 57,998 74,364 25.1 Skilled Agricultural and Fishery Workers (61) 1,670 227 1,897 0.6 Architects, Engineers and related professionals (214) 20,604 5,589 26,192 8.8 Teaching personnel (23) 20,769 20,105 40,874 13.8 Labourers In Mining, Construction, Manufacturing 6,860 92 6,952 2.3 And Transport (93) Total 145,018 151,762 296,780 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

135 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2008 1.Nationals Specific occupations Male Female Total Housekeepers and related workers (5121) 3,717 46,508 50,225 Cooks (5122) 104,314 94,894 199,208 Waiters, waitresses and bartenders (5123) 127,844 243,178 371,022 Child-care workers (5131) 3,580 102,393 105,973 Personal care and related workers not elsewhere 85,304 517,483 602,787 classified (5139) Medical doctors (2221) 90,796 82,212 173,009 Nursing and midwifery professionals (2230) 75,514 626,443 701,957 Skilled Agricultural and Fishery Workers (61) 269,436 39,625 309,060 Architects, Engineers and related professionals (214) 566,121 54,404 620,525 Teaching personnel (23) 395,075 820,683 1,215,758 Labourers In Mining, Construction, Manufacturing 231,048 6,993 238,041 And Transport (93) Total 1,952,747 2,634,815 4,587,563

2. (Other) EU15 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 886 886 0.7 Cooks (5122) 7,126 1,387 8,513 6.7 Waiters, waitresses and bartenders (5123) 8,037 7,833 15,870 12.6 Child-care workers (5131) 151 4,790 4,940 3.9 Personal care and related workers not elsewhere 2,329 11,817 14,146 11.2 classified (5139) Medical doctors (2221) 4,731 3,947 8,678 6.9 Nursing and midwifery professionals (2230) 1,531 15,902 17,433 13.8 Skilled Agricultural and Fishery Workers (61) 668 355 1,022 0.8 Architects, Engineers and related professionals (214) 11,273 5,390 16,662 13.2 Teaching personnel (23) 12,030 22,207 34,237 27.1 Labourers In Mining, Construction, Manufacturing 3,617 146 3,763 3.0 And Transport (93) Total 51,490 74,659 126,149 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

136 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2008 3. (Other) EU10 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 1,427 4,033 5,460 7.5 Cooks (5122) 5,606 1,237 6,843 9.5 Waiters, waitresses and bartenders (5123) 4,114 12,538 16,652 23.0 Child-care workers (5131) 603 3,215 3,819 5.3 Personal care and related workers not elsewhere 670 10,429 11,099 15.3 classified (5139) Medical doctors (2221) 968 1,263 2,231 3.1 Nursing and midwifery professionals (2230) 0 2,224 2,224 3.1 Skilled Agricultural and Fishery Workers (61) 2,289 497 2,786 3.9 Architects, Engineers and related professionals (214) 3,125 816 3,941 5.4 Teaching personnel (23) 652 2,821 3,473 4.8 Labourers In Mining, Construction, Manufacturing 12,169 1,655 13,824 19.1 And Transport (93) Total 31,622 40,728 72,351 100.0

4. (Other) EU2 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 0 0 0.0 Cooks (5122) 774 286 1,060 11.7 Waiters, waitresses and bartenders (5123) 391 303 694 7.6 Child-care workers (5131) 175 1,069 1,244 13.7 Personal care and related workers not elsewhere 0 648 648 7.1 classified (5139) Medical doctors (2221) 323 290 613 6.8 Nursing and midwifery professionals (2230) 0 193 193 2.1 Skilled Agricultural and Fishery Workers (61) 0 0 0 0.0 Architects, Engineers and related professionals (214) 0 0 0 0.0 Teaching personnel (23) 0 0 0 0.0 Labourers In Mining, Construction, Manufacturing 4,624 0 4,624 51.0 And Transport (93) Total 6,287 2,789 9,076 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

137 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2008 5. Third-Country Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 1,442 3,921 5,363 1.7 Cooks (5122) 31,559 4,001 35,559 11.0 Waiters, waitresses and bartenders (5123) 15,390 14,672 30,062 9.3 Child-care workers (5131) 179 6,216 6,395 2.0 Personal care and related workers not elsewhere 16,596 41,087 57,684 17.8 classified (5139) Medical doctors (2221) 23,051 10,919 33,970 10.5 Nursing and midwifery professionals (2230) 17,205 59,084 76,289 23.6 Skilled Agricultural and Fishery Workers (61) 2,443 107 2,550 0.8 Architects, Engineers and related professionals (214) 20,952 5,236 26,188 8.1 Teaching personnel (23) 16,222 23,104 39,325 12.2 Labourers In Mining, Construction, Manufacturing 9,998 275 10,272 3.2 And Transport (93) Total 155,035 168,621 323,656 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

138 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2009 1.Nationals Specific occupations Male Female Total Housekeepers and related workers (5121) 2,347 45,333 47,680 Cooks (5122) 112,045 89,893 201,938 Waiters, waitresses and bartenders (5123) 125,071 244,642 369,713 Child-care workers (5131) 4,025 105,080 109,105 Personal care and related workers not elsewhere 92,117 544,230 636,347 classified (5139) Medical doctors (2221) 90,976 87,941 178,917 Nursing and midwifery professionals (2230) 76,748 607,058 683,806 Skilled Agricultural and Fishery Workers (61) 293,517 35,561 329,078 Architects, Engineers and related professionals (214) 560,312 48,738 609,049 Teaching personnel (23) 404,889 861,224 1,266,113 Labourers In Mining, Construction, Manufacturing 201,394 5,821 207,215 And Transport (93) Total 1,963,440 2,675,520 4,638,960

