Neighbourhood Effects: Theory, Data, Evidence and Policy: (an exploratory analysis in relation to teenage parenthood

1. Neighbourhood Effects: Theory, Data, Evidence and Policy:(an exploratory analysis in relation to teenage parenthood Ruth Lupton and Dylan Kneale ESRC Neighbourhood Effects Seminar 4th/5th February 2010

2. Aims of Our Research • Adopt a theory-driven approach to testing for neighbourhood effects, and explore the value of such an approach • Trial the use of a longitudinal data source not previously used for neighbourhood effects research

3. The Empirical Question: • Are there neighbourhood effects (for females) on the likelihood of becoming a parent before the age of 20 (or in the first 25% of one’s age cohort)

4. Teenage Parenthood and UK Policy • A major element in New Labour’s strategy to combat social exclusion: • A marker of disadvantage • A predictor of disadvantage for teen parents and their children • Policy aims: • Halve the U18 conception rate and establish downward trend in U16 rate • Improve support for young parents (housing, employment, education) • Policy interventions: • Teenage Pregnancy Unit and strategy • Information campaigns • Strengthened advice and guidance and youth contraception services • Tailored midwifery and health visitor support, support into education and employment

5. Spatial Dimensions • Spatial Elements to Policy: • Each LA area to have own strategy • Additional funds for high prevalence areas • Pilot projects in these areas

6. Area Effects? • Policy documents make reference to area effects although give no evidence that there are ‘effects’ rather than merely a distribution. • Quantitative research tends to find ‘neighbourhood’ effects • Qualitative research shows that ‘round here’ is important, but specific geographies and mechanisms not well elaborated

7. Does it matter if there are area effects? • Common misconceptions: • If there are no area effects there should be no area-based policy • If there are area effects, they occur through peer-group effects so the best way to tackle them is through mixing communities (aka demolishing social housing estates) • We argue: • There are many rationales for area-based policy that hold in the absence of area effects (eg efficiency, local knowledge and tailoring) • But policy might be different if there were area effects • If we knew how they worked • And at which geographies

8. A critique of the existing literature • Many studies only ask whether there are area effects or not, without exploring how they work • Partly because teen pregnancy is one of a number of measured adverse outcomes of interest to people who do not study it specifically • Some studies do test specific mechanisms, usually one only • Most studies use available geographies regardless of their theoretical significance • So data-led not theory-led in respect of explanation and geography

9. Our approach • Theory driven in terms of data: • Identify possible area influences on teenage parenthood • Test them individually • Theory driven in terms of geography: • Identify theoretically relevant geographies for different mechanisms and test using these • In theory (!) this is: • Specific and accurate (a better basis for policy) • transparent

10. What leads to teen parenthood? • Calculations of opportunity cost • Values/orientations /normative practices (linked to social class or social exclusion theory) • Characteristics of social networks

11. How could area matter • Influencing opportunity cost calculations • Influencing values orientation • Providing social networks that support early fertility or discourage it

12. Policy Implications • If neighbourhood is influential because it affects opportunity cost calculations: • Education quality improvements • Labour market policies • Recognition of rationality of choices • If neighbourhood is important because it reflects different values : • Recognise validity of different perspectives • Enable alternative perspectives/individual choices • If neighbourhood is important because of characteristics of social networks: • Specific tailored interventions that recognise influences of networks/provide alternatives/strengthen advantages

14. Data: The BCS70 • Census of Births from a week in 1970 • One of four nationally representative Birth Cohort Studies • England, Wales, Scotland • Tracked at key developmental points from birth to 34 (38) • Multidisciplinary • Publically available - over 300 publications

15. Data: Advantages of BCS70 • Nationally representative (almost) • Examine birth, childhood and adolescent predictors of adult outcomes • Reduced possibility of recall bias • Increased the possibility establishing causality • Collected indicators that can’t be collected retrospectively (e.g. expectations) • Information collected from parents, teachers, medics

16. Data: The Sample • Full fertility histories collected ages 30, 34 • Teenage Parenthood defined as any live birth occurring before age 20 (10%) • Use childhood antecedents to predict teen motherhood on a sample of 4,174 (E,S,W) • Use multiple imputation model to impute missing values and maintain sample size

17. Data: BCS70 Neighbourhood • To date, no neighbourhood data beyond Local Authority made available for any sweep • Missing data on ward from Age 16 prevented usage in the past • Return to paper copies for original postcode • Original fertility sample of 6,065 decreases to 4,174 with excluded cases (missing data). • 4,174 for sub-region decreases further to 3,320 for wards (- SC, -missing) • Slightly lower proportion of teen mums (Full: 9.8%, Sub-region: 8.6%, Ward: 8.2%) in working data file – limitation

18. Data: Method • Sub-regions formed from Local Authorities • Modelled ward and sub-regional effects and characteristics in binary logistic regression models modelling the probability of becoming a teen mum versus not • Also considered early (first 25%) motherhood • Imposed hierarchical structure – necessary?

