When analyzing the impact of aging on economic growth, it is often difficult to separate the impact of policy interventions (e.g., pension systems, health-care benefit reforms, labor market policies, etc.) from the impact of demographic change itself. This leads to unclear interpretation of the findings. I am currently using the econometrics of Difference-in-Difference or panel data fixed effects model to control policy effects, is there any other way besides these?
Maybe there is some more elegant way of doing it, but I'm used to idea of tossing into initial model both the suspected predictor that I want to test and the other explanatory variables that I should control for. Later, I eliminate that what lacks statistical significance (plus check for collinearity).
It is a complex topic, which seems to me to be found more in the design of your research. In the case of Mexico, the current government has promoted universal basic income through economic incentives to many groups, modified the pension system, giving more burden to the state, while on the other hand it neglected the health issue. That is, the control and treatment studies, the temporality of these supports, as well as the demographic, economic and political variables are relevant in this research design.
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