Using robust standard errors has become common practice in economics. Robust standard errors are typically larger than non-robust (standard?) standard errors, so the practice can be viewed as an effort to be conservative.
In large samples (e.g., if you are working with Census data with millions of observations or data sets with "just" thousands of observations), heteroskedasticity tests will almost surely turn up positive, so this approach is appropriate.
Another means for combating heteroskedasticity is weighted least squares, but this approach has become looked down upon because it changes the estimates for parameters, unlike the use of robust standard errors. If your weights are incorrect, your estimates are biased. If your weights are right, however, you get smaller ("more efficient") standard errors than OLS with robust standard errors.