Learn more about LISER’s departments, shaping our contributions to societal and scientific challenges.

Discover research that examines key challenges in employment, economic trends and social dynamics within the labour market, offering valuable insights into these important areas.

Living Conditions

Engage with research that investigates the factors influencing living standards, with a focus on income distribution, housing conditions and social inclusion, providing evidence-based insights into these crucial areas.

Urban Development and Mobility

Studies urban dynamics to identify pathways of actors to stimulate sustainable urban transitions.

Appel à Communications: Les relations industrielles dans un monde du travail en mutation

Learn more about our cross-departmental programmes, which combine interdisciplinary expertise to tackle transversal challenges.

Crossing Borders

Discover how our research into the complexities of cross-border mobility is advancing knowledge, shaping policy, benefiting society and addressing global challenges.

Health and Health Systems

Engage with research dedicated to enhancing public health, improving healthcare accessibility, and promoting equitable and efficient health solutions at a global scale.

Discover LISER's initiatives, driving scientific progress and fostering meaningful collaborations with stakeholders.

Doctoral programmes

Meet a dynamic community of researchers addressing critical societal challenges through innovative and interdisciplinary studies. Our doctoral programme offers exceptional training, mentorship and opportunities to shape impactful policies and drive innovation in diverse fields.

Data Science and Simulation

The Centre of Competence for Data Science and Simulation (DSS) is a cross-departmental initiative. Our ambition is to develop infrastructure, tools and competences in in machine learning, big data, microsimulation and geoexperimentation to conduct excellent research and advise policy-makers as well as stakeholders.

cc-EXPAR - Competence Centre in Experimental and Participatory Research

The Competence Centre in Experimental and Participatory Research (EXPAR) is a cross-departmental initiative. Our ambition is to improve awareness of and skills in experimental and participatory research methods to strengthen LISER’s position at the forefront of internationally high quality, scientifically rigorous and societally relevant research.

Labour Market

Topic: Evaluation of public policies

Policy evaluation assesses the effect of public programs on participants' socio-economic outcomes in the fields of labor, education, health, family and socio-economic policies. To retrieve causal effects, it relies on counterfactual methods that compare treated and untreated groups while accounting for confounding factors.

Beyond correlation: Measuring the impact of public policies

The department assesses the impact of socio-economic programs across various domains, including labor, education, family, health, mobility, and housing. Through rigorous scientific studies on the causal effects of public policies, it provides evidence-based policy recommendations grounded in cost-benefit analyses. The objective is to assist policymakers in addressing market and policy failures, enhancing societal welfare, and developing effective responses to the complex and evolving challenges of contemporary society.

A key challenge in public policy evaluation is identifying causal effects rather than simple correlations. Public policies often target individuals with specific characteristics, making it problematic to determine policy impact by comparing the outcomes of participants and non-participants, as these groups may differ intrinsically. Likewise, evaluating participants' outcomes before and after an intervention may be misleading, as changes could result from external factors unrelated to the policy.

Randomized control trials

Randomized control trials (RCTs) are considered the gold standard for establishing causality. In policy pilot programs, participants are randomly assigned to treatment and control groups. Randomization ensures that, with sufficiently large groups, differences in average outcomes can be attributed to the intervention, as the groups are statistically equivalent apart from the treatment.

Non-experimental methods

When RCTs are infeasible, non-experimental methods are employed to approximate randomization. These include natural experiments, quasi-experiments, and observational studies. In such cases, researchers construct comparable treatment and control groups by controlling for other confounding factors that may also influence the outcome. Micro-econometric techniques are applied, including methods based on observable characteristics, such as matching estimators, and methods that address unobserved differences, such as difference-in-differences, regression discontinuity design, instrumental variables, and synthetic control method.

Evidence-based insights

By employing these rigorous methodologies, program evaluation provides policymakers with reliable, evidence-based insights, enabling more informed decision-making and ensuring that public resources are allocated effectively to achieve targeted social and economic outcomes.

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