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  • Propensity Scores

    I'm currently writing a undergraduate dissertation about the impact of participation in IMF programs on economic growth, and as part of it I'm trying to control for selection bias. Based off my readings so far, the best method to do this is through the use of propensity scores. Admittedly, I am learning as I go along so some help/tips/guidance or simply being pointed into the direction of some useful resources would be very useful.

    I have my treatment variable which is whether a country was in an IMF program, the outcome variable is economic growth rates and some examples of the covariates are tax revenue, domestic investment and external debt all as a % of GDP. My understanding so far is that I'll next create a propensity score matching model in Stata. This will generate some propensity scores which I can then use in a regression that includes variables that weren't used to create the propensity scores (like population because it doesn't affect the likelihood of being in an IMF program) along with the IMF dummy. The coefficient of the dummy should reflect the effect of it on economic growth.

    I'm primarily looking for reassurance that I have correctly understood the materials that I have read to do this successfully. Again, I'm a novice so I may have misunderstood some of the steps or brushed over/missed some important considerations. Any help would be greatly appreciated!

  • #2
    teffects will automate much of it.

    heckman model might work.

    Coarsened Exact Matching might also work.

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