2. Research Findings
2.1 Literature review
The purpose of data sharing for the public good and its uses is highly varied and can span a range of relationships, including public bodies, third sector organisations and the private sector. Alongside the variety of uses, there are also many legal and technical barriers to sharing the data. However, researchers and policymakers are finding an increasing number of innovative ways to overcome these barriers.
2.1.1 The purpose of sharing data
The motivations to share data are very broad and can include:
- Sharing information to enable targeting for new services. Some new services offered by local authorities may specifically target advertising towards particular groups that would benefit from the service.
- Linking administrative data. By connecting people's administrative data (such as passport or national insurance data) across departments, people do not have to spend as much time completing forms (and forms are more likely to be completed fully).
- Offering the right services to people when they need it. An example of this is the Department for Work and Pensions notifying local authorities when people are receiving universal credit so that their housing offer can be tailored correctly.
- Automatically providing people with the benefits that they are entitled to. HMRC and DWP may share data with other government services to identify those living on a low income and provide them automatically with any additional support available.
- Providing services that are integrated across departments. Some services (for example criminal justice or employability programmes) benefit from multi-agency interventions. Stakeholders from different departments can come together to discuss individual cases where required.
- Ensuring a continuity of care. Sometimes people need support from a variety of services over time (for example, if a person is discharged from hospital) which may require certain information to be shared across those services.
2.1.2 Legal and Technical Barriers
- Many of the barriers to effective data sharing are associated with the skill level among staff members around data storage and their knowledge about what they are allowed to share. Staff in public sector organisations often cite concerns about sharing data without the correct permissions as a reason for their reluctance to share. A lack of knowledge around data storage methods may also lead them to create data systems that are not interoperable with other public bodies (e.g. by using different database software or creating variables based on definitions specific to their organisation), which creates difficulties when attempting to merge data. Some studies suggest that organisational culture can act as a barrier, with some staff members basing their decision to keep data to themselves on the premise that 'knowledge is power' or out of concern that sharing the data may reveal aspects of organisational weakness.
There are also numerous legal concerns that act as barriers to data sharing, including a heightened sense of risk associated with data loss and fear around the repercussions of not fully complying with data regulations. The introduction of the General Data Protection Regulation in 2018 has caused increased concern among workers around their compliance and potential consequences for any accidental non-compliance.
Low public trust in data sharing can also lead to reluctance in public organisations to engage in it and can cause elected officials to be reluctant to put systems in place that would enable effective data sharing.
Resource and capacity constraints are another significant barrier to data sharing. Costs associated with data sharing may include data cleaning and reformatting, as well as administration-related tasks which take up staff time. These constraints may pose a more significant challenge for smaller third sector organisations.
Additionally, infrastructure requirements may act as a significant barrier. For example, it may be that organisations have incompatible software that does not allow for interoperability. It may also be the case that organisations cannot afford the licenses to software that would enable an easier and more secure transfer of data.
2.1.3 Potential solutions to barriers
Researchers and policymakers are increasingly adopting innovative solutions to the barriers that they face around data sharing, which fit into four broad categories, including people, technical capacities, partnership development and organisational culture.
1. Train staff to evaluate the benefits compared to the risks of data sharing
2. Ensure that the workforce has appropriate skills – integrating analytical and service teams so that both have a good understanding of each other's needs
3. Allocating staff time effectively so that data sharing is not seen as a burdensome additional task.
1. Prioritise interoperability into database system design to avoid later resistance to data sharing on the premise that it is too expensive
2. Adopt consistent definitions of variables (that are ideally in line with national standards – this can be done using the government's GDS Registers)
3. Consider back-up/recovery plans at early stage
4 . Ensure data is kept clean and up to date.
Partnership Development Organisational Culture
1. Maintaining trust between partners through strong communication to instil confidence in the idea that data will not be shared further and to enable partners to understand why there may have to be limits to the types of data shared
2. Taking care around the way that data provided by partners is shared in publications
3. Encouraging networks that bring people together to establish collaborative networks
4. Clearly outline the purpose of sharing data (eg a social problem, direct and tangible public benefits, achieving long-term impacts, aligning with public expectations around the boundaries for data sharing, minimising negative effects).
1. Be ready to accept unwelcome insights from data
2. Ensure all staff understand the basic principles of confidentiality, data protection, human rights and data capacity in relation to information-sharing and that the organisation is confident in espousing these
Encourage consideration of the risks of not sharing information
4. Establish leadership and accountability models for data sharing so that staff know who to contact for advice.
2.1.4 Examples of Best Practice
Building Resilient Families and Communities: Troubled Families Programme
The Troubled Families Programme signed an Information Sharing Agreement with South Staffordshire and Shropshire Healthcare NHS Foundation Trust. The aim of the program was to support families with multiple, high cost challenges by developing an agreed improvement plan. In Staffordshire, it was found that many of the families experienced extensive difficulties with mental health issues so the Information Sharing Agreement provided a legal basis for sharing mental health data, which improved the way that resources were directed within the program and improved the referral process.
