Using socioeconomic data in teaching and research

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Teaching with Data - ESDS International and World Bank project

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  • The map shows the global pattern of downloads for the World Bank World Development Indicators accessed through ESDS International. A country was counted each time data for that country was downloaded as part of a multi-national download between September 2007 and August 2009. India was the country with the highest number of downloads during the period, followed by the United Kingdom, Mexico, Brazil and South Africa. The data comes from the ESDS International web server logs. Looked at total number of selections China came top which suggested people were looking for data on China but not finding what thye wanted.It wa during a period of a great crisis over sharp increases in the price of basic foodstuff you may remember the riots We wrote a paper about this which has been published by the OECD
  • GDP can be measured in different ways – producing very different pictures of global outputs. So using GDP by exchange rate data – high income countries accounted for 76% of global output, whereas using the GDP by PPP reduces their share to 57%, whilst that of the lower middle income group increases from 12 to 24%. So this illustrates how perception of the world, and policy preferences, change according to data selection. In this case – i.e. GDP – GDP by PPP is not appropriate for evaluating financial resources, although PPPs are necessary for making meaningful international comparisons of welfare or standards of living.
  • Using socioeconomic data in teaching and research

    1. 1. Using socioeconomic data in teaching: a tale from the UK Data and statistical skills for UK social science students<br />Dr Jackie Carter<br />Learning and Teaching &<br />Social Science Data Services<br />
    2. 2. The UK context<br />Data services and infrastructure<br />Quantitative skills deficit in UK Social Sciences<br />A project with the World Bank<br />What we did and what we found out<br />What’s next<br />How data services can help address the ‘quantitative skills deficit’ in UK Social Sciences<br />Overview<br />
    3. 3. Economic and Social Data Service<br />UK National Data Infrastructure<br />Census Programme<br />Funded at national level by Economic and Social Research Council (ESRC) and Joint Information Systems Committee (JISC)<br />
    4. 4. Socioeconomic Data Services at Mimas<br />Census Dissemination Unit<br />ESDS International<br />Time series data from Intergovernmental Organisations<br />Census Aggregate data since 1971<br />
    5. 5. Quantitative Skills deficit (1) <br />'critical deficit in quantitative skills within the UK’ <br />Commission on the Social Sciences (2003) <br />quantitative social science a ‘strategically important and vulnerable subject’ <br />HEFCE (2005)<br />
    6. 6. Quantitative Skills deficit (2) <br />www.esrc.ac.uk/funding-and-guidance/funding-opportunities/15407/latest-opportunity-13.aspx<br />http://www.hefce.ac.uk/news/hefce/2011/sivs.htm<br />
    7. 7. Royal Statistical Society: getstats<br />www.getstats.org.uk<br />
    8. 8. ESDS International<br />Evidence of use from researchers<br />CDU<br />
    9. 9. Point One<br />Point One One<br />Point One OneOne<br />Point One OneOne<br />Point One One<br />Second Point<br />Third of the points<br />Another point, the fourth one<br />The student experience<br />Higher skills and employability<br />Gathering evidence<br />"The cutting edge of social science is reliant on use of real-world datasets, and there is a great desire to improve research-led teaching in this area. The UK provides access to a rich set of social science data resources with ESDS regarded as a jewel in the crown for the UK's social science data community.<br />"The challenge for educators in addressing quantitative literacies lies in promoting students' use of data, but the benefits in doing so can improve both academic performance and job prospects for students. "<br />Real World, Real Data, Real Stories<br />
    10. 10. Conferences and papers (including IASSIST 2010, E1)<br />Case study www.esds.ac.uk/international/casestudies/real-data/real-data.asp<br />ESDS International’s Teaching Tools (IASSIST 2011, PK)<br />Project with the World Bank<br />Open University Teaching Fellowship<br />Real World, Real Data, Real Stories<br />
    11. 11. Simon Industrial Fellowship funded <br />Promotion of research and teaching in social sciences<br />Dr Eric Swanson World Bank Group <br />Series of talks<br />Semi-structured interviews with top users of ESDS International WB data<br />The Project<br />
    12. 12. Paper accepted for International Journal of Research and Method in Education<br />Special Edition on Using Secondary Sources in Educational and Social research<br />The aim is to contribute to the methodological debates regarding the use of secondary data sources in Educational and Social research, as well as to present examples of empirical research which use this approach innovatively as the main method or in combination with other approaches<br />Carter, J., Noble, S., Russell, A. and Swanson, E. ‘Developing Statistical Literacy Using Real World Data: Investigating Socioeconomic Data Resources used in Research and Teaching<br />Further information<br />
    13. 13. Mutual interest to explore use of data.<br />To strengthen links between the service, the data providers and the data users. <br />Opening up of World Bank’s data made it timely to focus on academic users of the data in order to better understand the benefits afforded through ESDS-International, and any barriers to use.<br />To better understand what the academic community do with the Bank’s data, why they do/or don’t use it and what would help them to use it more.<br />To find out if users of one dataset tend to use other datasets too and if so what obstacles (if any) they face when accessing data from multiple domains.<br />To contribute to improving statistical literacies and capacity building; trying to learn from good practice where this exists<br />To take good practice from one discipline and extend into other social science disciplines if possible; to avoid ‘re-inventing the wheel ‘.<br />Identify users who could help to build narratives around dataset use through either case studies or contribution to the Teaching Tools area of the website through future activity.<br />Communicate the benefits of using international data in teaching and research across the social science community.<br />Project aims and objectives<br />
