Tony Smith: Turning the tide on migrant boats

3 Jul

Tony Smith is a former Head of the UK Border Force and Director of Ports and Borders in both the UK and Canada. He is now Managing Director of Fortinus Global Ltd, and Chairman of the International Border Management and Technologies Association.

Rarely a day goes by without news of more migrants crossing the English Channel from France to claim asylum. What began as a trickle two years ago has now become a stream. Over 1800 came across in 2019. Over 160 arrived in a single day on 3rd June. At current rates, the 2020 figure will double last year’s total; it could even go higher still. Yet only around six per cent are returned to France.

Those who said that these waters were too difficult to navigate in unseaworthy vessels have been proved wrong. We have seen arrivals in all forms of makeshift craft, even inflatables and canoes. So how do we turn the tide, and stem these illegal flows?

This is a complex problem. There are significant challenges raised by international law including 1951 Refugee Convention, the UN Convention on the Law of the Sea (UNCLOS), the Convention for Safety of Life at Sea (COLAS), and the Convention on Maritime Search and Rescue (SAR).

Following media reports that French vessels were “escorting” migrant boats into British waters in May, Priti Patel announced that she would change international law to close the Channel loophole; but any change in international law needs international agreement.

Article 98 of UNCLOS encourages neighbouring states to establish regional arrangements for search and rescue at sea. Examples include joint patrol vessels, or the placement of officials from one jurisdiction on board the vessel of another.

So there is no reason in international law why the British and French governments could not introduce joint SAR patrols. They would have to meet the requirements on international law; but – crucially – refugees and asylum seekers can be taken to any place where there is no risk of their life or freedom being threatened in accordance with Article 33(1) of the Refugee Convention, on the principle of “non refoulement”.

So subject to mutual agreement, we could establish an integrated UK/French border patrol to rescue migrants at sea and bring them to a place of safety; and as both countries are signatories to the 1951 Convention, that could be to a port on either side, and not necessarily to the country whose vessel happens to rescue them.

Of course, this needs a political agreement with France. Some may say this is not achievable. Maybe not. But in 2002, the total UK asylum intake figure rose to over 100,000, with the vast majority arriving from France. To stem the flows, the UK and France agreed a bilateral Treaty (Le Touquet) in to establish “juxtaposed controls” whereby officers would conduct passport inspections prior to boarding ferries.

As these inspections were “extra territorial”, asylum claims were excluded. This led to a far harsher reduction of asylum claims from France than the numbers we see on the migrant boats today. In my experience, successive French governments have been prepared to work with UK border enforcement agencies to disrupt and deter irregular migration on the cross-channel routes. They don’t like human smugglers any more than we do. This suggests that there is scope for further bilateral agreement to counteract the maritime threat.

Although France is a “safe third country”, the current Dublin Convention trumps safe third country rules. To return as asylum seeker to another member state, the receiving state has to prove that an asylum claim had already been made in the other state.

Given that nearly all migrants are undocumented on arrival, this evidence is rarely available – and accounts to a great extent for the very low returns rate. As the UK departs the EU, it will no longer be party to the Dublin Convention. A new “safe third” agreement is needed.

There will always be migrants in France who want to come to the UK. Some may have legitimate reasons for doing so – for example, those with family connections here. To meet this demand, the UK could offer a legitimate migratory route to the UK for specific categories of persons via our offices in France; thereby reducing the incentive for illegitimate routes and simultaneously disrupting the smuggling supply chains.

I hope that the Government’s current strategy to encourage better enforcement in France pays off. It is certainly having an impact. But if we believe that this could escalate into a crisis like the one we saw back in 2002, we will need a more fundamental and radical approach to tackling the problem.

That means reaching a new international agreement France on joint patrols, search and rescue, and safe returns whilst simultaneously exploring alternative legitimate offshore processing routes for those with a genuine case to enter. Then – and only then – will we finally be able to turn the tide on migrant boats and defeat the maritime threat to our borders.

Rob Sutton: Introducing the top 50 Conservative MPs on Twitter

29 Jun

Conservative MP Twitter power rankings: the top 50

Rob Sutton is an incoming junior doctor in Wales and a former Parliamentary staffer. He is a recent graduate of the University of Oxford Medical School.

Amongst the social media giants, Twitter is the primary battleground for political discourse. It’s also one of the key avenues by which MPs convey their message, and has near-universal uptake by members in the current House of Commons.

The effectiveness with which Twitter is utilised varies considerably between MPs, but it is difficult to compare like-for-like. How does one take into account the differences between, for instance, a freshman MP and a veteran Cabinet member? Length of service in Parliament and ministerial rank give a considerable advantage when building a following.

In this article, I have compiled a power ranking of MPs in the current Parliament, with the top 50 shown in the chart above. The MP’s follower count was adjusted by factoring in their previous experience, to better reflect the strength of their following and their success at engagement on the platform.

Being Twitter-savvy is about more than just a high follower count: any Secretary of State can achieve this just by virtue of the media exposure their office brings. Building a Twitter following based on thoughtful commentary and authentic engagement requires skill ,and can be achieved by members across all Parliamentary intakes and ranks of Government.

Though the top 10 is still dominated by MPs holding senior ministerial offices, the composition of the list beyond it is far more variable. A number of prominent backbenchers are in the top 20, and four members from the 2019 intake make the top 50, beating longer-serving and higher-ranked colleagues.