Key Learning: While data sharing is often considered to be a resource-intense activity, there are also situations in which it enables a better use of resources through targeted interventions.
Examples of multi-agency collaborations across the country include 'Multi-Agency Risk Assessment Conferences' (MARACs) and 'Multi-Agency Public Protection Arrangements' (MAPPAs). MARACs bring together different government agencies and commissioned services to coordinate risk-based responses to domestic abuse, while MAPPAs bring agency representatives together to assess and manage the risks posed by sexual and violent offenders. Attendees usually include representatives from the police, children's social care, health representatives, housing, probation, education, mental health, homelessness, local drug and alcohol services and children and family court advisory and support services.
Key Learning: Insights from multiple agencies that are gained from face-to-face interaction can enable effective risk management for serious and complex cases, ensuring that a person's needs are dealt with holistically with different aspects of their life taken into consideration.
Use of Housing Benefit Data in Dudley
Dudley council mapped the distribution of people on benefits, using software that allowed the analysis of characteristics such as date of birth, address and number of dependants. They received data from DWP that was not publicly available. They used the data to target people in priority areas that they would invite to Community Information Days. As a result of their efforts, they exceeded the number of expected attendees.
Key Learning: Programs can be made more successful through targeted signposting efforts and a deeper level of analysis that can only be completed by the sharing of non-public data.
2.1.5 PACE partnership and data sharing
A number of studies have sought to address the data sharing issue, for instance in 2018 Carnegie UK in association with Involve produced Data for Public Benefit, a report which highlighted the need to balance the risks and benefits of data sharing. This study, like the present PACE work, involved a dialogue with the organisations and partners involved.
The Carnegie UK report - and other related reports - examine the issues around data collection and sharing, particularly in relation to local authorities and other public sector bodies. There is often a focus on the rationale behind greater sharing of information and the benefits it can bring in terms of delivering more effective, efficient and often more personalised services. Most of the literature focuses on the barriers and challenges to the implementation of successful data sharing arrangements, and we would expect some of these to be relevant to the PACE scenario, and also identifies the steps which can be taken to improve data sharing, with some examples of good practice which may be applicable in the PACE context.
Typically, the issues encountered when partners attempt to share data can include:
- Consideration of the benefits and risks of sharing data between public bodies
- Identification of how barriers can be overcome and/or avoided, for example, anonymisation of data, explicitly gaining client approval for sharing
- Establishing how public sector professionals understand, define and value data sharing
- Assessing what data is available and what actually needs to be shared, and by which organisations, for what purpose: this kind of analysis can often identify some relatively small scale sharing - between a small number of organisations - that can bring significant benefits
- Assessing the overall need and/or desire for data sharing amongst public sector bodies
- Identification of the risks to the wider public associated with personal data and data sharing
- Understanding how public sector bodies can best harness data to improve their work and benefit
- The infrastructure requirements for effective and legal data storage and sharing.
A range of specific information can be used in responding to redundancy situations. These include:
- Client skills and experience, roles in work, and time in employment – data from the employer and the employee
- Client benefits/UC situation and housing status – data from DWP and LA
- Client tax situation – data from HMRC
- Redundancy payments and how to manage them
- Identifying new opportunities that match with the aspirations, skills and experience of each client
- Interventions provided and outcomes achieved
- Managing client progression which may involve training, personal development and job search
- Client job placement and sustainability, that is, how successful has the PACE support been, and what are the outstanding tasks? (To explore this the Scottish Government carry out the PACE Client Experience Surveys on a 2 year cycle).
Since the existing data is held by a range of organisations – and the data collected during the redundancy support process is also collected by a range or organisations, the issue of data sharing is important in ensuring the support to those leaving employment is experienced as appropriate, responsive and flexible, efficient and joined up – so enhancing the likelihood of achieving successful outcomes.
This project stems from a desire to ascertain if PACE Delivery Partners are supportive of customer data sharing and if so, what barriers, challenges, legal/technical/governance issues may need to be overcome to implement effective data sharing. The remainder of this report outlines the views expressed by PACE Partners and our resulting conclusions and recommendations.
2.2 Stage 1: Initial interviews with key partners
As highlighted in our methodology section, we conducted a series of interviews with PACE Partners by telephone between November 2019 and January 2020. This section of the report highlights the main views expressed and key findings.
2.2.1 Overall views on data sharing
The majority of partners shared the following views:
- Aware of potential. Delivery partners understood the potential benefits for PACE clients of increased sharing of individual data, and in principle were supportive of efforts to facilitate data sharing
- Organisational access to relevant data. Partners questioned whether they (and their member organisations) had access to sufficient or relevant individual data.
- Aware of the challenges. All partners were aware that significant legal and technical challenges would need to be overcome. This was particularly the case for key players such as DWP and HMRC who would have the added challenge of facilitating a Scotland-only data sharing arrangement from within a UK-wide institution.
