1. What would you use the data for? Straightforward secondary analysis –To assess theoretical accounts –To quantify characteristics or behaviours –To challenge official views –To apply alternative definitions Context to your own primary research –Your research could be quantitative or qualitative –To assess the national context of an area study –To assess whether your sample is typical –To assess the scale of behaviours

2. The Research Use of the Government Surveys: Why use the data? Already seen that the data are: – Free to academics and easy to access – Good quality with good documentation Also: – Allows comparison over time – Large samples – Hierarchical – Flexible

3. Using and producing the time series Economic activity rates: 1,2 by sex United Kingdom Percentages 1 Males aged 16-64, females aged 16-59. The percentage of the population that is in the labour force. 2 Data are seasonally adjusted, at spring. Source: Labour Force Survey, Office for National Statistics

4. Hours of work in reference week by employment and sex Source: Labour Force Survey (Computer Files) 1981, 1991 and 1996 Base: all women 16-59, all men 16-64 (including students)

5. Before and after policy analysis Ginn – interested in impact of change in pensions policy in 1988 GHS collects information about pensions as well as socio-demographics & employment Compared data for 1987 with 1993/4 Source Ginn in Gilbert (2001) Researching Social Life

6. Using successive cross- sectional data over time Pros… Reasonable amount of comparability Can pool years/quarters Data is representative at each time point Good at looking at impacts on groups Cons… Limits to continuity in the data (e.g. ethnic) Cannot establish individual change –Cannot look at dynamics –Unlikely to be able to look at individual process (e.g. modelling with predictor variable)

7. Hierarchical data Data: GHS, LFS, EFS, FRS, HSE, SARs (BCS) Other levels – benefit unit, event

8. Use the hierarchy to… Better describe the household Describe the household context of an individual Look at intra-household differences (& sameness)

9. Describing the household e.g. Is the household deprived / in poverty? Equivalising income (e.g. FRS) –Need information on total income (all members not just Household Reference Person) –Need information on household composition Identifying workless households –E.g. Gregg and Wadsworth (1999)

10. Source: Richard Dickens, Paul Gregg and Jonathan Wadsworth (2000) New Labour and the Labour Market, CMPO Working Paper Series 00/19 Table 5

11. The effect of partnership (mothers)

12. Looking at small populations Only the Samples of Anonymised Records have larger sample sizes Many surveys with 10+k respondents –Permits minority groups to be represented –Rare subpopulations sample size may be too small… can consider combining years if appropriate

13. Blackaby et. al. (1999) Unemployment Among Britains Ethnic Minorities The Manchester School 67(1): 1-20 Combined Annual LFS data 1987-91 to give: 100,000 men 2,716 Indians 1,575 West Indian/Guyanese 1,495 Pakistani/Bangladeshi Used data to model (probit) each ethnic groups employment separately Concludes : W.Indian/Guyanese appear to suffer discrimination Pakistani/Bangladeshi education appears to have least benefit If Indians/Pakistani/Bangladeshi groups had the same charactersistics as whites they would not have higher unemployment Combining datasets to increase sample size

14. How has the data been used? Lists of publications based on the data are available on the ESDS Website CCSR will be looking for others…

15. Summary Use the data as the heart of a project or to give context to a primary study Key strengths –Flexibility –Comparison over time –Sample size –Hierarchy Need to be clear what you need before deciding which dataset to use, and look at the documentation beforehand