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mySociety and SpendNetwork have been working on a project for the UK Government Digital Service (GDS) Global Digital Marketplace Programme and the Prosperity Fund Global Anti-Corruption programme, led by the Foreign & Commonwealth Office (FCO), around beneficial ownership in public procurement. This is one of a series of posts about that work. 

There are three steps to working with beneficial ownership data: collection, verification and analysis. These three areas interact – how and when data is collected affects how viable different methods of verification are, and both of these in turn affect what forms of analysis are possible.

While collection of beneficial ownership data does not have to be part of the procurement process, (for example, if there is already a national register) requirements for bidding or winning public contracts are good pressure points to require disclosure. The following diagram shows a bird’s eye view of how ownership data (green lines) might be collected by different government agencies as part of the corporate lifecycle and the government contracting process (click for more detail).

In an ideal world, beneficial ownership information would be available and accurate at all of these points. But realistically, choices must be made over when and where to introduce beneficial ownership data collection and how to resource verification. Such choices will have an effect on the scale and timeliness of the data collected, however, as we can explore with the following diagram:

For instance, if a goal is to check for bidding cartels in the process of judging the bid, this information can be collected at any point: when companies are formed; when they register as a supplier; or when they make a bid. If companies submit multiple bids, it reduces duplication if information is collected sooner. However, this also increases the potential time between submission and analysis, and so requires an update process to avoid information becoming out of date.

Collecting at different points also makes a difference to the size of the database. Each successive capture point is collecting a smaller sample of organisations. This affects analysis in two ways: scope and accuracy. The more companies covered in the dataset, the more forms of analysis become possible. If you have only collected information on bid winners, you cannot investigate bidding cartels. If your collection only includes the beneficial ownership of registered suppliers, you cannot identify the ‘sibling’ entities (which are not in the direct ownership chain of a company, but are owned by the same owners)  in a corporate structure that might contain hidden debts.

While collecting data about more companies does not inherently make data more inaccurate, there is an indirect effect in that collecting more information raises the overall cost of verification.  Collecting information about all companies creates a much larger dataset to verify than just those who win contracts. If verification resources are not increased accordingly, the dataset will have a much larger scope, but be less accurate, and so the resulting analysis may be less useful.

This inaccuracy may have a higher effect on fraud/anti-corruption analysis than its overall incidence in the dataset. This is because while some errors will be accidental, some will have been deliberately introduced to disguise ownership. Analysis that is only possible with large amounts of data may not even really be possible if the database is not supported by a strong verification system.

To provide two different models, the UK’s Persons of Significant Control (PSC) register requires declarations as part of a company’s annual statement. With a few exceptions, it includes all companies registered in the UK. This data is submitted by the company without verification, as examining suspicious statements is a resource intensive problem across the entire jurisdiction (see the recent Global Witness report for a description of the verification problems in the UK PSC register).

The Slovakian Register of public sector partners (RPVS) requires beneficial ownership information be submitted only before a high value contract is awarded and so has a much narrower scope. However, there is a much stronger verification process, with third parties (generally legal offices) submitting the information and the process they used to reach it, with an in-country individual held legally responsible for the accuracy of the data. Methods and useful concepts in the verification process are explored in more detail in an OpenOwnership briefing.

Depending on the specifics, smaller databases (with verification) may lead to more basic—but more accurate—analysis. The calculation made in Slovakia for instance, is that there is less need for using data as part of the procurement process if the post-award checks are very good, because raising the chances of being caught raises the costs of cheating. On the other hand, this is then missing out on the prospect of identification of cartels. Disclosures may be accurate while the procurement process is still distorted.

Each expansion in the number of companies included does not expand the process in the same ways. While smaller registers may be cheaper to verify, new forms of analysis may open up with small increases in size. For instance, if the overall number of companies participating in bids is not much larger than those winning bids, the additional compliance costs may be negligible and allow the possibility of cartel analysis.

See all posts in this series.

Header image: Omar Flores on Unsplash

mySociety and SpendNetwork have been working on a project for the UK Government Digital Service (GDS) Global Digital Marketplace Programme and the Prosperity Fund Global Anti-Corruption programme, led by the Foreign & Commonwealth Office (FCO), around beneficial ownership in public procurement. This is one of a series of posts about that work. 

The idea of beneficial ownership is meant to address the problem that the official directors and board of a company may be different from the true owner or controller.

Without knowing the true owners of a business, you cannot understand who benefits from or controls its activities. In a procurement context, without beneficial ownership information about suppliers, it can be difficult to detect organised corruption or conflicts of interest.  Greater knowledge of ownership and control can give greater insight into supply chains and product quality. In the case of government contracts, collecting and using beneficial ownership data can have a very real impact on ensuring state funding is directed towards legitimate, high quality services and infrastructure for citizens.

