What’s Wrong with the 2020 Census — Why You Should Care and How to Deal with It

The release of the 2020 U.S. Census data marked a major shift in how population statistics are produced and protected. In an effort to strengthen privacy safeguards, the Census Bureau intentionally altered small-area data. While the goal of protecting individual identities is necessary, the scale of these modifications has created serious challenges for analysts, planners, and organizations that depend on accurate geographic detail.

A detailed review conducted by the data team at Applied Geographic Solutions (AGS) revealed that the privacy protections applied to the 2020 Census introduced far more disruption than expected. In many cases, the published data contain patterns that are not just unusual, but statistically impossible. To restore usability, AGS undertook a comprehensive effort to rebalance the census data while preserving privacy protections.

Why Census Data Looks Different This Time

Historically, the Census Bureau has taken extensive measures to protect confidentiality. Personal records containing names and addresses are sealed for decades, and when the census included both a short form and a long form, sensitive information was collected from only a sample of households. Statistical techniques redistributed characteristics across nearby areas, reducing disclosure risk while maintaining realism.

That framework changed when the long form was replaced by the American Community Survey. The modern census now consists of fully enumerated geographic units. Although this improves completeness, it also increases the risk that census counts can be combined with external datasets—such as property records or mailing lists—to infer personal identities, particularly in small areas.

To address this risk, the Census Bureau implemented a new privacy framework that injects controlled noise into the data. The result, however, is a dataset that often no longer reflects plausible population patterns at fine geographic scales.

When Privacy Protections Go Too Far

The most troubling outcome of the 2020 Census privacy approach is the appearance of impossible demographic conditions. AGS identified numerous examples, including census blocks containing children but no adults, occupied housing units with zero residents, unusually large family sizes, and even households placed over bodies of water.

Beyond these outright contradictions are far more subtle distortions. For every clearly impossible scenario, there are many more that are highly improbable. These issues become especially problematic in detailed census tables covering age, sex, race, Hispanic origin, ancestry, household size, and family structure—data that form the foundation of demographic analysis.

In fact, so much of the privacy budget was consumed at the block group level simply to release total population counts that the Census Bureau has considered limiting the release of detailed tables to the census tract level. Given the extensive reallocation required for high-level statistics, publishing finer-grained breakdowns presents an even greater challenge.

Why Small-Area Data Matters

From an operational perspective, maintaining privacy while preserving the defining characteristics of each geographic unit is extraordinarily difficult. The 2020 redistricting data demonstrate that the approach used was not limited to modest adjustments between neighboring areas. Instead, large-scale reallocations were applied, fundamentally altering the character of many census blocks and block groups.

The presence of statistically impossible outcomes makes clear that this balancing act was not fully successful.

To illustrate how these issues manifest in real places, AGS conducted a detailed examination of a single census block group in Thousand Oaks, California—a well-established residential area near the company’s headquarters. The location was deliberately chosen because it had remained stable over the previous decade and did not represent an outlier.

A Closer Look at a Stable Neighborhood

The block group examined had not been redefined since 2010, aside from minor adjustments involving unpopulated freeway-adjacent blocks. At a high level, the neighborhood appeared unchanged: modest infill development, a slight increase in housing units, and a gradual decline in average household size, consistent with national trends.

At the block level, however, the data told a very different story. Vacant housing units in the block group doubled, yet all were assigned to a single block that appeared no different from surrounding blocks. Evidence suggests these vacant units were effectively “borrowed” from nearby areas.

Even more striking, that same block experienced a sharp population increase, causing its average household size to surge from approximately 2.9 to more than 6.4 persons per household. While large households do exist, such a concentration is extremely unlikely in a long-established, upper middle-income neighborhood.

When average household size was mapped across block group boundaries, a clear pattern emerged: nearly every block group contained one block that stood out dramatically, often accompanied by unusually high vacancy rates compared to adjacent blocks.

AGS’ Approach to Restoring Usability

The introduction of a formal privacy budget adds complexity because even baseline population counts at the census block level have been altered. While the Census Bureau states that total population and group quarters counts are reliable at the block level, nearly all other variables have been modified. Significant anomalies persist even at higher geographic levels.

Because AGS conducts demographic modeling at the census block level, these distortions posed a serious challenge. To address this, AGS developed a process referred to as “balancing,” which leverages the full geographic hierarchy of census data.

State-level totals—designated by the Census Bureau as accurate—serve as anchors. These totals are used to balance county estimates, which then constrain census tracts, block groups, and ultimately individual blocks. This hierarchical approach produces block-level estimates that are internally consistent and avoid creating single blocks that bear no resemblance to their surroundings.

Why This Matters Going Forward

From a modeling and forecasting standpoint, balanced data significantly improves reliability. It reduces the risk of trending against datasets that are no longer comparable and helps restore geographic realism without compromising privacy.

While it is impossible to know the exact original census counts, AGS is confident that the adjusted dataset more closely represents actual population conditions than the published data alone. This makes it far more suitable for planning, analysis, and decision-making.

Organizations and analysts who rely on census data must understand how privacy protections have reshaped the dataset—and what can be done to correct for those changes. Addressing these issues is not optional; it is essential for anyone working with small-area demographics.

Those interested in learning more about AGS’s methodology, results, or accessing comparison datasets are encouraged to reach out for a detailed discussion.