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.

Bob Seely: Ministers must revise the housing plan to give our cities the homes they need

8 Oct

Bob Seely is the MP for the Isle of Wight.

The 2019 Conservative Party manifesto made a clear pledge to Northern voters. We said: “We will use … historic investment to level up and connect this country, so that everyone can get a fair share of its future prosperity.”

I support levelling up 100 percent. Yet the Government’s housing targets now threaten that agenda and whilst, for the moment, it is suburban and shire Tory MPs who are speaking for their constituents and opposing these new and damaging targets, it is Red Wall and swing-seat MPs who perhaps have the most to lose.

Today, I am leading a Backbench debate in Parliament on the housing targets and the Housing and Planning White Paper. Politically, getting this policy right will help us to win the next election; getting it wrong will result in electoral pain for years, to say nothing of the wider economic, social and environmental ramifications.

If ‘levelling up’ means anything, it surely means an integrated Government plan to support infrastructure, jobs, and housing to revive the Midlands and Northern towns overlooked in recent decades, and to stop the endless drift of jobs and opportunities to the shires.

But, broadly speaking, the new housing algorithm undermines our Levelling Up pledge. It concentrates the biggest falls in housing targets in the urban North and Midlands – the very areas we pledged to level up, and the biggest increases in requirements in London and the South, where the wealth already is.

Worse, if infrastructure funding is going to follow housing, as the Government says, that means that money which Northern and Midland MPs hoped, indeed assumed, would be invested in their patches is instead going to the suburbs and shires.

In general, the case for Levelling Up is overwhelming. Just looking at population alone, whilst the population of the North East has grown by just two percent since 1961 in the South East it’s 28 percent, in North West it was seven percent and opposed to the South West’s 31 percent.

Northern cities fair even worse. Since 1961, the cities of: Newcastle, Sunderland, Hull, Liverpool, Manchester, Birmingham and Stoke, have all declined in absolute numbers, according to research from the House of Commons Library. Liverpool alone has seen an absolute population decline of over 300,000 people since 1910. The revival of Northern and Midland cities is vital both for those cities but also the suburbs and rural areas around them.

Yet the housing targets for major cities are set to decline. Targets for Liverpool and Newcastle are 48 per cent and 56 per cent lower than recent building rates. In Preston the new targets see a decline of 24 percent over the local plan for – 1,827 fewer homes over 15 years. In Doncaster the new targets see a decline of 22 percent over the local plan for a total of 4,039 fewer homes.

These are not isolated cases; there are more than 30 northern Local Planning Authorities with targets less than their local plan. This isn’t ‘build build build’, it’s ‘please don’t build, build, build’.

Whilst city targets are being lowered, the targets for suburbs and shires around them, including in the Midlands and North, will be raised substantially. So, over 15 years and compared with local plans, Manchester is expected to build less, but the suburbs and rural seats around it will be expected to build much more, and on greenfield too.

Let me give you another example. Targets for Nottingham city, where there are three Labour seats, fall by 3,700, whilst Nottinghamshire rises an additional 25,000 to 71,000 – the equivalent of 14 new small towns. Many of those major new developments will be in four historic Labour/Tory swing seats, three of which are now Tory but were Labour a decade ago. This is bad environmentally and economically, but also politically.

Whilst we don’t yet know the long-term impact, if any, of Covid, it is likely to speed-up the process of home working, which means less office space in cities and more space for housing. Therefore, the ‘rebalancing’ away from cities seems even more bizarre. So whilst these planning targets are bad for the South, they are equally damaging for the Midlands and the North.

The worst of all worlds is to hollow out our cities, urbanise our suburbs, and suburbanise the countryside, and in doing so, focus infrastructure spending away from where it is needed. I fear that this is what we may accidentally achieve, despite our good intentions.

This is not levelling up. It is concreting out. Tory shire voters will be furious. Red Wall voters will feel betrayed. This is lose/lose.

Conservative MPs need to work with Government to inject a dose of common sense to develop housing policy which supports home-owning aspiration as well as protecting and respecting the environment. The fate of our newly-elected friends in the North may, in part, depend on it.

Bob Seely: The Government must urgently re-assess its misguided housebuilding strategy

11 Sep

Bob Seely is the MP for the Isle of Wight.

Across rural shires and southern England, the Government is set to impose unachievable and damaging house-building targets which will undermine the levelling up agenda.

Environmentally, they will heap pressure on shires, whose infrastructure is already under strain. Economically, they will reinforce jobs and growth in the South when we have promised to level up the North. Politically, they will prove deeply unpopular.

This latest piece of self-induced, foot-shooting has come in the form of the new Standard Method for house-building. It accompanies the Government’s White Paper on housing, Planning for the Future. Whilst the White Paper itself will face debate and potential amendments, the new Standard Method can apparently simply be adopted. It will damage this Government.

MPs and councillors across Britain are slowly waking up to this. Ministers belatedly claim to be listening; they need to.

If ‘levelling up’ means anything, it means an integrated Government plan to support infrastructure, job creation and house building to revive the Midlands and North, especially towns overlooked in recent decades, and to stop the endless drift of jobs and people to the South. Yet this housing  strategy, as Neil O’Brien has outlined in his well-researched article, results in much lower targets in Northern cities, where we should be kickstarting revival, and significantly higher targets in rural and suburban areas.