Someone may not even be an ‘owner’ in the sense of having a significant proportion of shares to have ‘control’ over it. They might own no shares but still exercise control through a right to appoint board members. In most cases they would still be considered beneficial owners of the company.

Where this becomes interesting is when companies are owned not just by ‘natural’ (real) people, but also by other companies. For some companies, this can result in long chains of ownership, with many levels of companies owning other companies. But sooner or later, all ownership chains must terminate in real people, not corporate entities – those people are the beneficial owners.

Tools for visualising beneficial ownership structures are still quite varied, but most attempt to represent ownership as a network, with companies and people as nodes:

An alternative approach is to think about only the ultimate owners in a chain. This can be particularly useful when you need to make quick decisions about who owns or benefits from a given company, regardless of how many ‘steps’ they are removed from the company itself:

Perfect vs practical definitions

A broad definition of beneficial ownership (such as ‘deriving significant benefit from or having control over a company‘) is useful for an investigator trying to understand whether specific individuals can be said to be beneficial owners of an organisation. It is less useful when an organisation is being asked to declare who their beneficial owners are. This requires concrete disclosure requirements that may approach, but are likely to fall short of a broad definition. For instance, it might be decided that stockholders who have more than 25% of voting rights qualify for disclosure. In the terminology used by the World Bank/STAR Puppet Masters report, this is a “formal” rather than “substantive” approach to understanding the beneficial ownership of companies.

When talking about beneficial ownership, it is important to keep in mind this distinction between the concept of a beneficial owner and the inherently imperfect ways of identifying them. Better management of procurement risks means knowing more about who benefits from a company receiving a contract. But on the other side, the people hoping to subvert the process will want to maintain secret ownership ties in order to control or benefit from the company.

Closing the gaps in knowledge with additional beneficial ownership disclosure addresses the current state of evasion, but not how dishonest actors will react to new requirements. Introducing new requirements will address some amount of fraud and corruption, but also creates a strong incentive to find new ways to conceal conflicts of interests. This arms race dynamic means there is no one ‘good’ formal definition of beneficial ownership, but a number of different criteria that need to react to the practices of concealment in evidence in a country at a particular time.

As such, the best way to think about the long term impact of beneficial ownership on public procurement is not as a silver bullet, but as a tightening net. Future escalations may involve changed definitions, or improving the means by which information is validated. Underlying tools and standards need to be flexible to a range of national contexts, as well as a potential for change over time.

Beneficial ownership is part of a solution to several different problems

Several different frameworks promoted by inter-governmental bodies or international transparency/anti-corruption groups push towards more collection of beneficial ownership information.

The Extractive Industries Transparency Initiative (EITI) required as part of their 2016 standard that all participating countries mandate the disclosure of beneficial owners within extractive industries (oil, coal, gas, mineral extraction), and recommend publication in public registers or through the country EITI report.

The Financial Action Task Force (FATF) 2012 recommendations include the importance for financial institutions of discovering the beneficial owner as part of customer due diligence when establishing a new business relationship, and apply enhanced diligence if a beneficial owner is also a politically exposed person (PEP). While not calling for an open register, they do recommend that there are timely forms of accessing accurate beneficial ownership available for ‘competent authorities’.

The Open Government Partnership (OGP) supports the Beneficial Ownership Leadership Group with the aims of strengthening disclosure requirements and verification processes,  supporting a common data standard and allowing public access to enable citizen monitoring. Over 40 countries (including Mexico, South Africa and Indonesia) have incorporated commitments related to beneficial ownership transparency in their OGP plan.

On the practical side of how international ownership data should be processed and stored, OpenOwnership is an organisation with the goal of making beneficial ownership data more widely available through technical development, partnerships and research. They are the key developers of the BODS data standard and host a global open registry of beneficial ownership data.

More directly related to public procurement, as part of their COVID-19 response, the International Monetary Fund (IMF) has asked countries requesting emergency assistance to make commitments to publish information on the contracts with and the beneficial owners of companies benefiting from the emergency funds.

Ownership in public procurement

The problem beneficial ownership data can address in public procurement is corruption or subversion of the procurement process, but it also has a bearing on procurement efficiencies, risk profiling and enactment of preferential procurement policies.

Making beneficial ownership data available to procurement officers helps them discriminate between bidders for work in a current procurement process. For instance, a problem described by several interviewees in our research on this area is bidding cartels. This is where multiple bidders (who are in reality controlled by the same owner) coordinate to drive up the price and raise the chances of winning. Knowing more information about the ownership of the companies in this bidding cartel would make it easier to detect.

