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Jacques BROSIUS CEPS/INSTEAD, Luxembourg. Policy effects of social research Labour market policies. Joint seminar March 18 – 20, 2008 on EU policy making – EU reality. www.ceps.lu. Short presentation of CEPS/INSTEAD. Public research centre in Luxembourg Socio-economic research

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Policy effects of social research labour market policies l.jpg

Jacques BROSIUS

CEPS/INSTEAD, Luxembourg

Policy effects of social researchLabour market policies

Joint seminar March 18 – 20, 2008 on EU policy making – EU reality

www.ceps.lu


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Short presentation of CEPS/INSTEAD

  • Public research centre in Luxembourg

  • Socio-economic research

  • Independent research programme +Contracts with different government departments

  • Main topics: poverty, labour market analysis, behaviour of firms, spatial aspects of the economy, living conditions

  • Our role: national research & international representation of Luxembourg


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Today’s presentation

  • The pitfalls of presenting research results to policy makers

  • Examples from labour market research


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What you learn at University (1/2)

  • At University: focus on academic research

  • Writing and presenting rules are imposed by standard layout of journals

  • Typical example:

    • Introduction

    • Literature review

    • Data

    • Theoretical chapter

    • Empirical chapter

    • Conclusions


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What you learn at University (2/2)

  • Writing style should be short and concise

  • Scientific wording and mathematical formulations are standard

  • Common terminology of your research field are known and do not need to be explained

  • Emphasis is put on the value added of your theory or your empirical results

  • There is a preselection of papers by the journals


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When you work for a policy maker

  • New public -> New rules

  • Previously: academic research communityNow : national (international) decision maker

  • The tools and methods stay the same (up-to-date methodology with best models)

  • The presentation changes!


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Real world examples

  • Three examples from real-life experiences with the decision makers in Luxembourg:

    • Are your results statistically significant?

    • Are your statistical techniques confusing?

    • Is your writing style too technical?


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Example 1: Are your results statistically significant?

  • Topic: participation rate of women in Luxembourg

  • Data used: labour force survey

  • Result: 2006 (65%), 2007 (63%)

  • Interpretation by the government: the participation rate has decreased

  • In reality: difference is not statistically significant

  • In statistics you usually have confidence intervals due to survey data

  • Difficulty: how to explain this technical aspect to a non-specialist audience


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Example 2: Are your statistical techniques confusing?

  • Topic: evaluation of active labour market policies

  • Difficulty: estimation of the counterfactual

  • We concluded: « An additional 10% of unemployed found a job thanks to the policy »

  • They concluded: « 10% of those benefitting from the labour market policy found a job »

  • We had to clarify results in meeting with Minister


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Example 3: Is your writing style too technical?

  • Topic: Why do cross-border workers come to work in Luxembourg?

  • Presentation of simple descriptive statistics with results for sub-populations: 45% of cross-border workers are interested by higher wages; 60% of these have a low qualification

  • One member of parliament said in a speech: 60% of all cross-border workers have a low qualification

  • Too many descriptive statistics seems to confuse some of the decision makers


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Avoid the pitfalls

  • The following slides show some typical situations you will be confronted with when working with government officials.

  • Suppose you are the only expert working in your country on a specific problem. This is realistic even in a large country as the UK if your topic is sufficiently specialised.

  • Let’s call the decision maker M. (as in Minister)


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  • M. does not have the technical abilities to check your results and has confidence in your research

    • Do not misuse this confidence!

    • Do not pretend having answers to everything just because you are afraid M. could believe that you are incompetent

    • Double check all your results (advantage of working in groups)


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  • M will use your results for changing policies that could affect many people in your country.

    • Be aware of the impact that your research can have for the population

    • Make sure you apply the correct methods and that your results are robust (not very large variations of results with different methods)

    • Only present results that are statistically significant

    • Search for alternative explanations for your results


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  • M. is interested by political conclusions

    • Do not just interpret the coefficients of your regression, go a step further and present policy implications

    • But do not go as far as to impose decisions; your role is to inform, the role of M. is to make informed decisions

    • Make sure to give all elements that you think are needed for making a good policy decision

    • Verify that the research topic is clearly defined between you and M. Do not announce something you cannot do (because of data limits for example)


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  • M. might want to influence your research by suggesting expected results

    • You need to avoid this pitfall by staying scientific and not trying to satisfy M. (even if M. pays the research contract)

    • Always remember that independent researcher could be asked to check your results!

    • Stay objective, avoid being subjective (also during your presentations)

    • Be aware that, in the political arena, not everyone will like your results


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  • M. has certain time constraints (governmental meetings where results should be presented, elections,…)

    • Make sure you respect these deadlines

    • Clearly explain to M. that these deadlines could limit the extent of your research

    • Also make sure M. understands your constraints: data accessibility, time constraints, budget constraints


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  • M. usually does not have a degree in statistics

    • Keep your explanations simple

    • Define the concepts that you use

    • Do not use abbreviations without defining them

    • Add a methodological appendix, detailing the more sophisticated research results that could not be put in the main text

    • You are the expert and have to decide which results are the most reliable; make sure to explain the impact of data quality, weighting, number of observations,…


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  • M. has hired you because you are the expert

    • Keep updated about the recent policy changes

    • Inform yourself about policies applied in other countries

    • Continue to read about new statistical methods

    • Try and get access to the best possible datasets

    • Use the appropriate graphical presentations


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  • M. needs to be convinced of the quality of your presentation

    • Appear confident

    • Be prepared for additional questions

    • Keep the message clear

    • Use a graphical approach

    • Avoid technical jargon


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Conclusions

  • When working with a policy maker, you need to use the tools you have learned in your statistics courses but you need to adapt the presentation of these results to the non specialist audience

  • Always keep in mind that from now onwards your research does no longer concern only yourself but it can have an important impact on thousands of people affected by a possible policy change due to your analysis


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