- Statutory requirement. Most partners felt a statutory framework would be a pre-requisite for setting up effective data sharing amongst PACE Partners. Without a statutory basis most partners felt it would be difficult to get a data sharing initiative off the ground.
- Lack of knowledge. Several partners highlighted that they were unsure what data other delivery partners held that would be useful to them. Some partner representatives were new (or recent attendees) at PACE meetings and were not entirely sure what all the partners did, or what data they held.
- Sharing or wanting? Linked to above, there was a perception that data may be 'wanted' or 'needed' by PACE rather than 'shared' or 'reciprocated'. Most partners could not think of data that they would need from other partners so there were concerns that data sharing could be one-way.
- Data sharing policies. There was limited evidence of partner organisations having specific policies or guidelines on data sharing. In the post-GDPR landscape, organisational attitudes towards data sharing were described as 'cautious' and 'conservative' with most organisations taking a case-by-case approach to data requests that came their way.
- Build on existing database. Several partners highlighted that the SDS PACE database represents the best PACE database at present and they asked whether there was an opportunity to build and improve on it via PACE outreach work.
- Opportunity cost. Linked to awareness of the challenges involved in creating effective data sharing, several partners felt the effort involved could be better spent focussing on outcomes for PACE clients and on gauging the impact of PACE in other ways, for example, through the client survey.
Overall, partners recognised that there was potential to share more complete information about PACE clients and that this could help to provide a better PACE service. However, there was also recognition that the investment required in terms of time, money and resources, and questions around the value of the data, meant that the chances of it bringing a commensurate return were quite low - and in the meanwhile it could get in the way of delivering a good service.
2.2.2 Services offered
The table below highlights the services they currently offer to PACE clients (and others). Note – the delivery partner may not always offer these services directly themselves, for instance, some services are provided by members (STF, CS) or via referrals to partners and contracted specialists. Additional work and services not listed below include arbitration (ACAS) and policy direction (SG).
|Alternative employment training, support & advice||Income & money management advice||Wellbeing|
|Employability advice||Career development||Literacy/ Numeracy||Core skills||Vocational training||Business start-up||Benefits||Rights and entitlements||Money||Pensions||Coping with stress||Mental health & wellbeing|
Note: Table excludes Unite as they are not a delivery partner. These services are liable to change and have not been fully verified.
2.2.3 Data collection
SDS are the only delivery partner who currently collect data on PACE clients and the table below sets out the information they collect. Data collected for individuals by SDS
PACE interventions Monitoring
Monitoring PACE outcomes
Time spent on PACE
DP / data sharing arrangements
PACE client feedback
Aside from SDS, none of the delivery partners applies criteria or a flag for PACE (or, more broadly, redundancy) on their databases.
The information in the tables on the next two pages reflects the types of data, in general, that the partners collect on individuals, businesses and on intelligence around redundancies. In terms of unique identifiers, these were most likely to be National Insurance number, although DWP now apply a universal client number to Universal Credit applicants and at HMRC, in addition to National Insurance number, a Unique Tax Reference is also attached to individuals. As previously highlighted, some of the information in the table is collected by members of the delivery partner organisation, for example, colleges or training providers. Again, we have excluded 'piecemeal' data collection, for instance, when a registration process may (or may not) collect information on an individual's employment history.
|Unique identifiers||Demographics||Postcode||Employment history||Skills, quals & experience||Benefits history||Tax records|
Several delivery partners used informal networks and media to keep abreast of business performance and fluctuations in their sectors and geographic areas. The local PACE Partnerships and similar forums were identified as important early-warning mechanisms for the health of individual businesses and sectors. HR1 forms, though deemed unreliable in terms of overall redundancy levels, were also a mechanism by which some delivery partners became aware of redundancy situations.
|Local sources of intelligence about redundancies||Information gathered on additional services or funding eg training/quals||Information gathered to monitor performance with PACE clients|
2.2.4 Current data sharing
Examples of sharing individual and aggregate level data that partners have been involved in include:
ACAS. The Scottish Government approached ACAS when considering the abolition of employment tribunal fees. The Government requested data on the number of cases and level of involvement in Scotland. Drawing on historical data (and postcodes) ACAS were able to estimate the number of businesses based in Scotland from their overall UK database. This was an ad-hoc project involving aggregate level data and was justified by ACAS on the basis that it might help tribunal courts and applicants.
COSLA. Single Entry Point - Business Gateway and Scottish Enterprise share a CRM system. In 2019 Business Gateway planned to share a system with the SDS employer engagement team (following advice from the Enterprise and Skills Review) to support the business environment to understand client needs/wants (creating a 360 view of businesses/business people) and assist with marketing and development. Single Entry Point is motivated by the need for a 360 view – and there are realistic benefits for Business Gateway's work. A key challenge is to build staff understanding of the system so that the full benefits can be realised. There is a communication programme to convey to 300+ staff which will take some time across 2019/20.