Better visibility of who is benefiting from public procurement contracts can be beneficial even when companies are behaving perfectly within rules. Entirely legitimately, a set of apparently independent companies may have won many bids. However, in reality these are part of a broader group with a set of common owners. Beneficial ownership data can make it easier to understand the connections between these companies (either because chains of corporate ownership have been revealed, or the final owners directly revealed). This can allow identification of where procurement contracts are ultimately flowing. Where beneficial ownership data is broadly available for organisations, this also allows identification of other businesses in which owners have an interest. This can be used to risk profile broader corporate structures.

Explicitly collecting the data required to catch violations of existing rules can also create a chilling effect, by making potential bad actors aware of the scrutiny that may be given to the information, especially if combined with more effective enforcement. A government official (elected or otherwise) with power over the procurement process may have significant involvement in a company bidding for a contract, but this fact would be undeclared and invisible on official paperwork. Greater visibility of the beneficial owners of these companies leaves fewer places to hide, and raises the risk of detection and costs of attempting to subvert the process.

See all posts in this series.

Last year, mySociety worked as part of a consortium to deliver three local citizens’ assemblies in the UK. This was as part of the Innovation in Democracy Programme, which was a joint project between the Department for Digital, Culture, Media & Sport and the Ministry of Housing, Communities & Local Government. The goal was to trial new ways of involving citizens in local decision making. Alongside Involve, the Democratic Society and the RSA we investigated how digital tools and methods could be used as part of deliberative processes. 

As one of the final parts of this programme, the RSA has published a handbook about what we learned, and case studies of each of the assemblies:

• How to run a citizens’ assembly: a handbook for local authorities based on the Innovation in Democracy Programme 
•  Innovation in Democracy Programme case studies

The RSA have also blogged about the handbook.

mySociety’s part in this project was primarily to investigate how best to use digital tools to complement an in-person citizens’ assembly. We published this as two sets of guidance:

• Citizens’ Assembly websites: practical guidance
• Digital tools for Citizens’ Assemblies

The first is a practical exploration into what materials are best to prepare and show on a website for a citizens’ assembly; the second looks at how tools can be used to bring evidence and external contributions into the debate, without diluting the representative nature of how participants were selected.  

The handbook also describes an approach we helped with at the assembly in Test Valley. Discussions at pre-evidence sessions were recorded in argument maps for reference during the event. 

This thinking has led into our work on the UK’s climate assembly helping proceedings, evidence and outputs to be transparent and available to everyone who is interested. 

Since that project, for fairly obvious reasons, many organisations that previously focused on offline deliberation are now looking to pivot rapidly into how to run online deliberation. Involve has a good guide as to the range of tools and approaches that can be useful.

We are continuing to research and think about how citizens can be more integral to decision making, and what the appropriate role of technology is in making this happen. You can subscribe to our research newsletter to hear more: 

The House of Lords Select Committee on Democracy and Digital Technologies has released its report: Digital Technology and the Resurrection of Trust. 

mySociety submitted written evidence last year, and our Head of Research, Dr Rebecca Rumbul, gave evidence in February 2020.

The recommendations can be seen online, but we were pleased to see a point taken up from our friends at Democracy Club, that there should be more open data about elections, candidates and polling stations so focus can be on providing that information to citizens rather than sourcing it. 

This recommendation in particular reflects mySociety thinking:

Technology can play an important role in engaging people with democratic processes. Parliament and government, at all levels, should not seek to use technology simply to reduce costs, and must ensure that appropriate technology is used to enhance and enrich democratic engagement.

Through our research and practical work in the last few years, we have been concerned with finding the appropriate place for technology in addressing problems. 

Digital solutions have enormous potential to scale cheaply (and have powerful uses in democratic transparency), but also have uneven engagement and require different skill sets to manage. Where digital tools allow more efficiency, this should enable resources to be redirected towards improving the overall quality of the exercise. 

As we argued last year when we were looking at digital tools and democratic participation:

Where using a tool can bring down other costs, those funds can be redeployed towards outreach and other real world activities to broaden participation. The use of digital tools must be understood as part of the whole system, which involves gauging not just what the tool does, but the effort and time it can free up to address other priorities.

The problem of citizens and communities being excluded from the political process will rarely be fixed by a digital tool alone, but when correctly aligned with democratic efforts to involve people in decision making,  they can be a powerful part of the solution. 

Photo credit: Photo by Ciel Cheng on Unsplash

This blog post is part of a series investigating different demographics and uses of mySociety services. You can read more about this series here. 

I saw a comment on Twitter the other month along the lines of: “is civic tech too boring? It’s dominated by reporting potholes to councils”.

