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Sub-county population estimates: new methods needed for providing more accurate data.

Article Excerpt
Editor's note: In an attempt to provide annual population data between the decennial censuses, the U.S. Census Bureau developed the American Community Survey, an ongoing statistical survey that replaces the traditional long form. The transition began in the mid-1990s and should be fully implemented by 2010.

Producing sub-county population estimates is challenging for a variety of reasons, and the Census Bureau is currently researching methods to provide more accurate data. As chairman of the Federal State Population Cooperative Program for Population Estimates, author Jim Sylvester has a leading role in this project.

When Great Falls city leaders saw the U.S. Census Bureau's latest population estimate for their city, they knew something wasn't right. The city had estimated the population at close to 59,000 while the Census Bureau estimated it much lower at a little over 56,000. The 0.8 percent population decline reported by the Census Bureau didn't make sense considering the home construction boom the residents were witnessing. So the city challenged the U.S. Census Bureau figures and found that the Great Falls city population had actually grown almost 3.3 percent, the biggest jump in population since the 1960s.

All over Montana, census population estimates are subject to similar error. Census data overestimated the city of

Bozeman's growth by as much as 180 percent, for example, while Manhattan and Three Forks were underestimated by as much as 400 percent over a six-year period. During that same time frame, census data show a population decrease in all cities within Cascade County, when in fact the towns of Belt and Cascade may have grown by about 20 percent.

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These inaccurate population estimates, along with others throughout the state, have significant implications for Montana. More than $300 billion per year is distributed to communities throughout the United States according to population size. Population data also influence policy decisions, as well as funding for programs and services such as school districts, low-income housing, highway improvements, and much more. Furthermore, businesses...

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