Colleges Scotland. Most colleges share individual level data with SDS currently and, in general, colleges are very accustomed to sharing data, with lots of arrangements in place. Colleges have adapted well to GDPR legislation and are comfortable sharing data within its requirements (unlike some organisations). Data controller to data controller sharing eg SFC to SDS – there is strong legislation underpinning these kinds of data sharing so they work well. For instance, Colleges share data with HMRC via National Fraud Network.
DWP. Scottish Prison Service (SPS) send data to DWP on individuals entering/exiting custody. This emanates from Scottish Government policy which stresses importance on understanding re-offending risk. Data is shared electronically via a spreadsheet which is sent to DWP. Some prisons are private so the data is not universal. In addition, DWP have prison work coaches (x 14) in prisons. Prisoners get a letter inviting contact. Prisoners can't claim UC before they exit prison but DWP can help them set it up in advance.
DWP. Integrated Employment and Skills. This data sharing involved DWP and SDS. In 30 DWP offices there were SDS advisors who DWP could refer people to. The paperwork (data share) went from DWP to SDS and feedback was provided to DWP on the outcomes. This was effective data sharing but it was discontinued with the introduction of GDPR in 2018.
HMRC. Data analysis requests are made via FOI. For instance, how many PAYE businesses exist in a region? This involves extracting aggregate information. At an individual level HMRC sometimes gets journalist requests and police requests (for which there are strict rules and processes) in place. HMRC treat requests on a case-by-case basis and are open to requests and discussions.
SDS: Beginning in 2010/11, the 16 years+ Data Hub aimed to encourage shared information amongst key partners to provide tailored support to young people when they leave school, specifically to those who are not moving on to learning or work. A key point for the development of the Data Hub came in 2013/14 when it was tied to legislation (The Post 16 Education (Scotland) Act 2013). This made effective data sharing much more likely as the Act formalised matters and gave SDS more power to encourage partner cooperation within defined timescales. With the legal framework in place the bulk of the work involved addressing the technical challenges around the data sharing process and creating secure data transfer mechanisms for partners to use.
STF: SDS have standard forms that all members ask trainees/students to complete. This is for Scottish Government funded training. Some members work outside this also, but probably 125 out of 140 STF members would collect this information, so SDS have access to a lot of information in this way (and potentially could ask about redundancy via this mechanism).
SUL. Following referrals from STUC, SUL may refer people to other learning providers, for example a local college, and so data is shared with these providers. SUL have data sharing as part of their contract with training providers and when individuals register with SUL they give their consent to having their data shared with relevant third parties. SUL report to STUC on learning outcomes at an aggregate level but don't share this information outwith the STUC at present. SUL might also report back to unions like UNITE on individual progress but not to employers as individual confidentiality is key. In essence, personal data could be held in three places – initially collected by UNITE, passed to SUL (with some additional data collection during SUL registration) and then passed to a training provider (such as a College) if SUL refer the individual to one of their courses.
UNITE. Examples of receiving data via secure transfer from SDS on unionised individuals who have contacted them for advice. UNITE have then been able to contact them to offer additional support. UNITE have not reciprocated, mainly because of uncertainty around what services SDS could offer to our members.
Observations on data sharing arrangements:
- Statutory: many of the larger scale data sharing arrangements involving delivery partners had a statutory basis, in other words, governmental and /or legal frameworks were involved that made data sharing mandatory. For many partners, a statutory arrangement means they can commit to making it happen, sometimes without the need to record the time/cost, as the requirement becomes part and parcel of their workload.
- Reciprocal: outside of statutory arrangements some data sharing occurs when it is to the mutual benefit of the organisations involved. These arrangements are often 'low-level' and covered by registration form waivers and the observation of key GDPR safeguards such as secure data transfer.
2.2.5 Costs of data sharing
As stated previously, data sharing arrangements were often viewed as part and parcel of Delivery Partners' work. As a result, there were only a few examples of the costs associated with data sharing:
- SDS (Data Hub):
- Development costs with the Scottish Government's SEEMiS pupil database on Data Hub (exact £ not known)
- DWP spent c.£15k in making adjustments to their system
- SDS costs not known but significant human resource over a number of years.
- DWP (Data Hub):
- Initial data analytics costs were c.£7k
- Annual costs are approx. £10-14k
- Main set-up costs involved 2 x senior members of staff.
- COSLA – BG (Single Entry Point portal):
- Legal costs were c.£15k
- Not costed, but around 20 staff days during set-up phase
- Currently, 3 x FTE staff to support use of CRM system
- Expenditure to date in region of c.£200k (£40k adjusting systems, £160k on development work.
These examples highlight significant financial implications for large scale, statutory data sharing arrangements, mainly made up of staff, IT and legal costs. There were also significant timescales involved in many of the data sharing arrangements which partners highlighted.
