Kevin Lewis points us to this article, “Do US TRAP Laws Trap Women Into Bad Jobs?”, which begins:
This study explores the impact of women’s access to reproductive healthcare on labor market opportunities in the US. Previous research finds that access to the contraception pill delayed age at first birth and increased access to a university degree, labor force participation, and wages for women. This study examines how access to contraceptives and abortions impacts job mobility. If women cannot control family planning or doing so is heavily dependent on staying in one job, it is more difficult to plan for and take risks in their careers. Using data from the Current Population Survey’s Outgoing Rotation Group, this study finds that Targeted Restrictions on Abortion Providers (TRAP) laws increased “job lock.” Women in states with TRAP laws are less likely to move between occupations and into higher-paying occupations. Moreover, public funding for medically necessary abortions increases full-time occupational mobility, and contraceptive insurance coverage increases transitions into paid employment.
Here’s what they did:
Of course I think they’re making a (common) error when they say that certain coefficients “should be statistically significant.”
Setting that aside, it’s hard for me to say without a more careful look. The effect seems reasonable but the analysis has lots of moving parts so lots of ways things could go wrong. I’d like to see some graphs of raw data building up to their final analysis, rather than just seeing the results presented as a fait accompli.
One thing that we don’t really train researchers to do, is to understand and explain fitted models. All those steps that should take you from the raw data, through simple comparisons, to more complicated inferences. We did some of that in our stop-and-frisk paper but we never set it up as a general method.