Related Experiment Video
Updated: May 5, 2026
08:36
Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
13.3K
Mismeasurement of Usual Hours Worked In the Census and ACS
Nathaniel Baum-Snow1, Derek Neal
1Brown University & NBER.
Economics Letters
|November 22, 2013
Summary
Wage data from censuses and the American Community Survey (ACS) are useful but contain errors. These errors lead to unrealistic wage calculations for part-time workers, impacting labor market analysis.
Area of Science:
- Labor economics
- Demographic analysis
- Statistical methodology
Background:
- Decennial censuses and the American Community Survey (ACS) provide crucial data for economic research.
- Accurate wage rate calculation is essential for understanding labor market dynamics.
- Previous studies have relied on census and ACS data for wage estimations.
Purpose of the Study:
- To evaluate the accuracy of wage rate calculations derived from census and ACS data.
- To identify and analyze errors in reporting usual hours worked.
- To assess the impact of these errors on implied wage rates for part-time workers.
Main Methods:
- Analysis of data from recent decennial censuses and the American Community Survey (ACS).
- Examination of reported usual hours worked during the past year.
- Calculation of implied average hourly wage rates.
Main Results:
- Data on usual hours worked contain reporting errors.
- These errors result in implausible wage figures for individuals working part-time.
- The inaccuracies affect the reliability of wage rate estimations.
Conclusions:
- The current methodology for calculating wage rates using census and ACS data is flawed.
- Reporting errors in usual hours worked significantly distort part-time wage calculations.
- Methodological improvements are needed to ensure accurate labor market data.
Related Concept Videos
01:20Random and Systematic Errors
11.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.2K
01:19Errors and Mistakes in Surveying
1.1K
Errors and mistakes in surveying refer to inaccuracies in measurements and data recording. The errors are deviations from the actual value caused by human sensory limitations, equipment flaws, or environmental effects. These errors are typically unintentional and can result from the inherent imperfections in the instruments used, atmospheric conditions, or the observer’s inability to perceive exact measurements. On the other hand, mistakes are caused by the surveyor's lack of...
1.1K
01:15Systematic Error: Methodological and Sampling Errors
8.7K
In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
8.7K
03:37Uncertainty in Measurement: Accuracy and Precision
94.0K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
94.0K
01:07Midrange
2.8K
A somewhat easy to compute quantitative estimate of a data set’s central tendency is its midrange, which is defined as the mean of the minimum and maximum values of an ordered data set.
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
Simply put, the midrange is half of the data set’s range. Similar to the mean, the midrange is sensitive to the extreme values and hence the prospective outliers. However, unlike the mean, the midrange is not sensitive to all the values of the data set that lie in the middle. Thus, it is prone to...
2.8K
02:16Measures of Central Tendency
19.3K
The "center" of a data set is also a way of describing location. The two most widely used measures of the "center" of the data are the mean (average) and the median. The words "mean" and "average" are often used interchangeably. The substitution of one word for the other is common practice. The technical term is "arithmetic mean" and "average" is technically a center location. However, in practice among non-statisticians,...
19.3K