Nicky Morgan and Damian Collins: Ministers must make Britain’s world-leading tech sector a top priority

3 Jun

Nicky Morgan is a Conservative peer and former Digital Secretary. Damian Collins is MP for Folkestone and Hythe and former Chair of the DCMS Select Committee.

In spite of recent news there is definitely now light at the end of the Covid-19 tunnel. With the Government continuing to deliver on its ambition to offer a vaccine to every adult by July, we can start to look at policies beyond Covid.

As co-Chairs of the recently formed Conservative Friends of Tech, we strongly believe one of the priorities for Government should be to leverage time, energy, and policy to developing and strengthening our tech sector, including sectors such as fintech and ‘govtech’.

There was a record £10.1bn investment into UK tech companies in 2019. Between 2010 and 2018, the tech sector GVA (gross value added) grew nearly six times as fast as that of the UK economy as a whole. The UK’s tech and digital sector is worth more than £400 million a day to the UK economy.

As respectively a former Digital Secretary and DCMS Select Committee Chair, we know that the UK remains one of the best countries in the world for tech, and continues to attract the best and brightest entrepreneurs and minds in the world. Eighty UK tech companies are now ‘unicorns’ (companies with valuations over $1bn) – more than any other country in Europe.

The UK also has 136 potential unicorns –  companies with a value of $250m to $800m. This is more than twice as many as Germany and France, the countries with the next largest pipeline of future unicorns (61).

It is expected that the tech sector will have 100,000 job openings a month by the end of June this year, and we believe that the many thousands of highly skilled jobs that the sector is creating will be a vital and valuable part of our economic recovery, including for those who have been furloughed and have returned to work to find their previous jobs or sectors changed by the pandemic.

According to recent reports, ten per cent of all current UK job vacancies are for tech jobs, demonstrating not only the value and importance of a sector that already represents nine per cent  of the UK workforce, but highlights the potential value the sector will continue to add over the coming years.

In the aftermath of both Brexit and a global pandemic that has impacted, and will continue to impact, the way we work and recruit people, the challenges for the next few years will be ensuring that both major tech companies and vibrant start ups can hire the talent that they need. As the British tech sector is a global centre of excellence, it needs access to the best talent both at home and from overseas, to facilitate agile and ambitious growth. According to research by The Entrepreneurs Network, 49 per cent of the 100 fastest growing start-ups have at least one foreign-born co-founder.

The pandemic triggered an outflow of around 700,000 foreign-born residents from London alone. This presents challenge to the tech sector, although employees have grown used to working remotely.

But as the UK, and the world looks to find a ‘new normal’ post-pandemic, we should use the opportunity to assess our immigration policy and student visas to ensure we really can attract the brightest and best people to the UK – and turbocharge the sector and the economy.

Oxford, Cambridge and Imperial college account for three of the seven top-ranked universities in the world with regard to their computer science departments, and these are great places to nurture talent for tech start-ups. We believe the Government should preserve funding for top-performing universities so as to enhance their attractiveness to entrepreneurially-minded students, and to create and embed an ecosystem of learning, development and symbiosis between tech and education.

Of course, immigration policy and nurturing future talent are only two of the many policy areas we, and the Government need to focus on if we wish to truly support, and catalyse the tech sector. We also need to ensure fair competition within the tech sector to give start-ups and young businesses the chance to thrive

. This means empowering the competition authorities to prevent abuse of market power from big tech platforms, and requiring all firms to pay their share of taxes relevant to the value of the businesses they do in this country.

We hope you will join us at CFTech alongside industry leaders, politicians, think tanks, ministers, and academics, so we can discuss how best to support and galvanise the sector.

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.