The answer to your question really depends on the specific details of your analysis. As the Cleveland et al. (2011) paper states (pp. 13):
The IPUMS samples are large, and for the great majority of studies there is little risk of drawing invalid inferences because of underestimated variance. Geographic clustering can lead to overestimated standard errors for a set of variables describing household characteristics, but analysis based on these estimates will be conservative at worst. For studies of weak relationships or small population subgroups, however, there can be risk of misleading estimates of statistical significance. The effects of clustering are of greater concern because underestimated standard errors have the potential to lead to erroneous findings of statistical significance. However, most census research has minimal household clustering because it focuses on particular subpopulations that rarely cluster in households.
Additionally, as is discussed on the IPUMS International variance estimation page:
An alternative, thanks to improvements in the analytical power of modern statistical software, is to incorporate information about sample design into estimation procedures. All major statistical software programs, including SAS, Stata, SPSS, and R, now allow researchers to specify basic elements of complex sample design. These programs make use of Taylor Series linearization to adjust variance estimates and tests of statistical significance. IPUMS users can specify the household identifier (SERIAL) as the cluster variable (or primary sampling unit) for any analysis that might be influenced by household clustering, and can also specify the weight variable (PERWT) to account for the effects of heterogeneous sample weights. The IPUMS staff is developing a cluster variable that will offer the potential for more refined variance estimates. The new variable will identify geographic clustering as well as household clustering.
As of September 2015, we have added a new variable to aid in accounting for the effects of stratification on sample variance. As discussed above, stratification improves the precision of samples, and findings of statistical significance without adjustments for stratification will be conservative. Accordingly, adjusting for stratification effects is of less concern than adjusting for clustering. The new STRATA variable includes information about explicit strata whenever such information is available, and includes geographic pseudo-strata for systematic samples following the procedure described in Davern et al. (2009).
So, if you want to ensure that your standard errors are calculated as accurately as possible, you should incorporate sampling weights (as discussed above), use the household identifier (SERIAL) as the cluster variable, and use the STRATA variable to identify stratification. You can incorporate these variables in the survey set up options in most statistical software. However, in most cases, these procedures will not make too much of a difference.