This disjointed policy demands significant greenfield development. I know not a single Tory voter in the last election who voted for this. If this is an example of co-ordinated Government, it is a well disguised one.

The 12 biggest absolute decreases in housing targets by local planning authority on 2018/19 delivery are generally Labour controlled Midlands and northern cities and towns, with few exceptions: Salford (-59 per cent, -1882 dwellings per annum (dpa)), Birmingham (-27 per cent, -1131 dpa), Liverpool (-48 per cent, -1063 dpa), Leeds (-30 per cent, -1040 dpa), Southampton (-48 per cent, -784 dpa), Newcastle upon Tyne (-56 per cent, -978 dpa), Manchester (-30 per cent, -699 dpa), and Nottingham (-38 per cent, -559 dpa).

Instead, rural and suburban England is going to be hit. This will alienate both millions of Conservative voters and thousands of Conservative Councillors. Moreover, the withdrawal of powers from local Government suggested in the White Paper will undermine local democracy and the important role of councillors.

Council colleagues should know the following local planning authorities will all be required to more than double their 2018/19 delivery rate. This is likely to result in a tsunami of local anger from those who believed they could trust a Conservative Government not to concrete the countryside. It will fire up our political opponents and may suppress our support in future elections, beginning next May. Here is a modest selection, with hyperlinks:

Arun in Sussex (+239 per cent, +1454 dwellings per annum – dpa), Thurrock (+263 per cent, +1075 dpa), Tonbridge and Malling (+241 per cent, +1018 dpa), North Somerset (+134 per cent, +979 dpa), Teignbridge (+138 per cent, +888 dpa), Dover (+187 per cent, +833 dpa), Southend on Sea (+169 per cent, +832 dpa), Swale in Kent (+120 per cent, +809 dpa), Thanet (+246 per cent, +727 dpa), Havant (+261 per cent, +696 dpa), Isle of Wight (+199 per cent, +695 dpa), Canterbury (+162 per cent, +695 dpa),  Somerset West and Taunton (+129 per cent, +694 dpa), Blaby (+120 per cent, +626 dpa), Shepway (+134 per cent, +597 dpa), Basildon (+141 per cent, +480 dpa), Worthing (+198 per cent, +579 dpa) Sevenoaks (+222 per cent, +565 dpa), Reigate and Banstead (+104 per cent, +556 dpa), Mendip (+108 per cent, +552 dpa), Ashfield (+171 per cent, +513 dpa), Harborough (+170 per cent, +509 dpa) Waverley (+148 per cent, +499 dpa), Bromsgrove (+244 per cent, +492 dpa), Hinckley and Bosworth (+109 per cent, +464 dpa), Fenland (+114 per cent, +450 dpa), Lewes (+126 per cent, +446 dpa), Epping Forest (+104 per cent, +442 dpa), Epsom and Ewell (+266 per cent, +439 dpa), Three Rivers (+292 per cent, +438 dpa), Oxford (+262 per cent, +406 dpa), North Hertfordshire (+181 per cent, +403 dpa), Guildford (+208 per cent, +381 dpa), New Forest (+102 per cent, +395 dpa), Eastbourne (+274 per cent, +356 dpa), Cannock Chase (+146 per cent, +341 dpa), Forest of Dean (+125 per cent, +338 dpa), Rochford (+124 per cent, +324 dpa), Tandridge (+118 per cent, +289 dpa), Broxtowe (+128 per cent, +275 dpa), Hastings (+146 per cent, +269 dpa), Gosport (+461 per cent, +254 dpa), North East Derbyshire (+121 per cent, +230 dpa), Adur in Sussex (+188 per cent, +213), Oadby and Wigston (+132 per cent, +123 dpa), and Rossendale (+153 per cent, +164 dpa).

(A full list is available here.)

Take my constituency, the Isle of Wight; the proposals will see our target increased by over 50 per cent. Half the Island is designated as an Area of Outstanding Natural Beauty, yet we will be ordered to build more houses per year than either Portsmouth or Southampton, both cities with major infrastructure and services, and populations almost 70 per cent larger. This is just nonsense.

Why? First, our services and infrastructure are already overwhelmed with the increases we have already had. We have basically the same Victorian country lanes we had two centuries ago, minus most of our railways. Second, we are dependent on a tourism economy that crammed roads and shoe-horned housing estates will undermine. Third, our island building industry produces between 250-400 homes per year. It can’t build more. Our current targets are already unachievable. The Government might as well order the Island’s Council to develop a Moon Landing programme for all the likelihood of achieving these new targets.

It won’t help our young, either. Increasing in housebuilding do not necessarily result in increased affordability. (The FT explains why here.) Factors such as low interest rates, slow wage growth, and a need for the right type of homes are key. As with many other parts of the UK, we need one and two bed homes for residents, built in sensitive numbers in existing communities, with rent-to-buy schemes to support the young. We get three- and four-bed, generic (sorry, ‘superior’) housing in soul-destroying, low density, greenfield estates because that is what suits developers. From all sides of the political spectrum, people are fed up.

The Government’s Standard Method produces unviable, undesirable targets for swathes of rural England. What is being proposed is not levelling up, but a levelling down – from the cities to the shires. It will cost us economically, environmentally and politically. It will not help young people. It will worsen quality of life. It is not what many of our electorates voted for.

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.