2.2.6 Barriers to data sharing
Barriers include some which were anticipated and others which were more nuanced and specific to the organisations involved eg in-house processes. Virtually all of the barriers we discussed with delivery partners were identified as having potential to challenge effective data sharing (see table overleaf):
|Infrastructure requirements (eg equipment, software, licences)||Costs were likely to be incurred with the requirement for new equipment, software or in licencing|
|Technical incompatibilities (eg different systems)||Most partners had encountered technical incompatibilities when sharing data with other organisations and anticipated this would be an issue in PACE data sharing|
|Variations in data recording methods||Partners were aware that NI number was a fairly universal unique identifier, however pointed out the potential for partners to record similar things in different ways eg. codes for an individual's work status|
|Cultural resistance (eg amongst staff)||Although attitudes to data sharing varied across partners there was a perception that front line staff were very cautious when it came to data sharing|
|Legal considerations (eg data protection/GDPR considerations)||Post-GDPR most partners felt their own institution had become more conservative when it came to data sharing, and that the need to set-up protocols and legal frameworks made data sharing less likely|
|Variations in unique identifiers used||Although NI number was a common identifier, DWP's new systems for UC uses a different unique reference. In addition, some partners did not need to collect NI numbers for their work with PACE clients|
Additional barriers were identified:
- In light of GDPR and some high profile data breaches, confidentiality is a very sensitive issue for many of the Delivery Partners and there is a sense that only statutory arrangements can overcome the cautious and conservative attitudes to data sharing that currently exist
- There is a need to embed the data processes, not just rely on individual members of partner staff to complete tasks – in other words, it is important to automate the process to ensure reliability.
- If not statutory:
- Reciprocity becomes important – in other words organisations need to see some kind of return from sharing data in terms of their own insights or enhanced process efficiency. This is hard to achieve, especially across multiple partners
- Legal side eg protocols become time consuming and costly.
- People may not want to admit they've been made redundant – so the accuracy of any data collected around the reason for unemployment being redundancy will always be an issue
- Linked to above, key partners (eg DWP) cannot guarantee 100% accuracy of record keeping due to clients not sharing information and/or staff input errors. Also, 'redundancy' flags would need to be switched on/off depending on the individual's status
- Data retention – some partners (eg SUL) only hold personal information for the length of time their workstreams/projects are funded. This is often two years.
- UK wide organisations being asked to comply with a Scottish-only issue is a barrier, although this has been done before (eg by DWP for the Health and Work Support Pilot in Dundee and Fife, funded jointly by the Scottish Government and the Department for Health and Social Care/DWP Work and Health Unit)
- Adjustments required to registrations forms and permissions eg third party access
- Adapting new systems to data sharing requirements (eg DWPs The Build system, which DWP staff are still adjusting to)
- Workload – Delivery Partners often gauge the human/technical resources that ad-hoc data sharing requests will involve, and in some cases refuse on the basis that too much work is involved.
2.2.7 Reflections on Stage 1
Two key partners
There are 20+ partners but two of these are of central importance in the context of data sharing:
- DWP: only organisation that can realistically flag 'redundancy' at an all-population level
- HMRC: only organisation that can realistically track the outcomes of people made redundant (PACE or non-PACE).
These are the two partners who have records for all those who may become redundant – all other partners work on specific topic areas eg advice on pensions (Citizens Advice Scotland), re-training opportunities (SDS, SUL/ Colleges Scotland), re/self-employment/start-ups (SLAED, Business Gateway), workplace tribunals/arbitration (ACAS) and some are representative organisations who have no contact with people who have been made redundant (STF, ICAS).
The case for data sharing
The aim of building an accurate picture of redundancies in Scotland and the desire to measure the impact of PACE are understandable. However, given the multiple barriers and likely costs involved in facilitating effective data sharing, the case for data sharing needs to be made. This raises a number of questions:
- Would the addition of flags to key partner databases result in an accurate figure on redundancies in Scotland? Bearing in mind the potential for client non-disclosure and data input errors there is a question mark on how an accurate figure could be established. The Data Hub is linked to facts (eg date of birth), whereas a redundancy flag needs to be switched on/off.
- Are current PACE interventions significant enough to warrant measurement? A key strength of PACE is that its aims are aligned to those of its Delivery Partners. However, this does raise the question of attribution. Aside from effective signposting to relevant services, to what extent is it possible to pinpoint the added value of PACE?
- Is there potential for reciprocity? A number of partners were unsure how they could benefit from increased data sharing with PACE Partners. In some instances this was borne of a lack of knowledge about other PACE Partners and their work. This is important in the context of PACE data sharing as Delivery Partner representatives have a job to do in convincing internal colleagues that PACE data sharing is worthwhile for their organisation.
- Are there grounds for statutory compliance? As highlighted, many partners feel that a statutory requirement would be the most viable way to motivate the changes (eg redundancy flags) that would be required to facilitate effective data sharing. In addition, some partners highlighted legislation that could be used (eg post-16 Education Act).
In the course of Stage 1 we assessed a number of measures which could help increase the accuracy of knowledge on redundancy levels in Scotland:
- Effective data collation could improve overall redundancy estimates. Currently, HR1 forms are an important indicator of redundancy occurrences in the UK, however, for a variety of reasons, they are generally regarded as an unreliable measure of overall redundancy figures and would need to be improved before they could be used in this way.
