Practice Areas
Education & Skills

by Ummuhan Bardak, Senior Human Capital Development Expert, ETF

 

Introduction

It is no secret that connecting science with policy is not easy and there is a need for reducing the gap between evidence providers (e.g. researchers) and evidence users (e.g. policymakers) to increase the capacities for effective policymaking. This is also valid in the field of skills anticipation and matching, transforming the results of skills demand research into concrete and effective actions in education, training and employment policies.

To understand the issues at play, it is helpful to use ‘Knowledge Cycle thinking’. Such thinking highlights that there are multiple phases in turning knowledge into policy, starting with knowledge creation (phase 1), interpreting and disseminating knowledge (phase 2) and finally turning the knowledge into policy (phase 3) - see Figure 1. This article focuses on what happens after producing ‘evidence’ on skills needs, on the second and third phases illustrated in Figure 1. It should be noted though that the first phase of creating evidence is not without its challenges, such as data quality and comparability issues, inadequate funding, lack of interest and/or technical capacity, ad hoc nature of analyses, weak inter-institutional coordination and cooperation. 

Figure 1: Three phases of knowledge: evidence creation, mediation, and application

Typical obstacles faced in using skill intelligence

The ETF has a long experience with skills anticipation and matching-related research, focusing on the current and future skills demand in the rapidly changing labour markets of the Partner Countries (see Skills demands analysis | ETF). Our experience shows that the use of the knowledge and data created for effective policies is not automatic and often remains challenging. 

Time constraints and the lack of a common language to address real challenges are main reasons, often leading to limited dialogue between researchers and policymakers and creating a gap between them. The mistake usually starts with the orientation of the research in the first place, when it is not linked to an existing policy, with the aim of improving people’s lives. This leads to limited or no involvement and a lack of support from the key stakeholders in the research area, so the resulting evidence lacks ownership as well as advocacy and further funding.  

Another mistake is when researchers stop their efforts as soon as they produce the evidence and publish a report. Once evidence is created, it requires real time and effort to reflect on the most effective approaches for packaging, disseminating and advocating of results. Involving all stakeholders in validating and disseminating the results can increase the outreach, but generic and non-targeted messages do not help. The results of a study may also be too general or too technical for non-experts, with low reliability or accuracy of results. 

On the side of policymakers, scattered responsibilities among institutions with ‘co-sharing’ reduce their ownership of the results, with each institution having their own priorities and feeling limited responsibility to respond to identified needs. Lack of coordination and leadership for action, weak cooperation and coordination across mandated institutions and social partners, overlaps and lack of synergies with national visions seriously hamper policy responses to evidence. Considering time inconsistency often faced between research and policy cycles, it takes time to translate findings into policy actions.

Success factors in transforming evidence into policy action

A first success factor is to conduct more ‘policy-oriented research’ which focuses on generating knowledge and insights to inform public policies, improve decision-making and address societal challenges that affect people's lives. When designing research, experts must identify and then select the real problems of people and policies as their topic and involve the main stakeholders in their research process. Their research must be oriented towards finding solutions to existing challenges, not doing research for the sake of research. 

A second factor is to allocate enough time for reflection/ follow-up of a study, as the results are often not immediately actionable. The knowledge and data of the research must be turned into ‘skills intelligence’ based on the needs of different users to assist in their decision-making.[1] Therefore, information and data need to be interpreted and packaged into intelligence for each target group. Evidence is turned into products with end-user-friendly language for specific target groups such as: ministries/ institutions, companies, schools, young people, jobseekers, students, parents, etc. 

A third factor is a careful selection of key policy messages from the results. Studies often include a long list of recommendations that are out of touch with policy. Prioritisation is necessary as fewer and simpler actions are more feasible to achieve. Moreover, recommendations often involve a trade-off among different policy options and giving up something in return for getting something else – which must be recognised in the key policy messages. 

Initiating policy advocacy is a fourth factor, e.g. engaging in a dialogue to make decision-makers take ownership of proposals and subsequently act upon them. A process of coalition-building among institutions and stakeholders could help to reach policymakers with a common agenda. This might involve capacity-building of policymakers (skills, knowledge and attitudes) in understanding and using evidence in the policy cycle. Forming alliances (public, private, NGOs, media) is an essential part of the policy advocacy process. 

Recommendations to link researchers and policymakers

Bridging the divide between scientific knowledge and policymaking requires actions to address time constraints and the lack of a common language between researchers and policymakers. Once produced, evidence must be disseminated in several written formats, such as (lengthy) analyses like reviews and evaluations, as well as brief texts like policy briefs and blogs. Here is a short and interesting guide which explains how research projects can create a clear, impactful policy brief that informs policy: Sharing evidence with policymakers - Publications Office of the EU. 

Another way of dissemination is ‘dialogue-oriented formats’, such as workshops, invited talks, expert discussions and hearings in parliaments. One effective way is organising policy-to-science dialogue schemes, e.g. policymakers (and practitioners) submitting a list of questions/ interests to the universities/ researchers and organising short face-to-face meetings of one hour between policymakers and multiple researchers. With this, policymakers get access to scientific knowledge, and scientists learn policy perspectives on their research (capacity building).  

Bringing researchers and policymakers together is essential to reduce the gap between these two worlds and initiate a collaborative framework for effective policies. The European Joint Research Centre (JRC) developed a webpage to share ‘Evidence-Informed Policy Making’ tools (Evidence-Informed Policy Making) to help researchers and policymakers better connect scientific knowledge and policymaking in the EU. Particularly interesting are the two competence frameworks developed (one for researchers and another for policymakers), the training tool and the self-reflection tool listed below. 

 

References for further reading 

[1] Skills intelligence is the outcome of an expert-driven process, drawn from multiple sources and adjusted to the needs of different users. It not only identifies needs but also provides information and evidence for stakeholders to make better-informed decisions on skills supply and demand.

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