19. Data: Method II • Individual predictors include several childhood antecedent characteristics from different developmental points • Educational Expectation Measures • Socioeconomic Factors • Educational Measures • Behavioural and Philoprogenitive Measures • Home Learning Environment and Demographic Measures

20. Data: Method III • Test neighbourhood factors individually in models with individual/family level factors before introducing possible neighbourhood confounders • Neighbourhood variables reflected theory driven approach • Sub-regional models test socioeconomic (census) and attitudinal (cohort derived) predictors • Ward models test socioeconomic (census) predictors

21. Results I • Small but significant ward effect - focus on characteristics of ward • Higher proportion of young married women in ward increases the odds of becoming an early/teen mother: ↑1 S.D = ↑Odds of teen motherhood by 19% (16-19 yrs) and 24% (20-24 yrs) • Higher proportion of young women (16-24) from ward in further or higher education decreases the odds of becoming an early/teen mother ↑1 S.D = ↓ Odds of teen motherhood by 31% (Opportunity Cost – indirect effect?) • Results robust to ward social class – a different indicator • Little evidence of specific labour market effects

22. Results II • Small but significant sub-regional effect • Little evidence of a labour market effect for wards, but significant at sub-region and decreased unexplained variance – a manufacturing story? • No attitudinal variables were significant • Higher proportion of local dependency on manufacturing sector increases the odds of becoming an early/teen mother: ↑1 S.D = ↑Odds of teen motherhood by 23% (Males emp), 21% (Females emp) and 18% (Unemployed) • Higher proportion of married women in employment decreases the odds of becoming an early/teen mother ↑1 S.D = ↓ Odds of teen motherhood by 25% (Opportunity Cost?) • Results robust to social class

23. What do these results mean? • ‘Area’ seems to be important, possibly as important as some individual predictors • At ward level, area seems to be influential through local norms, values, practices • May influence opportunity cost calculations • Suggests recognition/de-stigmatisation as policy interventions? • Although at sub-regional level, labour market structure seems important, and no evidence of attitudinal/cultural effects at this geography: • Opportunity costs in different labour markets • Or ‘fit’ between class norms and industrial structures? • Or is the geography wrong?

24. Overall – hard to draw firm conclusions about how area works to influence likelihood of teenage parenthood • Except that: • Area seems to matter, and • Geographies of measurement possibly very important

25. Limitations • Many aspects of neighbourhood (eg networks and resources) are not measured • Neighbourhood effects may be underestimated as also picked up in individual ‘controls’ • Overall, quantitative testing of specific mechanisms is imperfect, since it is rarely clear what mechanism a variable is testing • Theoretically relevant geographies are unknown, and may not match on to administrative units. They may be of different sizes.

26. Next Steps • Test smaller and larger geographies together • Construct bespoke geographies by merging contiguous wards that share similar characteristics, in relation to theoretically important variables • Testing the extent to which existing typologies of neighbourhoods (the 1981 ONS ward classification and the 1981 ACORN classification) reflect characteristics relevant to teenage parenthood and to what extent they provide a useful way of classifying neighbourhoods for this kind of enquiry.

27. Next Steps • Investigate neighbourhood dynamics and their impact on the use of Census data from 1981 matched to survey data from 1986. • Use Census and Labour Force Survey data to examine the labour market characteristics of areas with different industrial structures, to better understand the relationships between industrial structure, social class and parenthood patterns. • Examine non-linear effects

28. Final thoughts • A theory-driven approach is valuable but imperfect to operationalise: need stronger qualitative work to examine relevant geographies and unpack mechanisms • BCS 70 is a valuable source, and longitudinal data is powerful. But sample size difficulties may inhibit more sophisticated methodologies • Establishing the ‘right’ geographies is difficult but necessary • Risk that policy implications are drawn from top-line findings of studies that lack theoretical approach to mechanisms or geography: What responsibilities do we have as researchers?