Defining a new analysis: help defining the feature space

I am weighing creating an informal analysis of innovation and its effect on economic performance.

So far, I have the following data pulled; from a preliminary look, most datasets appear to have a large number of non-null values. I am thinking of performing OLS/Linear Regression. The data is grouped by country and would per analyzed per capita.

Independent variables:

- New patent applications(discrete)

- Average work hours per week (continuous)

- Government type (categorical)

- Social progress score (continuous)

Dependent variable:

- GDP (continuous)

However, I have two concerns. First, I would like to have more variables as inputs, as what I have so far seems to be a weak proxy for “innovation”. One option is to add in confounders (addressed below), normalize for these, and create an “innovation composite score”.

Second, if I do an innovation composite score, I am unclear exactly how to normalize the input variables based on the confounding variables. If I do not do an innovation composite score, I am also at a loss for how to add in these features into the feature space - categorical binning of a “developed” score? Am I overthinking it?

Potential confounders

- Education score (continuous)

- Income (DON’T HAVE - need to find)

- Poverty (proxied through “number of calories per day”, continuous)

- Infrastructure score (continuous)

In summary, I am looking to further define my feature space, including accounting for confounders. Thank you for your thoughts!

Sources:

New patents by country (2023, 2024)

- https://worldpopulationreview.com/country-rankings/patents-by-country

Education levels by country (2023)

- https://worldpopulationreview.com/country-rankings/education-rankings-by-country

Average hours in a work week by country (2023)