As someone working in civic tech I find this terribly unfair because civic tech is about so much more than that! For instance, we also report dog poo to councils. 

But it’s certainly true that there are a lot of potholes involved. It’s the largest use of FixMyStreet, representing a quarter of all reports. People have submitted over 361,000 reports and over 54,000 photos of potholes. As a result, while the FixMyStreet database represents a fraction of all potholes, it represents one of the largest datasets of pothole reports covering the whole country. 

And while it’s easy to think of potholes as the obsession of people pointing at roads in local papers, they are a serious problem. There are a lot of them, they appear everywhere, cause problems on roads when people try to avoid them, and damage when they don’t.  For cyclists, potholes can be fatal. 

Given that, what does FixMyStreet data tell us about potholes?

How many potholes are reported through FixMyStreet?

Up to the end of 2019 there have been 423,736 potholes or road surface defects reported through FixMyStreet (either .com or a cobrand), with 90,000 reported in 2019. Working from a rough figure of 675,000 actual pothole reports a year, this is around 13% of all potholes reported in the UK. 

A feature of reports to FixMyStreet  is that, while the majority of reports are made by men, there are different ratios in different kinds of reports and categories are often gendered in terms of reporters. Deriving the gender of the reporter from their name, potholes and road surface defects are mostly reported by men, and disproportionately more than the site in general. 

As explored in a previous post, this isn’t an essential gender difference but is likely to result from men having far more cause to encounter potholes. In 2013, men in the UK were on average driving twice as many miles per year as women. 

People who report potholes are more likely to have reported multiple problems than other reports. Most pothole reports are made by people who have reported multiple reports and represent a smaller proportion of single report users than other report types.  

When are potholes reported?

Potholes tend to be reported during the day, but disproportionately compared to other requests around the evening commute. The chart below shows the distribution of reports by time of day, where green indicates the number of reports is higher than the general distribution of FixMyStreet data. 

While potholes are associated most with the start of the year, they occur in smaller numbers all year long. The number of potholes reported through FixMyStreet peaks on the 28th February. 

Where are potholes reported?

While reports in FixMyStreet are less likely to be made in less deprived areas in general, this effect is larger for potholes:

This effect is driven more by reporting being lower than usual in more deprived areas than especially high in less deprived areas:

This pattern was generally similar for the Income and Employment domains of deprivation. This does not necessarily mean there are more actual potholes in these areas, but possibly that people in areas with higher income and levels of employment are more likely to report them. 

Examining reports using the deprivation subdomain that measures difficulty accessing services (GPs, supermarkets, etc) shows a different pattern, where a disproportionate amount of pothole reports are made in areas with the least access to services. 

The access to services measures in Scotland and Wales also reflect that the least accessible places have  a large number of pothole reports compared to the general dataset:

The area with the worst access to services (typically a measure of distance to services) has a disproportionate amount of total pothole reports on FixMyStreet. This doesn’t necessarily indicate this is where most of the potholes actually are, but more remote, less traffic-ed potholes will rank lower in risk-based calculation than those on busier roads, and hence may go longer without fix, and make a report on FixMyStreet more likely.

Repeat potholes

Fixing potholes is a never-ending task, as they are an inevitable result of erosion of roads over time. That said, poor repairs will make the return of a pothole more inevitable than it might be. The issue isn’t just that  the same pothole returns: if a pothole initially formed because the road surface was poor, others are likely to form in the same area too. 

Looking at reports on FixMyStreet up to the end of 2016, for 3% of potholes a new pothole was later reported within 10m between six months and two years after it was first reported (with an average time lag of 15 months). Expanding that ratio to a 20m radius, 7% of potholes had a new pothole reported in the same time range. 

While FixMyStreet’s data on potholes is far from universal, the geographical range gives us better scope than any single local authority’s data to see how reporting of potholes relates to social factors. You can examine this data yourself, on our geographic export, which gives counts of different categories of report by LSOA. 

Photo by Markus Spiske on Unsplash

This blog post is part of a series investigating different demographics and uses of mySociety services. You can read more about this series here. 

When people make their first Freedom of Information request using WhatDoTheyKnow they are sent an email two weeks later, asking them to complete a survey.  This survey has been running from 2012 and in that time has received 6,861 replies. Because this is an optional survey and not a requirement of making a request, this is a small proportion of the number of first time requesters in that time (around 3-4%).  This response rate reflects that the survey is currently quite long and asks questions that, while more useful when the service was new, are now less helpful in understanding its ongoing impact. 

