Thanks for this! It seems like much of the work that went into your CEA could be repurposed for explorations of other potentially growth- or governance-enhancing interventions. Since finding such an intervention would be quite high-value, and since the parameters in your CEA are quite uncertain, it seems like the value of information with respect to clarifying these parameters (and therefore the final ROI distribution) is probably very high.
Do you have a sense of what kind of research or data would help you narrow the uncertainty in the parameter inputs of your cost-effectiveness model?
I’m not convinced that our CEA is particularly useful for more generalised interventions. All we really do is assume that the intervention causes some growth increase (a distribution rather than a point estimate) and then model expected income with the intervention, with the intervention 10 years later and with no intervention. The amount the intervention increases growth is the key parameter and is very uncertain so further research on this will have the highest VoI, but this will be different for each intervention. We treat how the intervention increases growth as a black box so I think looking inside the box and trying to understand the mechanisms better would shed some light on how robust the assumed growth increase is and how we might expect it to generalise to other contexts.
Furthermore, we only model the direct benefits of the growth intervention. In general, I’d expect the indirect effects to be larger and our modelling approach doesn’t say anything about these so I expect looking into these indirect benefits, perhaps via an alternative model, to have higher VoI than further modelling of the direct benefits.
For charter cities in particular, we could probably further tighten the bounds on the direct benefits by getting more rigorous information on city population growth rates and the correlation between population growth and income growth.
Thanks for this! It seems like much of the work that went into your CEA could be repurposed for explorations of other potentially growth- or governance-enhancing interventions. Since finding such an intervention would be quite high-value, and since the parameters in your CEA are quite uncertain, it seems like the value of information with respect to clarifying these parameters (and therefore the final ROI distribution) is probably very high.
Do you have a sense of what kind of research or data would help you narrow the uncertainty in the parameter inputs of your cost-effectiveness model?
I’m not convinced that our CEA is particularly useful for more generalised interventions. All we really do is assume that the intervention causes some growth increase (a distribution rather than a point estimate) and then model expected income with the intervention, with the intervention 10 years later and with no intervention. The amount the intervention increases growth is the key parameter and is very uncertain so further research on this will have the highest VoI, but this will be different for each intervention. We treat how the intervention increases growth as a black box so I think looking inside the box and trying to understand the mechanisms better would shed some light on how robust the assumed growth increase is and how we might expect it to generalise to other contexts.
Furthermore, we only model the direct benefits of the growth intervention. In general, I’d expect the indirect effects to be larger and our modelling approach doesn’t say anything about these so I expect looking into these indirect benefits, perhaps via an alternative model, to have higher VoI than further modelling of the direct benefits.
For charter cities in particular, we could probably further tighten the bounds on the direct benefits by getting more rigorous information on city population growth rates and the correlation between population growth and income growth.