Lessons learned & guidance for implementation

Lessons learned – How to do employment diagnostics?

ILO’s approach to employment diagnostics lies on basic principles of being holistic, human-centric, and participatory in nature. While these common principles guide the work, the studies can take different shapes and forms depending on the local context in which they are applied. ILO’s extensive experience in doing country analytics from our 107 country offices provide a wealth of lessons that guide further analytical efforts.


On this page, ILO specialists share their insights from recently conducted country studies to answer the frequently asked questions and issues that might arise when embarking on journey towards better analytics.
 

First lesson: Timing, context, and approach needs to be tailored to the task at hand
 
While there are common frameworks and guidelines for employment diagnostics, each country study will be different. The methods used and the focus of the study will be determined by the situation in the country and the process where the diagnostics will be used. Listen to Sangheon Lee, Director of the Employment Policy Department at the ILO talk about his key lessons from conducting employment diagnostics that lays out key aspects to bear in mind when planning a study.


Second lesson: Different data sources

Many of the employment diagnostics use Labour Force Surveys (LFS) as their primary source of data. However, in situations where a recent LFS is not available, or need to be complemented, other sources of data will need to be used to complement the analysis and to be able to answer the questions posed in the study. For potential sources of data, please see the section on different data sources. Listen to Sara Elder, Senior Economist at the ILO regional office for Asia and the Pacific talk about what to do when data is scarce.


Third lesson: Dealing with multiple crises requires a systematic approach to complex problems
 
In the current context of multiple crises employment challenges are intertwined with climate risks, geopolitical challenges, and economic downturns. The policy makers will need to deal with these challenges simultaneously and find solutions that account for the links between the different thematic areas. Applying a systematic approach to the analysis and viewing the employment challenge in the light of multiple overlapping crises can pave the way for a better and more integrated policy advice. Listen to Kinan Bahnassi, Senior Specialist on Labour Market Policies and Employment talk about their experiences from analysing the situation in climate-risk prone Pacific Island Countries.



See further material including in-depth interviews and studies that compile lessons from a range of analytic efforts in the ‘Key resources’ on the right and ‘What’s new’ sections below. For guidance on how to integrate thematic considerations such as focus on gender dynamics, impact of digitalisation, further analysis of climate and environmental degradation on the world of work, or specific considerations for conflict settings into employment diagnostics, see the thematic deep dives section.

Process – How to implement employment diagnostics

ILO’s approach to employment diagnostics follows a participatory process involving constituents (government, and workers’ and employers’ organizations), along with development partners (UN, multilaterals, bilaterals), academics, thinktanks and NGOs, where relevant. This process ensures that relevant information is embedded in policy responses linked to crises and shocks, while supporting different views that have bearing on the analysis. While the conclusions of the study should be objective and fact-based, the consultative process during the analysis will enrich the insights and ensure ownership among actors who can act based on the study findings.

The key steps when conducting an employment diagnostic study include:
  • Establish a task team: Undertaking employment diagnostics requires planning and resources. When possible, establishing a task team early on, composed of international and national labour market and employment specialists and development economists, will facilitate the work and ensure that different skills are covered.
  • Collect and review existing evidence, data, and studies: Before starting the analysis, it is necessary to identify and review existing recent diagnostics and impact assessments on the economy, employment and labour market. Also, discussions with key partners will be helpful to set the scene for the analysis.
  • Define the reference points for analysing change: To be able to analyse change and monitor recovery, it is important to understand the reference points before a crisis, at the peak of a crisis, and the present.
  • Collect the data and tabulate core tables for analysis: Once the framing of the research question has been established, it is time to collect and sort the data for analysis. What do the data tell you? Is it in line with the information from previous studies and from partners?
  • Write up the analytical conclusions that arise from the data and other available material: Write up the analysis based on the data and background information received from the key stakeholders. Using both quantitative and qualitative sources of data will help to deepen the understanding of the local context.
  • Validation and consultation with national constituents: Presentation and discussion of main findings and conclusions at a meeting with constituents are important for validation of the results. Preferably this could take place in form of a joint analytical workshop. Here, the conclusions can be discussed in light of the government strategies, policy notes, and other relevant frameworks that have bearing for the policy actions ahead.
  • Finalise the report and disseminate widely: After validation and consultation, the finalised report can be disseminated widely to all stakeholders and interested parties. Making sure the analysis is available to decision-makers and development actors, as well as general public contributes to transparency, mutual learning, and knowledge generation.