- The PACE client survey is a powerful tool. The client survey could be used to gather more information on redundancies – it has 1,000 respondents – however, this is unlikely to create a fully reliable data set.
- The PACE Partnership local network. A recent PACE Partnership Review (Sept 2019) highlighted a number of recommendations for local PACE Partnerships around roles and responsibilities. Given their local links and knowledge it seems possible that the partner network could collect data on the frequency, type and number of redundancies in their area, and feed this information into a central database. However, this would require a level of resourcing / administration that is currently not available to the local networks.
It should be noted these suggestions are concerned with overall redundancy numbers and would not result in the establishment of a PACE client database or address the need to measure the effectiveness of PACE. On this issue the following points are relevant:
- Expand the SDS PACE database. Currently, SDS collect and maintain the only PACE client database. Is there potential to increase the current data collection process at PACE events, in cooperation with PACE Partners and employers so that this resource becomes more powerful? For example, while SDS already collect data at PACE events, it may be possible to collect more, for example quick sample surveys to identify personal situation, plans and expectations.
- Expansion of client survey. Linked to the above, a larger database of PACE clients would allow the client survey to establish a wider understanding of the frequency, type and number of redundancies in Scotland. Currently, the survey is the main means by which PACE performance is monitored and measured. We would recommend that quantitative and qualitative insights (eg case studies), be developed from the core client survey.
2.3 Stage 2: Follow-up interviews with key partners
Stage 1 of the consultation was followed by an interim discussion with the project steering group. This highlighted the potential role and significance of the PACE Partnership in terms of understanding redundancy – what kind of jobs are being made redundant, in which sectors, and why, what kind of jobs are people affected by redundancy finding, and to what extent does this represent continuity or a change in direction/skill? It was agreed that answers to these and other questions could form a key part of an effective approach to business development, economic development and an effectively targeted employability service in Scotland.
With this in mind, Stage 2 of the consultation sought to contribute to how the information held by PACE Partners could be drawn on to better understand redundancy and its consequences, and appropriate responses. This was felt to be particularly important at a time of significant change in the labour market, in terms of automation, disruptive technologies, the growth of self-employment and the gig economy, responses to the challenge of climate change, and the ever unfolding impact of COVID-19.
This work was complemented by desk research to explore the extent to which it was possible to pinpoint those people whose jobs had been made redundant who were at a higher risk of long term unemployment. The PACE Partners were keen to understand the scope for focusing more intensive post redundancy support on those who were at particular risk of longer term unemployment, and the information that would be needed to identify those at greater risk.
In addition, contemporary events (April-May 2020) had also highlighted increased data sharing in some key industries such as logistics and retail, all of which highlighted the relevance of the data sharing topic. In this context, Stage 2 involved the following four components:
- Views on the direct relevance of the PACE Partnership and redundancy to the specific roles and objectives of each partner organisation
- Collation of the customer information each partner collects in order to assess the potential for data matching
- Partners' response to a data sharing scenario involving anonymised matching of data (see Appendix 4)
- Desk research to explore the likelihood of newly unemployed people finding work and what this means in terms of the focus/prioritisation of the PACE Partnership /the ability to identify those people whose jobs are being made redundant who are at higher risk of long term unemployment.
At the time of these interviews (late May to early July 2020) the impact of COVID-19 was still unfolding (and still is now in September 2020) with PACE Partners and the people they assist and work with being affected in different ways. The feedback received in Stage 2 needs to be viewed in this context.
For instance, many partners did not feel that redundancies had increased significantly as a result of COVID-19, however most recognised that this was because the UK Government's Job Retention Scheme had protected many employees from this outcome. It was also clear that many PACE Partners were 'fire-fighting' at the time of interview, with advice to the recently unemployed, funding issues, strategic re-direction and support for members all at the forefront of day-to-day activities.
2.3.1 PACE Partners: potential for data sharing
During Stage 2 we asked the partners involved to provide us with the main data collection categories that they use when dealing with their clients, customers or members. Seven partners provided this information. The table below provides an overview of the data cross-overs which helps establish the potential for data sharing across PACE Partners.
|Name||Address||DOB||Phone||Gender||Disability||Empl. status||NI number|
|COSLA / BG||√||√||√||√||√||√||√||√||x|
|Unite / SUL||√||√||√||√||√||√||√||x||x|
The table indicates that all partners collect basic personal information from clients (Name, Address, Phone, Email), and the majority collect additional information on gender and date of birth, while around half collect data on disability and employment status.
None of the partners that provided data, with the exception of HMRC, collect clients' National Insurance numbers. The lack of consistency in the collection of National Insurance number could provide a challenge in selecting a unique identifier that would enable robust data cross-referencing between partners.