As it’s unclear how representative this sample is of requesters of WhatDoTheyKnow, the overall results shouldn’t be read as authoritative of the user base. What is more interesting is how different groups of respondents use the site in different ways. The data from the surveys has been added to the explorer minisite, a research tool that uses chi-square tests to examine if there is a statistically significant difference in the distribution of responses.

Survey demographics

Looking at the overall picture, the average age of respondents is around 45-54. 

There were more than double the number of male respondents as female respondents. 17% of respondents said they had a disability. Disability is a broad category where self-identification can vary, which makes comparison to national figures difficult. However in 2011, 8.5% of the population of England and Wales were ‘limited a lot’ in their daily life as a result of a health problem of disability, while 9.3% were ‘limited a little’. This suggests that use of WhatDoTheyKnow is not broadly different  from the national picture – however this could be disguising variation within different kinds of disability. 

There is a good spread of income ranges among respondents — but the average respondent has a greater income than the UK median of around £28,000.

43% of respondents were working full time, 10% were working part time and 21% were retired. A majority (57%) were university educated. 

On ethnicity, most respondents declared ‘British’ or ‘English’. 16% were part of a BAME (Black, Asian and minority ethnic) group. Because of the small number of responses over a large range of ethnicities, a second ‘reduced’ option was created by grouping responses that just presents BAME/Not BAME/NA. This would make general trends statistically detectable, but may also disguise trends when different ethnic groups have effects in different directions. 

Over time there is a small number of trends. There is a slow rise in the number of female respondents – from 24% to 33% in 2019. There was statistically a larger proportion of BAME respondents in  2015 and 2016 (19-20%) and fewer in 2012-13 (13%). The number of respondents with disabilities does not show any significant differences between years.

Authority type

BAME respondents are more likely to write to Education, Central Government and Other than the general dataset, and less likely to write to health, local government, emergency services, military services, and media and culture.  BAME survey respondents make up 5% of requests to media and culture and 29% of education respondents. 

Female respondents are more likely than male respondents to write to education and health authorities than the general dataset, and are less likely to write to emergency services, media and culture, transport, and military and security services. Female survey respondents make up 14% of requests to military and security services, and 40% to education. 

Respondents with disabilities are more likely to write to health-related authorities and the emergency services than the general dataset, and less likely to write to transport and education. Respondents with disabilities make up 9% of requests to education authorities and 26% for health authorities.

Retired respondents are more likely to write to environment-related and local authorities, and less likely to write to central government and education. Retired respondents make up 7% of requests to education authorities to 33% for environment-related authorities . 

Reversing the lens to look at one type of authority, respondents writing to education authorities are more likely than the general dataset to be female (but still majority male) and more likely to be part of a BAME group (but still majority white). They are more likely to be below the age of 24 and less likely to be above 55 than the general dataset and (related to that) more likely to be in education and less likely to be retired. 

Message concern

46% of respondents said they were writing on behalf of ‘all people in the community’. This group was more likely to be retired, less likely to be part of BAME group, but more likely to be part of a community group (but not a political group alone). 

20% said they were writing on behalf of themselves/family as well as similar people. 

14% said they were writing on behalf of all people — this group was slightly more likely to be earning less than 12,500, have excellent internet access, and more likely to be involved in political activity (less likely to be part of a community group), to have made FOI requests before, and to make lots of FOI requests.  

13% said they were writing on behalf of themselves or family. This group has a spread on age, but is more likely to be older than 75 (and less likely to be 45-54) than the general dataset. 46% are still university educated, but this is less than the general dataset and this group is more likely to be have secondary or technical college qualifications, and slightly likely to be part of a BAME group than the general dataset (while still majority not BAME). This group is more likely to not be involved in groups or to previously have made requests.

Previous FOI use

The profile of a user who had never made a request before using the site is in many respects similar to other users. This group contains slightly more 25-34 year olds and those in full time education. They are more likely to be making requests where the information is mostly relevant for themselves/family or people similar and more likely to not be involved in community or political groups.

What’s next?

While this survey has found some interesting things about our users, it’s currently overly-long and has a much lower response rate than some of our comparable surveys. We’re looking at the best way of modernising the questions and survey platform to replace this survey, while maintaining continuity with some of the trends identified above. 

Photo by Shawn Ang on Unsplash

This blog post is part of a series investigating different demographics and uses of mySociety services. You can read more about this series here. 

Just as there is interesting information to gain from where people make reports, there are also interesting things to discover from when an issue was reported. 

There are four interesting times in the life of a FixMyStreet report:

1. When a problem happened
2. When a problem was noticed
3. When it was reported
4. When it was fixed

In the FixMyStreet dataset we have lots of information for when a problem is reported, but less about the other times. A follow-up survey gives us some idea if a problem was fixed inside a month –but this isn’t universally responded to. 

