I'm not exactly sure what your data is, but you probably will have some collinearity within your independent variables. Your output is typically determined by the combination of labor and capital within a production function, so your coefficients on output, labor, and capital will be underestimated. Whenever modelling your data, I would suggest to start by doing a DAG (Directed Acyclic Graph) because it allows you to think through and visualize how each variable might have a causal effect on another. Take a look at this great primer on DAG: [https://mixtape.scunning.com/dag.html]
Also, whenever you have a panel data set, you should utilize one of the most important benefits of panel data, and that is controlling for unobservable factors between each of your cross sectional groups. This is simply done with a categorical dummy for each group.
Honestly, without other information about the panel data set you are using, and what exactly each one of your variables represents, it is difficult to give any specific insights. It appears your model is very similar to a production function, but typically the dependent variable is output and not expenditures.