This dataset on average weekly hours worked by private employees in the St. Louis metropolitan area was made by Trading Economics, but the original data comes from government sources like the Federal Reserve’s FRED database and the U.S. Bureau of Labor Statistics. These organizations collect employment data through surveys and reports from businesses. The purpose of compiling this data is to help researchers, businesses, and policymakers understand labor trends and overall economic conditions. It has been used for economic research, forecasting, and news reporting. The dataset is available online in graph form and can also be downloaded as a spreadsheet.
The data itself is structured in a simple way, mainly focusing on the date and the average number of hours worked each week. This makes it useful for looking at trends over time, such as whether people are working more or fewer hours during certain months, years, and periods of time. However, because the dataset only focuses on averages, it does not show differences between industries, job types, or worker demographics. This could affect how the data is interpreted since part-time and full-time workers are included in the same average.
The creators mention that the data has been standardized and sometimes seasonally adjusted to make comparisons easier. While this helps make the data cleaner and more consistent, it may also hide unusual spikes or drops that could be important for understanding real economic changes. The goals of the organizations involved also shape the dataset. Government agencies focus on measurable indicators like hours worked because they are important for tracking employment and productivity. Trading Economics then decides how to present the data, which can influence what users focus on.
I would use this dataset to study economic trends in the St. Louis region, especially how working hours change during recessions or periods of growth. I would also look at why fewer hours are worked in certain months of the year vs others. Overall, the dataset is useful, but it should be used carefully because it simplifies complex labor patterns into one average number. It also doesn’t account for certain factors, like part-time vs full-time employees.