In the world of statistical databases, jam values are common. They are hard-coded information displayed instead of derived measures. They are used to represent unique situations where either the information to be conveyed is an explanation for the absence of data, represented by a symbol in the data display, such as a dot “.”, or the information to be conveyed is an open-ended distribution, such as 1,000,000 or greater, represented by 1000001. Even an empty value or a zero (“0”) will often have a special meaning.
Jam values can also be used to explain why information cannot be disclosed, such as for privacy reasons or because the data does not meet certain filtering criteria.
Data definitions are provided with jam values and normally they are not difficult to utilize. Depending on the parameters of the project, some may even choose to ignore them altogether. However, it is important to understand that they exist, and are not treated like numbers in tabulations and analysis.
Special consideration must be taken when importing data with jam values because they are often in alpha form. Non-numeric values will be converted to zeros (0) if appended into fields that accept only numeric information.
Some users convert jam values to special numbers during the import process so numeric fields can be used. Numeric fields are easier to work with because they do not have to be converted when counted or used in calculations.
You will find jam values frequently employed in American Community Survey (ACS) estimates and margins of error as well as in our own ACS demographics product, pdACS2013.