Reka Solymosi, Kate Bowers and Taku Fujiyama (2018) examined FixMyStreet data and found some signs that enough reports are made close enough to the time of time a problem is noticed that they show a statistical difference. Using reports of broken streetlights (which should be more noticeable when it’s dark), they showed that more reports were made at night compared to other kinds of reports. 

This analysis is replicated on the Explorer minisite, which shows that more problems with street lights are reported during the winter months; and also that they are disproportionately likely to be reported during darker times of day than other reports as a broken streetlight is more noticeable at night (while other kinds of problems become less obvious). The below graph shows when street light problems were reported. While a fair number of reports are made during daylight (reflecting that not all issues are reported close to when they were observed, or that some street light problems are noticeable during the day), compared to the dataset as a whole the nighttime reports for this category stand out.

Potholes are reported at the start of the year, and disproportionately in the afternoon. Dog fouling is also reported more at the start of the year, but this is more of an early morning report, with a peak as people arrive at work towards 9:00 am:

Some patterns reflect how people’s activity changes when not working. Issues in parks and open spaces are reported more at the weekend, while potholes are reported more during the week. 

While some problems are driven by physical processes that raises their occurrence at certain times of year and their report at certain times of day, other reports result from the activity of other people. Rubbish is reported in the morning, but also has peaks on Sunday (following Saturday night) and Monday, as regular commuters return. 

Similar to the idea that more 311 reports are made in spaces that are contested between different communities, Solymosi and colleagues suggest that reports can also be driven by the handover of the same space between different groups: “The narrative descriptions included with [FixMyStreet] reports reveal that these reports are made by people who are waking up to go to work, and encountering signs of activity that took place in the same location, but at a different time. They see signs of another activity in the space their routine activity pattern takes them through but is incongruent with their current use of this space, and interpret these as a signal disorder, attributing meaning which can result in heightened fear or anxiety.”

For people writing to their representatives on WriteToThem, there are similarly differences in when people write to different kinds of representatives. These might be times people are exposed to something that makes them want to write to their representative, or when they have the time to write.  Compared to all messages sent through WriteToThem, people writing to MPs are more likely to be writing before work and in the late afternoon, while Councillors are sent more messages  between 8:00 am and 4:00 pm. 

While few people write during the night, compared to other types of representative messages are written to Lords more often at night. Looking at the gender of people writing to MPs, the data shows that men are disproportionately likely to be writing at night compared to women (although again, most messages by men are still sent during the day).

Examining the time people make reports helps to create a better picture of when people encounter an issue that a mySociety service might be helpful for, as well as when people have time to do something about it. This suggests possible ways a service could be differently reactive at different times of day and helps sharpen potential research questions.

Photo by Jon Tyson on Unsplash

We’ve previously written about how best to proceed when an FOI request has been refused, but when there isn’t a response at all   that’s a slightly different problem. However, up until now, we’ve treated both in the same way. We’ve now made some changes to reflect the difference.

If you do receive a response, but feel it’s inadequate or that your request has been wrongly refused, there are two ways of contesting the outcome. The first is to ask for an internal review, where the request is reassessed inside the same authority (by a different team or person). The second is to appeal to the Information Commissioner’s Office (ICO).

To reflect this, our approach when providing prompts to WhatDoTheyKnow users has been an escalation ladder — suggesting people request an internal review, and then appeal to the ICO if still dissatisfied. However,  as we learned when talking to the ICO earlier in the year, that this isn’t always the fastest route to getting a response.

An internal review is useful when disagreeing with a decision, but when the issue is that the statutory deadline for a response has passed, and follow-up messages haven’t been answered — a situation our colleagues at Access Info aptly refer to as ‘administrative silence’ — it can be better to complain directly to the ICO.

According to the statutory code of practice, a public body may take up to 20 working days to undertake a review, and so this route is likely to result in further delay, whereas an intervention by the ICO may have a faster result.

So when a request is overdue, our email prompt will no longer suggest that users might want to seek an internal review, but instead we suggest sending a follow-up message to the authority and note that they can appeal directly to the ICO.

However, if you have an issue with the actual decision of a request (for instance, disputing an exemption applied), internal review is the correct first port of call — and in a surprising amount of cases can be very successful. While we don’t have figures covering all kinds of authorities, for requests made to central government, 22% of internal reviews resulted in some change to the original decision (and 9% were completely overturned) and for local government this figure is between 36-49%.

Both internal reviews and appeals to the ICO can be effective methods at redressing disputes around Freedom of Information requests, but it is important to consider which is the right tool for the situation.

