I’m working on a project examining the effect of a 2016 cash transfer on fertility.

Who is eligible for the cash? All families with:

  • 2+ children, or
  • 1 low-income or disabled child.

The data doesn’t have a variable indicating who got the cash transfer, so as I understand it, I would be doing an “intent to treat” analysis by defining the treatment & control groups based on eligibility.

However, I keep getting stuck on how to define the treatment & control groups. I guess my question is since the cash transfer is universal for ALL families with 2+ kids, what would be the control group then? Theoretically, there should be two similar groups of families with 2+ kids (one who get the cash transfer and the other who don’t), but that’s not possible in this case?

Comparing eligible families (2+ kids or 1 poor/disabled kid) to ineligible families (1 kid that is not poor/disabled or zero kids) would violate one of the core assumptions of causal inference (that the treatment and control groups be similar and only differ in the “treatment”).

I think I’m getting tripped up by how the cash transfer is both universal and birth-dependent.

I’m exploring using a linear probability model with FE or a DID model, but not sure if a DID makes sense? Synthetic control? Any thoughts on modeling strategies?

More context: The data comes from a household survey, which I’ve organized into a panel with fertility histories for each childbearing-aged woman (e.g. each woman has 17 observations, or 18 years containing her time-variant birth information). I have data from 2010-2018 and the program started in 2016. The program grandfathers in anyone who falls in either one of two eligibility categories. The cash transfer is not means or work tested.

  • $\begingroup$ What is the geographical scope of eligibility for the cash, eg a whole country, a region within a country? That may give a clue as to a suitable control group. $\endgroup$ Jun 28, 2022 at 10:23
  • $\begingroup$ The context is a European country and the cash transfer applied to all of the country's regions equally at the same time. So unfortunately, I wouldn’t be able to use different regions as controls. $\endgroup$
    – Anna Bokun
    Jun 28, 2022 at 16:14
  • $\begingroup$ Wouldn't the ideal control group be families with 2+ children etc in another European country which does not have such a cash transfer, the country being chosen to be similar in socio-economic characteristics to the treatment group country? I appreciate however that it may not be practicable to obtain data for such a control group. $\endgroup$ Jun 28, 2022 at 18:54
  • $\begingroup$ That's my thinking too, but procurement of such data requires a rather extended process. All the same, I'm looking into it bc it seems to be the most appropriate empirical approach in this case. $\endgroup$
    – Anna Bokun
    Jun 28, 2022 at 20:21

1 Answer 1


It looks to me like your control group would be all families with 1 or 0 kids. This is not ideal as you mention.

If you observe all households before and after the transfer, then you could do a diff-in-diff.

$$y_{it} =\beta_0 +\beta_1 Eligible_i +\beta_2 Post_t +\beta_3 Eligible_i\cdot Post_t +u_{it} $$

where $Eligible_i$ is a binary variable for being eligible, and $Post_t$ is a binary variable for being after the transfer. $\beta_3$ would be your coefficient of interest.

You could also add in other controls to improve precision or if you believe the parallel trends assumption only holds conditional on other covariates.

  • $\begingroup$ Thanks for everyone’s thoughts. I also thought about using RDD, but having 0 or 1 kids vs 2+ kids is nothing like having an 85 or 86 vs 87 or 88 on a test when what's being compared is fertility…When a program is implemented in a way that is universally applied to everyone eligible, it limits which quasi-experimental methods can be applied to evaluate its effects, which is what I’m struggling with now. $\endgroup$
    – Anna Bokun
    Jun 28, 2022 at 16:16

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.