I hope that this list serves as recognition of the skill and contribution by Conservative members to public debate and engagement, beyond ministerial duties which so often dominate any mention in the media.

Building a model of Twitter power rankings

Success is judged by number of followers, with higher follower counts indicating greater influence on Twitter. The follower count was adjusted using three key parameters:

  • The number of years since an MP was first elected to Parliament.
  • The number of years the MP’s Twitter account has been active.
  • Their highest rank within Government achieved since 2010.

Higher values for each of these would be expected to contribute to a higher follower count. I built a model using the open-source Scikit-Learn package, and fitted it to data from the current Parliament.

The model was then used to predict how many followers a given MP might expect to have based on these three factors. The steps taken to produce a final “Twitter power score” were thus as follows:

  • Using these three factors, multiple linear regression was used to calculate the expected number of Twitter followers an MP might have.
  • Their true follower count was divided by the expected follower count to produce a single number which represented the MP’s performance at building a following.
  • Finally, a logarithm was taken of this ratio to make the number more manageable and to produce a final Twitter power score.

The final step of taking a logarithm means it is easier to compare between MPs without those who have very high follower counts (such as Boris Johnson) making the data difficult to compare, but it does not affect the order of the ranking.

Compiling the data

Having decided which factors to correct the model for, I collected the required information. All three factors were easy to find reliable sources for. The Twitter page for each MP displays the date the account was created, and the Parliamentary website provides the date of their first election to Parliament and previous government posts.

Members who are newly returned to the backbenches following governmental duties (such as Sajid Javid and Jeremy Hunt) are scored at their highest government rank since 2010 to recognise this. I was able to find the Twitter accounts and required information for 319 Conservative MPs who were included in this ranking.

To build a model based on this data required incorporating the highest government rank numerically. To do this, I assigned scores according to their rank. These grades recognised their relative seniority and media exposure associated with the office, with higher scores assigned to more senior positions:

  • Prime Ministers, Secretaries of State, Speakers, Leaders of the House and Chief Whips are scored 3.
  • Ministers of State, Deputy Speakers and Deputy Chief Whips are scored 1.
    Parliamentary Under-Secretaries of State, Parliamentary Private Secretaries and Whips are scored 0.5.
  • Backbenchers score 0.

When assigning these values, I considered the typical sizes of follower counts of MPs in each category. When comparing Secretaries of States to Ministers of State, the median follower count is around twice the size, but the mean follower count is around eight times the size, as a handful of very large follower count skews the results upwards.

Deciding on weightings requires a (somewhat arbitrary) decision as to which measures to use when comparing between groups, and the scores I decided on were ultimately chosen as a compromise across these different measures, which produced stable results when used in the model.

It is also worth explaining why Prime Ministers are grouped with Secretaries of State, despite the far higher media exposure and seniority of their post. When deciding on the respective weighting for different levels of government post, a sufficiently large pool of MPs was needed to produce a meaningful comparison. The only data points for comparison of Prime Ministers are Boris Johnson and Theresa May, so it is difficult to give them their own weighting without it being either unreliable or arbitrary.

While grouping them with Secretaries of State and other senior positions might be perceived as giving them an unfair advantage in the weighting, I felt it justified given these challenges in determining the “fair” weight to assign them. With this done, I had three parameters for each MP on which to build a model to calculate the expected number of Twitter followers.

Calculating the number of expected Twitter followers

I built a model to calculate the expected number of Twitter followers using the Scikit-Learn, a popular machine learning package in the Python programming language. The model used multiple linear regression to fit the input parameters to the known follower count.

The input data was prepared by removing extreme high outliers in the data which skewed the fit toward high numbers and away from the vast majority of MPs before fitting. Once fitted, an “expected value” of Twitter followers could be calculated for each MP, based on the year of their first election to parliament, the number of years on Twitter and their highest government rank since 2010.

Including more parameters increases the ability of the model to describe the difference between MPs’ follower counts (the variability). By increasing the number of input variables included in the model, more of the variability is captured:

  • One variable captures between 20.3 per cent and 36.1 per cent of the variability.
  • Two variables capture between 39.1 per cent and 43.1 per cent of the variability.
  • All three variables capture 48.7 per cent of the variability.

These three variables are therefore responsible for almost half of the variation between MPs in their follower counts. The remainder of the variability is likely due to a range of factors which the model does not include, of which the MP’s Twitter-savviness is of particular interest to us. I discuss these factors further below.

Limitations in the model

There are multiple other parameters which could be included in future iterations which I did not include in this model. In particular:

  • Membership or Chairmanship of Select Committees.
  • Previous election to a council, assembly, devolved legislature or the European Parliament.
  • Membership of the Privy Council.
  • Government positions prior to 2010.
  • Prominent positions within the Conservative Party, such as the 1922 Committee or European Research Group.
  • Twitter-savviness and effectiveness of their comms team.

Another limitation was not accounting for the perceived relative importance of various governmental departments: a Great Office of State or Prime Minister is scored the same as any other Secretary of State. The difficulties involved in ranking governmental departments were beyond this first model. The length of service in a given government post was also not considered.

Finally, the choice of model to fit the data may not be the optimal choice. Multiple linear regression assumes, per the name, that the distribution is linear. But the large outliers might be better described by a power law or Pareto distribution, or the non-linearities of a neural network.

During next week, ConservativeHome will produce profiles of six individual MPs who have performed notably well in the power rankings, and who reflect the contributions brought by members beyond their ministerial duties, if they have any.