Fewer children than expected: updating plans, or failing to realise ambitions?

1. Fewer children than expected:updating plans, or failing to realise ambitions? Maria Iacovou and Lara Tavares Institute for Social and Economic Research Essex University REPRO project, EU FP7 programme BSPS conference, September 2009

2. Motivation • Women (and men) consistently end up with fewer children than they say they want at the start of their reproductive years • Van Peer and Rabusic (2008) Quesnel-Vallee and Morgan (2003) • In the context of falling and below-replacement birthrates…. • Often conceptualised as an “unmet need” for children related to constraints (economic, social, biological, etc) • Goldstein et al. (2003) • Question: to what extent should we really think of the shortfall between expected and realised births as an “unmet need”… • …rather than people just changing their minds?

3. Theory of planned behaviour (Ajzen 1991)

4. Life span theory of control (Heckhausen 1999) Goals Primary control strategies Selective primary control; Compensatory primary control Failure Success Compensatory secondary control Secondary control strategies Selective secondary control

5. Revising expectations • Substantial literature on the formation of expectations • Sizeable literature on the relationship between expectations and outcomes • Much smaller literature on revisions of expectations over the life course • Lee (1980) moving target model • Several studies arguing that they probably occur: • Smallwood and Jefferies 2003, Voas 2003, Rabusic 2008 • Some studies showing that they do occur • Berrington (2004) uses BHPS, examines changes between 1992 and 1998 • Miller and Pasta (1995) and Van Peer (2002) • Very few studies looking at the determinants • Liefbroer (2008) Longitudinal cohort data over 18 years in Netherlands, random slopes model of family size intentions. • Heiland et al. (2008) West German longitudinal survey, fixed effects model of desired family size.

6. USPs • Analyse increases in expectations separately from decreases • Analyse men separately from women • Model events explicitly – model finding a partner separately from losing a partner • Incorporate analysis of partner characteristics

7. Data: the BHPS • About 10,000 interviewed individuals in 5500 households • 1st Wave in 1991, wave 17 in 2007 • Will continue as part of Understanding Society • Wave 2 (1992): fertility module • Info on every child ever born to (or fathered by) sample members – including those who died, were adopted, etc. • Household grid • Age, sex, relationship to other household members • Can merge birth histories file with household grid to get complete picture of births to each person at each wave

8. Create variable “nchild”, from fertility module Copy nchild to next wave Copy nchild to next wave again Create variable “newbaby” if we observe in household individuals who: - Are present in the data for the first time at W3 - Are listed in h/h grid as natural child of the individual - Have been born after the previous interview Add “newbaby” onto “nchild” for W3, to get a value which includes new children Procedure is robust to waves being skipped – preserves sample sizes Measuring actual fertility W5 W6 W1 W2 W3

9. Measuring desired fertility • Wave 2: • Do you think you will have any (more) children? • How many (more) children do you think you will have? • Slightly unclear in respect of pregnant women • Repeated at waves 8, 11, 12, 13, 17 • Wave 11: only for Scottish and Welsh booster samples • Data are very under-used (really, only Berrington 2004 for this purpose) Medium-term changes: 5- or 6-year intervals Short-term Long-term: 15-year interval

10. “ Women: falls by 0.1 age 18-27 0.2 age 27-33 0.1 age 33-44 Men: falls by 0.3 age 18-27 0.1 age 27-33 0.2 age 33-34 Suspect male under-reporting More modest fall than Liefbroer (2008)

11. Expectations and outcomes - women Other groups Sample: 585 women

12. Expectations and outcomes - men Other groups Sample: 457 men

13. As % of all men and women: • (compared with 11% of both sexes over-performing)

14. Updating intentions Other age groups Changes in 5- or 6-year gap between waves 2-8; 8-13; 11-17 • 12.5% increase expectations • 1% by >=2 • 19.3% reduce expectations • 4.3% by >= 2

15. Changes in expectations over 6 years • 19.5% increase expectations • 3.6% by >=2 • 27.3% reduce expectations • 9.0% by >= 2 • 53.2% remain stable • 16% had intention = nkids at initial wave • 12.5% increase expectations • 1.0% by >=2 • 19.3% reduce expectations • 4.3% by >= 2 • 68.2% remain stable • 41% had intention = nkids at initial wave • 6.1% increase expectations • 0.5% by >=2 • 7.8% reduce expectations • 1.4% by >= 2 • 86.2% remain stable • 76% had intention = nkids at initial wave

16. Of those who had not attained their intentions in the initial wave… • 12.3% increase expectations • 1.0% by >=2 • 33.0% reduce expectations • 10.8% by >= 2 • 54.7% remain stable (cf 53%) • 10.2% increase expectations • 0.2% by >=2 • 33.6% reduce expectations • 7.6% by >= 2 • 56.1% remain stable (cf 68%) • 8.8% increase expectations • 0.6% by >=2 • 35.1% reduce expectations • 6.2% by >= 2 • 56.4% remain stable (cf 76%)

17. Multivariate model – revising expectations • Change in expectations, treating increases as equal and opposite to decreases • Discrete dependent variable panel data models • Ordered logit regressions • Multinomial logit regressions • Multinomial logit ideal for 6-year intervals • Create a variable taking the following values: • -1 for revising expectations downwards • 0 if expectations stay the same • 1 if expectations increase

18. Explanatory variables • Quadratic in age • Ethnicity • Partnership • Never had one • No partner at T1, new partner at T2 • Partner at T1, same partner at T2 • Partner at T1, different partner at T2 • Where people get new/different partner, would like to know whether these new partners already have children – difficult but not impossible with these data. • Partner’s expectations • Education • Indicator of attachment to labour market / opportunity cost of having children • Income and partner’s income • Children • How many at T1 (by itself, and in relation to T1 expectations) • Children born since T1 • Also try: • Age of youngest child, health • Interact all variables with age

19. Multinomial logit regressions – whole sample

20. Add age interactions

21. Include variable indicating intentions • Age variable still goes the “wrong” way • Problems with including initial intentions

22. Include variable indicating difference between intentions and number of children • Explanatory power increases • Age still “wrong” • Greater shortfall: higher prob of revising downwards, specially in older women

23. Finally, restrict to those who have not yet achieved desired fertility

24. Conclusions • Upward and downward revisions are not symmetrical • Upward revisions: • To accommodate partner • In conjunction with births • Downward revisions: • To accommodate partner • On loss of partner • With own income (for women) • With partner’s income (for men) • Men and women are not the same • Women’s income leads to lower fertility intentions, men’s does not. • Greater degree of movement by men, if the woman wants more children • Men who have not attained desired fertility are less affected by age