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Developing a Socio-Economic Dataframe

Developing a Socio-Economic Dataframe. AIM: Construct, test and refine a framework for the collection and management of socio-economic fisheries data Make recommendations on how it could be operationalised – especially when making policy. Rationale.

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Developing a Socio-Economic Dataframe

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  1. Developing a Socio-Economic Dataframe • AIM: • Construct, test and refine a framework for the collection and management of socio-economic fisheries data • Make recommendations on how it could be operationalised – especially when making policy.

  2. Rationale • Commitment within the CFP to take account of social, economic and environmental factors in a “balanced manner” (EC 2002) when taking fisheries management decisions. • No system for monitoring and analysing social and economic circumstances of fisheries communities and sectors, so commitment not met • Project to develop methodology for systematic and consistent analysis of social and economic implications of fisheries management policy

  3. Figure 1 – Data collection process and purpose overview Dataframe Concept

  4. The Work • International team – UK, Holland, Denmark • Looked at industry, community and institutional factors that assessments of the socio-economic implications of policy need to consider • Comprehensive literature search to review the collection, management and use of socio-economic fisheries data around the world • Field research in Amble, Peterhead and Shetland to test draft Dataframe • Project workshops to develop and refine structure

  5. Literature Review • Institutionalisation of socio-economic analysis requires prioritisation in terms of time and resources at policy level • Local participation important in data collection and management BUT • Socio-economic expertise is also necessary to ensure correct interpretation of collected data • Industrial, community and institutional information is already used in fisheries management decision-making and can be organised, accessed and understood via systems of databases, indicators and profiles • Community Profile system in the US a good example

  6. Field Research • Peterhead, Shetland (Lerwick) and Amble • Look at how accessible and well documented socio-economic data is within fisheries and communities • Assess the utility of the Dataframe concept in practice • Found data at a range of scales, at diverse locations, and with high degree of incompatibility and discrepancy • Data sources include government statistics on catching sector and general population, public websites for institutional information, eg LAs, and local knowledge for non-fleet fishery sector and social network data

  7. Sorting the Data • Data inserted into draft Dataframe, analysed and refined during two workshops. Finalised with two main components: • Community and sectoral socio-economic profiles, underpinned by a full-scale baseline study of fishing communities and sectors • Seven socio-economic indicators related to industry, community and institutional spheres, underpinned by annual quantitative and qualitative data-gathering processes, such as the EU Data Collection Regulation

  8. The 7 Indicators • Industry: Profitability, Employment, Economic value • Community: Population, Social well-being • Institutional Arrangements: Social policy, Fisheries governance • Requires quantitative data (eg under Data Collection Regulation) and qualitative socio-economic data. • Requires data to be collected at the community scale • Without local-scale data, the analysis of socio-economic impacts of policy on fishing communities would not be possible

  9. L1 L2 L3 L4 Figure 2 – Illustration of the four layers of the dataframe

  10. Conclusions • Multi-layer Dataframe combined with systematic data-gathering process to ensure utility and durability of the Dataframe for intended uses: • Strategic policy development • Socio-economic impact assessment • Improve capacity of managers and communities to maintain information

  11. Recommendations • Request amendments to the Data Collection Regulation for inclusion of specific data • Establish quantitative and qualitative data-gathering mechanisms for data aspects not currently included under the Data Collection Regulation • Develop technical structure of Dataframe and its user-interface

  12. Outcome • Achievement of recommendations will enable governments, managers, resource users, community organisations and stakeholders to propose and make long-term policies that are more socio-economically sensitive to fisheries and fisheries communities and sectors

  13. Next Steps • Identify possibilities for research, collaboration and action in the EU • Involve governments, research institutes? • Potential to suggest research to Commission? • Review of work already undertaken? • Discuss…

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