Disclaimer: this answer comes from a microeconomic research perspective. Time series / macroeconomic specialists will likely have other perspectives.
There is no general rule for what's too low across the entire field of economics. Yes, microeconomic models (i.e., individual-level observations) will tend to give low R-squared values (often in single percentage point digits) because there are so many factors that can affect human outcomes, a lot of which just cannot be observed. But more generally, R-squared will depend on your data and model, specifically the nature of your dependent variable, any transformations you may have applied to the variable, what explanatory variables you include and what variables you exclude.
In Economics research, R-squared is rarely of concern because predictive power is often not the main goal. Instead, economists focus on finding reliable estimates of coefficients for variables of interest and deriving useful inferences from the structure of the model and the estimated values of its parameters. And standard error of the regression is considered a better metric than R-squared because it comes in the units of the dependent variable, provides the absolute measure of the typical distance that the data points fall from the regression line, and it scales the width of all confidence intervals calculated from the model.
Source for some of the points included in this answer: https://people.duke.edu/~rnau/rsquared.htm