2.3.2 PACE Partners: relevance of redundancy
We conducted desk research to understand the extent to which partner organisations address the issue of redundancy in their work. Overall, this exercise highlighted that whilst some partners provide general information and advice on redundancy, none seem to have specific aims and objectives linked to dealing with redundancy. The research found that:
- The majority of partners provide general guidance around redundancy practices, such as checking redundancy rights, ensuring redundancy processes are fair, and advice for employers and employees. Partners that provide this type of information include the Department for Work and Pensions, Citizens Advice Scotland, ACAS, and STUC.
- Two partners, Business Gateway and ICAS, include PACE information and data among their redundancy resources. Business Gateway provides information around the service and support offered by PACE, as well as a series of webinars designed specifically for people facing redundancy. ICAS provides data and statistics gathered by the PACE Partnership to date, bringing the focus onto the key facts and developments around redundancies in recent years.
- Colleges Scotland, SLAED, Scottish Training Federation and STUC do not mention redundancy among their web resources.'
Feedback from partners confirmed that redundancy, although recognised as an important issue, does not feature prominently in any of the partners own key aims and objectives. One partner described redundancy as 'not our bat' summing up the fact that most partners' main focus is elsewhere, for instance with business start-ups (Business Gateway), member representation (STF, ICAS) or training (SUL).
There was also a perception (and this is supported by evidence) that in terms of unemployment, traditionally at least, people made redundant tend to move into work more quickly than others (eg compared to the long-term unemployed). Most people being made redundant find a new job within 6 months, though the impact of COVID-19 may test this pattern in the coming months and years.
Partners recognised that redundancy levels had, and would continue to, increase, so highlighting the need for support to this group. However, partners had other priorities as well with public facing partners such as CAS already seeing big rises in benefit enquiries and debt advice.
This feedback highlights how important it is to make the case for data sharing in the context of redundancy services and also stresses the need to make data sharing processes as straightforward as possible for partners whose main focus may be elsewhere. These points were emphasised further when we discussed a potential data sharing scenario with partners (see below).
2.3.3 PACE Partners: response to data sharing scenario
Partners were shown a data sharing scenario and asked how they would respond to such a request. This provided an opportunity to discuss wider issues around the feasibility of data sharing in the context of redundancy services.
1. A redundancy flag is added to your database (or other mechanism) to identify some or all individuals affected by redundancy. Consider/think about:
a. Doesn't have to be 100% coverage
b. How could this process be started eg quick wins'
c. What would be the medium/long term tasks/challenges'
2. You supply a file containing unique identifiers eg National Insurance No. for redundancy flagged individuals. No other details are supplied.
a. How feasible would this be?
b. What would the best unique identifier be'
c. How often could this task be repeated'
3. The unique identifiers you send are merged with those from other PACE partners.
4. Another unique reference is created - a PACE ID.
5. You are asked to update the file (containing your unique identifier and the PACE ID) with data from your database eg name. address. demographics, interactions etc .... You would then send the file back, whereupon the PACE ID would be used to merge it with information sent in by other partners.
d. Could you supply data at an identifiable level?
e. Would anonymised data (eg no names, postcodes) be easier to supply?
f. How often could this task be repeated?
6. our organisation would have access to this PACE database and/or would receive reports based on analysis of the collated d ata eg how PACE has interacted with your clients. on most effective interventions, on time taken to re-enter workplace, on overall impact of PACE partnership measures.
The majority of partners felt this type of scenario offered a starting point for conversations on data sharing in the context of redundancy. On reflection, for most partners, the biggest difficulty with the scenario was at the start, with the ability or willingness to add a 'flag' to databases remaining an issue. It was also clear that each organisation would need to be approached in order to have their specific perspective on data sharing understood and worked through, with this potentially involving different teams and members of staff over a fairly significant period of time.
Overall thoughts on the data sharing scenario were:
- As in Stage 1, several partners highlighted that they do not have, nor could quickly add, a 'redundancy' flag to their customer database
- Some of these organisations felt they would need to be told who was redundant and at that point they might be able to match these records to their own database
- Overall, partners felt it would be much more feasible to share data at an 'aggregate' level as opposed to an individual level
- The majority of partners were open to exploring the data sharing scenario but stressed that it would involve other colleagues/departments and require time and effort to work through what was feasible
- There was acceptance from partners that it would be possible to deliver elements of the data sharing scenario, in effect, moving from zero data sharing to 100% in stages
- As in Stage 1, many partners stressed the need to convince wider colleagues and teams about the merits of this data sharing. There was still uncertainty about how it could benefit partner organisations.
At a partner-by-partner level, responses to the scenario are summarised.
|COSLA / BG||
2.3.4 To what extent would it be possible for the PACE Partnership to identify those clients at higher risk of long term unemployment?
The scarring on employment prospects created by redundancy and long-term unemployment is well documented, as is the correlation between length of time spent unemployed, and "ever-diminishing chances of finding work". A number of theories have been put forward as to why this is the case, including "insider/outsider theory", loss of skills and motivation, and "poverty and social isolation". It is likely that a combination of all of these contribute to the negative effects of being long-term unemployed.