    14. 14. Building an International Data Community <br />Source: www.esds.ac.uk/international/news/news.asp<br />
    15. 15. World Bank Data<br />Table source: ESRC Annual Report 2009-10 www.esrc.ac.uk/_images/Annual%20Report%2009-10_tcm8-13375.pdf<br />
    16. 16. The WDI database brings together a wide variety of statistical indicators relevant for monitoring social and economic characteristics of developing and developed countries<br />Over 1200 indicators<br />Social, economic, financial, natural resources, environmental<br />213 countries<br />Back to 1960<br />User guides http://esds.ac.uk/international/support/user_guides/wb/wbwdiwe.asp<br />World Development Indicators<br />
    17. 17. Celia Russell , Paul Murphy and Islay Gemmell, What do academics want? Research requirements for cross-national data, OECD Data Forum Background Paper, Available from: http://www.oecd.org/document/45/0,3343,en_40033426_40033828_44115565_1_1_1_1,00.html(30 March 2010)<br />Future Directions for International Data IASSIST 2010 presentation (http://www.iassistdata.org/downloads/2010/2010_d2_wiseman_etal.pdf)<br />What academics do with data <br />Foreign direct investmentLand useFood exportsFinal consumption expenditureAdjusted net savings<br />India United Kingdom <br />Mexico Brazil South Africa Ghana Nigeria Kenya China United States<br />
    18. 18. Talk held at University of Manchester<br />http://tinyurl.com/3z8u9rf<br />Open Data and its consequences<br />Data sources and coverage<br />Improving statistical capacity<br />Standardizing data<br />The International Comparison Program<br />Tools and applications<br />Talks<br />
    19. 19. GDP can be measured in different ways, producing very different pictures of global output<br />Using GDP by exchange rates data, high income countries account for 76% of global output, whereas using GDP by PPP reduces their share to 57%. That of lower middle income countries doubles from 12 to 24%<br />Illustrates how perception of the world, and policy preferences, change according to data selection. GDP by PPP is not appropriate for evaluating financial resources, although PPPs are necessary for making meaningful international comparisons of welfare or standards of living<br />Example: Using GDP<br />
    20. 20. Top 40 users emailed and Quants Methods list used to identify data users<br />Resulted in 12 interviews<br />7 were PhD or Masters level students; 1 researcher (post-doc); 1 senior lecturer; 2 profs; 1 research librarian <br />Economics and Development Economics (7); Health and Pop Studies (2); Statistics (1); Finance (1); Library and Management (1).<br />Highly diverse range of topics using international data in teaching and/or research<br />Trade poverty<br />Energy poverty<br />Financial development<br />Concept of household in developing country surveys<br />Impact of family planning and reproductive health on social outcomes<br />Evasion and corruption<br />Financial regulation and credit availability<br />Labour markets in developing countries<br />Interviews with data users<br />
    21. 21. World Bank’s World Development Indicators (WDI)<br />used in Master’s programmes<br />no time to collect primary data sources<br />useful as contains long time series for a wide variety of internationally comparable indicators <br />enables students to apply statistical methods with emphasis on cross-country and panel data analysis<br />Other data used:<br />UNIDO’s Industrial Statistics<br />IMF’s International Financial Statistics and Global Financial Statistics<br />International Energy Agency datasets<br />UN Common Database<br />Some used microdata as well as macrodata e.g. Demographic and Health Surveys and Living Standards Measurement Studies (not in ESDS International)<br />Data used<br />
    22. 22. Requests<br />more data e.g World Bank’s Worldwide Governance Indicators (WGI)<br />more tools – e.g. World Integrated Trade Solution for UN COMTRADE<br />Liked a single access point and tool for macrodata – ‘good to have it all in one place’<br />Although data available on open web – would continueto use ESDS International as it adds value<br />Feedback on data<br />University of Essex, Wyvern http://www.essex.ac.uk/wyvern/documents/March_11.pdf<br />Feedback from Case Studies and training courses supports this – especially for teaching <br />
    23. 23. Access interface ‘looking a little outdated’<br />We know!<br />Evaluating other tools<br />Data download format not suitable for panel analysis<br />Training requirement<br />Feedback on usability<br />
    24. 24. Most downloaded data and imported to other software<br />Excel, e-views, SPSS, Stataand MatLab<br />Often processed in Excel first<br />Research/teaching spilt of tools and methods<br />Multiple/simple regression<br />‘think like a social scientist’ course – real world data<br />WDI particularly useful for descriptive statistics<br />Used in undergrad dissertations, and to explore substantive issues around policy formation<br />IMF IFS data also well used<br />More complex analysis at postgrad level<br />Tools and methods used<br />
    25. 25. Only started to uncover ‘tip of the iceberg’ use in teaching<br />Top down and bottom up approach helps get data used<br />Teacher/student and research as teacher role important<br />Data access – how much analysis functionality is required?<br />Librarians help support data use<br />More teaching resources and datasets<br />Single point of access – and search - value added<br />More examples outside of economics needed and at undergrad level<br />Conclusions<br />
    26. 26. SCORE (Support Centre for Open Resources in Education) fellowship <br />Future research<br />
    27. 27. Sharing Teaching Resources and Practice<br />Engaging with open educational resources (OER) and open data agenda <br /> Learning from others (ICPSR’s Data Driven Guides)<br />Sharing pedagogy as well as resources<br />Exploiting (funding) opportunities<br />
    28. 28. Thank you <br />Jackie.Carter@manchester.ac.uk<br />Twitter: JackieCarter<br />Slides available on Slideshare<br />

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