The ten Conservative MPs who voted against the Health and Social Care Levy Bill at Third Reading

15 Sep
  • Baron, John
  • Chope, Christopher
  • Davies, Philip
  • Davison, Dehenna [pictured]
  • Drax, Richard

 

  • Everitt, Ben
  • Fysh, Marcus
  • Mackinlay, Craig
  • McVey, Esther
  • Redwood, John

There were 44 Conservative abstentions – which is in the same territory as last week’s vote on the same issue.  However, the usual cautionary note applies: though some Tory backbenchers will have refused to support the Bill, others will be abroad, ill, or absent for other reasons.

Marcus Fysh: The challenge of coronavirus shows the need for streamlined decision-making in Somerset

26 Feb

Marcus Fysh is the Member of Parliament for Yeovil

Regular readers will be aware of the ongoing debate around the future of local government in Somerset. This is, of course, important to the future of local democracy and public services in our county, particularly as we move into the post-pandemic phase. Creating a fit-for-purpose council structure will be crucial to helping communities to recover and in attracting new investment and jobs.

To do this without learning the lessons from the last year would be a huge missed opportunity. Dealing with the virus on a traditional silo-based model, with multiple artificial and ceremonial boundaries, was never going to work given the scale of the challenge. There have been unprecedented levels of collaboration between public services in the county, notably with health and care services coming together effectively on the current county boundary model. This model has proven invaluable in this time of crisis and is far more appropriate than reverting back to the old silos or, as the Lib Dems want, creating an even more complex structure that would hinder recovery.

That’s why I strongly believe that a single unitary authority to replace the county council and the four district councils in the county is the best option for the future, reflecting the lessons learnt in the last year.

Not only would a single unitary model deliver significant annual savings that could be reinvested into front line services; it would also join up and improve local service delivery with key partners, including health and police (both of whom support a single unitary). Aside from these real-world savings, achievable through lack of duplication and better management, the proposal would also give a real boost to local decision-making, enhancing the role of parish and town councils to make sure that people’s voices can make a real difference at a more local level. The people of Somerset should have one well-run and competent organisation, as has worked well in other areas that have recently adopted this approach.

The other proposal, led by the county’s Lib Dems, is a recipe for chaos and confusion that would create an even more complex structure than we have now. It would split the county in half, creating two small competing unitaries in the east and west of the county, outsource children’s services to a separate company, create a further shared services company, plus a combined authority. The existing five councils would, therefore, be replaced with five new bodies each with their own staff, budgets, back office, priorities, and so on.

This sadly has more to do with trying to keep a motley coalition of competing interests and personalities together who oppose a single unitary than trying to deliver better local services for our residents. It completely misses the point of what our county needs; relies on Lib Dem administrations and ideas that should have been put out to pasture years ago; would disrupt existing pan-county services including those for the most vulnerable; and would fail to learn the lessons from tackling COVID.

For the reasons above, and to put into practice the lessons of the last year, we need to move to a single unitary authority in Somerset as soon as possible, helping our communities to recover, whilst delivering on what our residents want: better local services and better value for money.

The forty-two Conservative MPs who voted against the Government on the 10pm curfew

13 Oct
  • Ahmad Khan, Imran
  • Amess, David
  • Baker, Steve
  • Baldwin, Harriett
  • Blackman, Bob

 

  • Blunt, Crispin
  • Bone, Peter
  • Brady, Graham
  • Chope, Christopher
  • Clifton-Brown, Sir Geoffrey

 

  • Daly, James
  • Davies, Philip
  • Davis, David
  • Davison, Dehenna
  • Doyle-Price, Jackie

 

  • Drax, Richard
  • Fysh, Marcus
  • Ghani, Nusrat
  • Green, Chris (pictured)
  • Hunt, Tom

 

  • Latham, Mrs Pauline
  • Loder, Chris
  • Loughton, Tim
  • Mangnall, Anthony
  • McCartney, Karl

 

  • McVey, Esther
  • Merriman, Huw
  • Morris, Anne Marie
  • Redwood, rh John
  • Rosindell, Andrew

 

  • Sambrook, Gary
  • Seely, Bob
  • Smith, Henry
  • Swayne, rh Sir Desmond
  • Syms, Sir Robert

 

  • Thomas, Derek
  • Tracey, Craig
  • Vickers, Matt
  • Wakeford, Christian
  • Walker, Sir Charles

 

  • Watling, Giles
  • Wragg, William

Plus two tellers – Philip Hollobone and Craig Mackinlay.

– – –

  • Seven Tory MPs voted against the Government on renewing the Coronavirus Act.
  • Twelve voted against the Government over the rule of six.
  • Now we have 42 this evening – enough to imperil the Government’s majority in the event of all opposition parties that attend Westminster voting against it too.
  • Fifty-six signed the Brady amendment, but it was never voted on, and wasn’t a measure related directly to Government policy on the virus.
  • We wrote last week that Conservative backbench protests would gain “volume and velocity”, and so it is proving.
  • There’s a strong though not total overlap between these lockdown sceptics and Eurosceptics.
  • We count eight members from the 2019 intake – and a big tranche from pre-2010 intakes.
  • Chris Green resigned as a PPS to vote against the measure.
  • He’s a Bolton MP and there’s clearly unhappiness there about these latest restrictions.

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.