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Image: Ümit Bulut

This blog post is part of a series investigating different demographics and uses of mySociety services. You can read more about this series here. 

WriteToThem is a service that assists people in writing to their representatives. Given a postcode, it lists the associated elected representatives at every layer of government and provides a form to write an email to them.

This can also be seen as a bundle of services. The main use of this website is to write to MPs, but this is just under half of messages ever sent (48%), with most messages sent to representatives in devolved or local government. Different services have different profiles of use and so need to have their effect judged separately.

In 2015, the British Election Study asked whether people had contacted a “politician, government or local government official” in the prior 12 months and found that 17% had. Based on this, over 11 million adults wrote to a representative or official that year — and WriteToThem’s 187,000 emails accounted for 1.6% of this. These results also showed that 20% of men had made contact compared to 15% of women, meaning that 57% of those doing the contacting were men. Extending this into a logistic regression shows that older respondents and those with higher levels of education were more likely to contact, with no significant difference for income and ethnicity once age and education were controlled for.

Demographic profile of WriteToThem users

Looking at the profile of people writing to MPs using WriteToThem, there is an uneven use by different demographics. Over all time,  60% of messages sent have been from men and  60% of people writing had written before. Using the index of multiple deprivation, more messages are sent by better off areas, with 55% of messages being sent by the less deprived half of the country, and 7% of messages coming from the most deprived decile (you would expect 10% if this were evenly divided).

There is a clear linear pattern of greater employment and income in an area being associated with a greater amount of messages sent.  Most of these gradients are slight, but in aggregate the effect is that WriteToThem reflects existing divisions in participation (although there are no good sources for the demographics of people who write to MPs specifically) .

But is this actually a problem? Should a service be judged for the proportion of existing represented groups making use of it, or what it does for the under-represented groups who do use it? WriteToThem has delivered 73,000 messages to MPs from people in the most deprived IMD decile alone, if this has led to dialogues that resolved issues that would not otherwise have happened, this is a positive regardless of whether the same is also true for more people in the least deprived areas. If WriteToThem lowers the cost of contact by making it easier, then it is unsurprising that many of the people making use of it would have made contact anyway — but also included in that are people who were previously unable to engage in the process.

When we look at the result of the survey asking whether a user of WriteToThem was writing for the first time, we can see that people from the bottom three IMD deciles were statistically more likely to be writing for the first time (this is also true when just looking at people writing to MPs, and when just looking at 2018). While generally the number of people using the site for the first time has decreased over time, this decline is demographically uneven and mostly occurs in less deprived areas.

For the complete time-span of the service, 47% percent of survey respondents in IMD 1 (most deprived) were writing for the first time compared to 38% of IMD 10 (least deprived). Looking at just 2018, this was 48% compared to 35%. While the service as a whole is used more by people in less deprived areas, of those using it in less deprived areas it is successfully facilitating a higher proportion of first time contacts.

The local picture

To return to the idea of bundles, WriteToThem is also quietly solving a much harder problem than contacting MPs. While people generally recognise their MP when prompted with a name, local councillors remain far more anonymous. From 2007 to 2018 WriteToThem has helped constituents send 450,000 emails to their local councillors (42,000 in 2018). This service has an effectively even gender ratio (with a female majority in 2018), with more reports coming from more deprived areas (54% by more deprived half).

If we imagine one of these bundled services being a site named “WriteToYourCouncillor”, it is in many respects a model service, with a user base displaying an even gender ratio, and more likely to be used in deprived areas. That in reality it is one function of a more well-used service in terms of numbers somewhat obscures this.

But while it is good to recognise where services are successfully reaching people we want to reach, it is also important to think about volume and overall impact.  One issue with a service used more by men or in better off areas might be if it shapes how resources are deployed or provides a false shape of the views of constituents (and emails received are certainly used by MPs to build a picture). Even a service that adequately represents under-represented groups may be ineffective if it exists in a wider ecosystem that does not.

At the moment, the systematic effect of any bias in WriteToThem outputs is marginal as WriteToThem accounts for a small fraction of parliamentary mail.  While the amount of physical mail entering the Houses of Parliament each year has decreased steadily, in 2018 it was still 24 times larger than the number of emails sent to MPs via WriteToThem. The average MP received 94 emails via WriteToThem in 2018; most MPs would receive more than this through other means in a week.

Returning to the British Election Study finding that 57% of contacting in 2015 was done by men, the equivalent figure for WriteToThem as a whole in 2018 was 55%. Being generous and bearing in mind the previous finding that the method used to assign gender from name undercounts women, this could be seen as a marginal improvement on the real world. However, it would be a marginal improvement in a pool that only represents 1.6% of the total amount of number of messages.