Catching people before this point is important, both for the individuals themselves, and economically. Intervening earlier is both more effective and less costly; capable of stopping people from ever reaching long term unemployment (LTU) in the first place. This is of direct relevance to the PACE approach – if it is possible to identify newly redundant people who are at higher risk of longer term unemployment, they can be offered more intensive early support to avoid the pain and costs of long term unemployment. It will also help to prioritise the work of partners in providing support to redundant workers.
Recognising this, a number of countries have attempted to create models capable of predicting individuals – including those who have just been made redundant – at high risk of LTU. These include France, Germany, Slovakia, the Netherlands, Australia, the USA and Ireland. In 2013, the UK Department for Work and Pensions (DWP) produced their own version of a profiling model. However, no statistical profiling currently occurs in the UK, and there are concerns that barriers to collecting and accessing data would limit its accuracy.
Most existing profiling models are complex and country specific. The methodologies used are often opaque, with little indication of how the model could be translated into a new local context. However, they do share similarities, and there is learning to be gleaned from their construction and approaches. In general:
- There is some consistency (with exceptions) in the risk factors that have been found to be statistically reliable across different models
- There are large margins of error across each model, resulting in a trade-off between capturing a greater proportion of those at risk of LTU, and 'wasting' time and resources on those who, in practice, may not become LTU. Accordingly, rankings, or at-risk scores, should be used to triage people and provide the most appropriate pathways of supporting, rather than excluding, people from a programme solely based on score. The success of the prediction is determined to a great extent by the 'cut off point' that is selected. For example, the DWP model is 33% accurate at predicting the 8% highest risk individuals, and 20% accurate at predicting the 30% highest risk individuals. However, the model is far more accurate at predicting those in the lower risk categories. It was 94% accurate at predicting the 8% lowest risk, and 96% accurate at predicting the 30% lowest risk cohort. Therefore, this is a hugely useful model for helping to prioritise who should be targeted for support services.
- In multiple studies, the role and discretion of the caseworker has been shown to be vital to the success of the model, and their knowledge has been used to complement it. This means that the professional judgement of frontline staff is important and they should able to 'override' scores that seem to them too high or too low
- Profiling models are less well equipped to compute people with multiple complex needs, reinforcing the need for caseworker discretion
- "Profiling needs to be part of an integrated and coordinated system to be useful"
- The greater the number and variety of indicators used (eg attributional, attitudinal and administrative), the greater the prediction power of the model
- Profiling information should be used to help jobseekers, but needs to be handled carefully, and not go against a strengths-based model of support.
Finally, in taking decisions about the adoption of an approach to early identification and intervention, it is important to appreciate that the choice is not binary. In other words, even if those identified for more intensive support would have found a job without it, the support is likely to accelerate their successful job finding. It is easy to underestimate the value to individuals, families and society of bringing forward job finding by even days or weeks.
Taking these findings into consideration, it seems possible and desirable to develop a checklist of risk factors to aid caseworkers in the identification of newly redundant people who are most at risk of LTU. This would include asking people about their individual expectations and confidence, as well as more quantitative questions regarding age, gender and place of residence. Such a checklist would be most useful as a guidance document and should complement caseworker knowledge and experience in identifying people at greater risk.
It can be used to help ease the burden of caseworkers as a result of high caseloads. As in the French and German profiling models, we would advise that individual caseworkers have "final discretion over the level of resources and the types of interventions offered to the unemployed jobseeker".
Stars indicate the significance of the risk factor, with *** representing the most well-evidenced factors.
|Question||Determinants of risk||Significance of risk factor|
|What qualifications do you have?||Lower level or no qualifications are substantial risk factors for LTU.||***|
|Do you have a full clean driving licence? Do you own a motor vehicle?||Possessing a full clean driving licence reduces the risk of LTU substantially, and ownership of a vehicle may reduce risk slightly.||***|
|How old are you?||Risk of LTU increases with age. Under 25s have the lowest risk of LTU, those aged 45 and over are at greatest risk.||***|
|Do you live alone or with a partner?||Living alone is associated with a greater risk of LTU.||***|
|Do you have dependants under the age of 18 living with you?||Having children living with you is associated with a greater risk of LTU.||***|
|Do you feel confident that you will be employed soon? Do you expect to find work? Do you feel that finding a job is based on luck or effort?||Less confidence, lower expectations and believing that finding a job is based on luck are all associated with a greater risk of LTU.||***|
|Where do you live?||Living in an area with higher levels of unemployment is associated with a greater risk of LTU.||**|
|Have you been actively seeking a job recently? Have you had any job interviews? Have you had contact with an employer||Greater job search activity may indicate a lower risk of LTU.||**|
|How would you rate your physical ability to do work? Do you have any disabilities? How would you rate your mental ability to do work based on the mental demands of work you are seeking?||Poor self-assessed health and having a disability may indicate a greater risk of LTU.||**|
|Was a parent/guardian in paid work until you were 16-years-old?||Having a parent/guardian in paid work until 16-years-old may reduce the risk of LTU. Having a father with A-level qualifications may reduce the risk of LTU slightly.||*|