2. (Other) EU15 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 1,354 1,354 1.1 Cooks (5122) 6,560 2,427 8,987 7.5 Waiters, waitresses and bartenders (5123) 7,760 6,000 13,759 11.4 Child-care workers (5131) 0 5,176 5,176 4.3 Personal care and related workers not elsewhere 1,631 10,840 12,471 10.3 classified (5139) Medical doctors (2221) 6,104 2,058 8,161 6.8 Nursing and midwifery professionals (2230) 2,038 12,366 14,404 11.9 Skilled Agricultural and Fishery Workers (61) 2,451 1,149 3,599 3.0 Architects, Engineers and related professionals (214) 11,509 4,287 15,796 13.1 Teaching personnel (23) 10,327 23,482 33,809 28.0 Labourers In Mining, Construction, Manufacturing 3,063 0 3,063 2.5 And Transport (93) Total 51,440 69,138 120,578 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

139 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2009 3. (Other) EU10 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 2,601 2,601 3.6 Cooks (5122) 8,544 2,416 10,960 15.0 Waiters, waitresses and bartenders (5123) 3,068 13,080 16,148 22.1 Child-care workers (5131) 1,130 5,457 6,587 9.0 Personal care and related workers not elsewhere 1,559 7,646 9,204 12.6 classified (5139) Medical doctors (2221) 143 532 675 0.9 Nursing and midwifery professionals (2230) 427 4,501 4,928 6.7 Skilled Agricultural and Fishery Workers (61) 3,551 300 3,850 5.3 Architects, Engineers and related professionals (214) 3,422 111 3,533 4.8 Teaching personnel (23) 1,512 3,551 5,062 6.9 Labourers In Mining, Construction, Manufacturing 8,008 1,641 9,649 13.2 And Transport (93) Total 31,363 41,834 73,196 100.0

4. (Other) EU2 Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 0 235 235 1.6 Cooks (5122) 285 482 766 5.1 Waiters, waitresses and bartenders (5123) 0 526 526 3.5 Child-care workers (5131) 823 2,084 2,907 19.5 Personal care and related workers not elsewhere 190 1,873 2,063 13.9 classified (5139) Medical doctors (2221) 740 1,358 2,098 14.1 Nursing and midwifery professionals (2230) 0 278 278 1.9 Skilled Agricultural and Fishery Workers (61) 0 0 0 0.0 Architects, Engineers and related professionals (214) 0 0 0 0.0 Teaching personnel (23) 0 0 0 0.0 Labourers In Mining, Construction, Manufacturing 5,899 114 6,013 40.4 And Transport (93) Total 7,936 6,949 14,885 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

140 Table D5 The stock of workers employed by specific occupations, 2004 - 2009 (continued) 2009 5. Third-Country Nationals Male Female Total % of Specific occupations Total Housekeepers and related workers (5121) 990 4,542 5,533 1.7 Cooks (5122) 32,675 3,383 36,058 11.2 Waiters, waitresses and bartenders (5123) 11,038 10,573 21,611 6.7 Child-care workers (5131) 0 7,030 7,030 2.2 Personal care and related workers not elsewhere 16,564 51,341 67,905 21.1 classified (5139) Medical doctors (2221) 26,221 9,615 35,837 11.1 Nursing and midwifery professionals (2230) 18,022 53,583 71,605 22.2 Skilled Agricultural and Fishery Workers (61) 2,784 84 2,867 0.9 Architects, Engineers and related professionals (214) 25,839 1,841 27,680 8.6 Teaching personnel (23) 16,657 22,265 38,921 12.1 Labourers In Mining, Construction, Manufacturing 7,263 0 7,263 2.3 And Transport (93) Total 158,052 164,257 322,309 100.0 Source: Labour Force Survey. Notes: There are no data available for Institution-based personal care workers (5132) and Home-based personal care workers (5133). There are no data available for number of unfilled vacancies.

141 Table D6 Flows by occupational category, 2004 - 2009, 2004 - 2009 Professional/Managerial Inflow Outflow Net 2004 All 175 -114 + 61 British 36 -74 - 38 Non-British 139 -40 + 98 2005 All 170 -137 + 33 British 40 -82 - 43 Non-British 130 -55 + 75 2006 All 154 -125 + 29 British 24 -80 - 56 Non-British 130 -45 + 85 2007 All 168 -112 + 55 British 30 -67 - 37 Non-British 138 -45 + 92 2008 All 187 -147 + 40 British 40 -79 - 39 Non-British 147 -68 + 79 2009 All 152 -128 + 23 British 36 -54 - 18 Non-British 115 -74 + 41

Manual and clerical Inflow Outflow Net 2004 All 131 -73 + 58 British 16 -47 - 30 Non-British 115 -27 + 88 2005 All 143 -83 + 60 British 23 -44 - 20 Non-British 119 -40 + 80 2006 All 136 -119 + 17 British 16 -56 - 40 Non-British 120 -63 + 57 2007 All 136 -101 + 34 British 15 -47 - 33 Non-British 121 -54 + 67 2008 All 125 -154 - 29 British 13 -46 - 33 Non-British 113 -108 + 5 2009 All 121 -111 + 10 British 24 -39 - 15 Non-British 97 -72 + 25 Source: ONS.

142