Defining success

Based on the above, we can think about three different kinds of ‘success’  of a civic tech service in serving under-represented groups:

Relative – The service improves under-representation relative to the current standard. e.g. a service where 60% of usage was by men is an improvement over an offline status quo of 70%.

Absolute – The service adequately (or over-) services under-represented communities to what would be expected based on their numbers in the general population.

Systematic – The service successfully services under-represented communities and is successful enough that this redresses issues of representation in competitor services/methods.

Working with these, we could say WriteToThem is a success on a relative level, servicing people in more deprived areas more than they would have been otherwise (larger proportion of first time writers), but not to the proportion of the population these groups represent.

The “WriteToYourCouncillor” part of the bundle is  a success on an absolute level, providing a relatively even amount of representation, with a slight weight towards groups who typically make contact less often.

But neither really makes a dent systematically. They may be redressing inequalities of access for individual users (which is good), but cannot significantly adjust inequalities in volume of messages and the corresponding perceptions of problems.

Making a dent in this problem is outside the scope of WriteToThem — and probably should be. While you can imagine a future where WriteToThem continues to lower the barrier to contacting representatives,  this is likely to create new users from currently-represented groups for each under-represented person successfully reached. Targeted interventions and partnerships with other organisations can avert this problem in terms of helping individuals make contact about their issues but turning the problem around, this is a platform that is unlikely to provide a balanced view of opinions and priorities of constituents.

If it is a problem that representatives have systematically skewed visions of the problems and views of their constituents, is an email platform that requires citizens rather than representatives to do work the best way to address that? A civic tech solution to this problem might look more like Consul (or similar general participation platform) than WriteToThem – but even explicitly designed online platforms still risk being skewed towards the online and present members of the community. Exploring better forms of local participation is something currently being explored through our Public Square project.

This blog post is part of a series investigating different demographics and uses of mySociety services. You can read more about this series here. 

Indices of deprivation are useful for mapping social phenomena onto geographic data. For a series of domains (in England: income, employment, health, education, skills and training, crime, barriers to housing and services, and living environment) all Lower Super Output Areas (LSOAs) are ranked from most deprived to least deprived. From these the Index of Multiple Deprivation is created  — which helps to illustrate which areas of the country suffer from multiple different negative factors.

The indices of deprivation are compiled separately for England, Scotland, Wales and Northern Ireland. While they cannot be combined, they do often illustrate similar measures and so are useful for cross comparison. As most FixMyStreet reports are made in England, more subtle patterns in how deprivation and reports are linked can be detected from this larger set of data.

The Explorer minsite uses the Index of Multiple Deprivation (IMD) and respective domains to understand how reports for different categories of FixMyStreet report are distributed and explore how deprivation affects reporting. This page shows the categories that are more likely than the general dataset to be reported in the lowest IMD decile (most deprived) and this page shows the categories that are more likely to be reported in the highest IMD decile (least deprived).

Missing reports

As examined in previous research, the most important finding when examining deprivation is the suggestion that there are reports that should be being made that aren’t. The Explorer minisite shows that reports of dog fouling have a peak in the middle deciles, but this does not reflect the real world incidence of dog fouling, which found that the most dog fouling was found in the bottom two deciles.

Even when actual incidence of problems is higher in more deprived areas, the reporting rate can be lower — any picture based on self-reporting is likely to have a large set of missing data. In the case of dog fouling, this means information about hotspots is not communicated to enforcement. In other cases it might mean road defects unfixed, or fly-tipping uncollected.

Exploring domains

While previous explorations of deprivation and FixMyStreet have used the index of multiple deprivation alone, the Explorer minisite lets you see how the distribution differs on each of the domains of deprivation. For instance, looking at reports of rubbish, we can see that while generally there are more in the bottom 50% of IMD deciles, there is a stronger relationship against the crime domain.

Rubbish vs Multiple Deprivation

Rubbish vs Crime IMD Domain

Examining the data for dog fouling shows that the peak in the mid-deciles is even clearer when mapped against income deprivation than for multiple deprivation. The income domain continues to show that compared to the general dataset there are fewer reports in the higher deciles than might be expected.

Abandoned vehicle reports have a scattered relationship with a few different factors, but the association with crime is much less noticeable than the association with lower housing costs. Problems with drainage generally are more reported in less deprived areas, but when focusing on access to service deprivation, they are concentrated in the most deprived areas.

Breaking down by the different domains that make up the index of multiple deprivation lets us better understand what factors are driving either problems or the reporting of problems. This in turn helps to frame questions to ask about what is driving these different uses of FixMyStreet.

Photo by v2osk on Unsplash