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You are here: Census.govBusiness & IndustryFederal, State, & Local GovernmentsGovernment Employment & PayrollHow the Data are Collected › Data Processing

Data Processing: 2013

Editing: Editing is a process that tries to ensure the accuracy, completeness, and consistency of survey data. Efforts are made at all phases of collection, processing, and tabulation to minimize reporting, keying, and processing errors.

Although some edits are built into the Internet data collection instrument and the data entry programs, the majority of the edits are performed post collection. Edits consist primarily of two types: (1) consistency edits and (2) a ratio edit.

The consistency edits check the logical relationships of data items reported on the form. For example, if a value exists for employees for a function then a value must exist for payroll also. If part-time employees and payroll exist then part-time hours must exist and vice versa.

For each function reported for the employees, the ratio edits compare data for the number of employees and the average salary between reporting years. If data fall outside of acceptable tolerance levels, the item is flagged for review.

For ratio edits and consistency edits, the edit results are reviewed by analysts and adjusted as needed. When the analyst is unable to resolve or accept the edit failure, contact is made with the respondent to verify or correct the reported data.

Imputation: Not all respondents answer every item on the questionnaire. There are also questionnaires that are not returned despite efforts to gain a response. Imputation is the process of filling in missing or invalid data with reasonable values in order to have a complete data set for estimating state and national totals.

For nonresponding general purpose governments, dependent and independent school districts, and for special district governments, the imputations were based on recent historical data from either a prior year annual survey or the 2012 Census of Governments: Employment Component, if available. These data were adjusted by a growth rate that was determined by the growth of responding units that were similar (in size, geography, and type of government) to the nonrespondent. If there were no recent historical data available, the imputations were based on the data from a randomly selected responding donor that was similar (based on the same criteria) to the nonrespondent. For general purpose governments, and for dependent and independent school districts, the selected donor’s data were adjusted by dividing each data item by the population (or enrollment) of the donor and multiplying the result by the nonrespondent’s population (or enrollment).

Tabulation: After the 2013 Annual Survey of Public Employment & Payroll data were edited and imputed, they were aggregated to yield the viewable and downloadable files that are available on the Website and American FactFinder. For employment statistics, full-time employees, full-time pay, part-time employees, part-time pay, full-time equivalent employment, and total March pay are published.

Sampling Variability: The data that are provided come from a sample rather than a census of all possible units. The particular sample that was selected is one of a large number of possible samples of the same size and sample design that could have been selected. Each sample would have yielded different estimates. The estimated coefficients of variation, which are provided for each estimate, are an estimate of this sampling variability. In this tabulation, the coefficients of variation are expressed as percentages. The coefficient of variation (CV) is the ratio of the standard error to the expectation of the estimate. We used a Taylor series method to estimate the standard error.

State government employment and payroll data are not subject to sampling error. Consequently, state and local government aggregates for individual states are more reliable statistically than the local government only estimates.

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Source: U.S. Census Bureau | Government Employment & Payroll | |  Last Revised: December 19, 2014