Wednesday, 21 January 2015

Needs a diff-in-diff

I'd love to see somebody else head into the Stats Datalab and do a bit more digging into recent declines in teenage fertility rates.

Teen fertility rates are down but we aren't entirely sure why. The new report commissioned by the Social Policy Evaluation and Research Unit notes increased use of contraception but doesn't have any clear reason why contraception use has increased. They note the link between deprivation and higher birth rates; it would be interesting if they presented results sorted by income cohort.

The time path has a sharp drop from the 1970s through the early 80s, then a slow decline, then a sharp rise from '05 to '08, then a reasonable decline since '08.

I wonder whether changes under National restricting the generosity of benefits paid to single mothers who have an additional child while on benefit have had an effect on teen birth rates.

Somebody with DataLab access could check:

  • differential effects on teenage fertility of the changes to benefits by comparing cohorts likely to access benefits conditional on childbirth with higher-income cohorts unlikely to do so;
  • differential effects of easier morning-after pill access in Auckland, later rolled out to other cities, as compared to regions where it's more difficult to access pharmacies;
Please go and make this your thesis and report back.


  1. The other thing about this drop is that it is part of a world wide phenomenon. This suggests that local changes (eg to benefit entitlements), unless they are part of a worldwide move in that direction, is unlikely to be the cause.

    There are also worldwide trends in other youth behaviour - they are drinking less, having less (and later) sex, and getting into trouble with the law less, and I would have thought that these are all related.

    I think that social workers and health professionals love to ascribe the change to their birth control. It makes them feel Wanted and Needed and Useful. I wouldn't put any reliance on this attribution without that old bugbear of all public servants -- hard evidence.

  2. Yup. Another reason you need the difference-in-difference analysis to distinguish policy effects from whatever the heck's driving the time trend.