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Labor Supply - Technical Notes
Labor supply can be defined as the number of persons who would potentially apply for work if a job becomes available. There are basically two ways to estimate labor supply. One way to estimate labor supply is a survey of a geographical area. Many current labor surveys use the "labor shed" concept, which includes commuting from other communities in the estimate. There are pros and cons to conducting and using the results of a labor survey. On the positive side, a labor survey can provide detailed information about the labor supply, including the wages and types of jobs of interest. Sometimes a prospective business may demand a labor survey before they locate to an area, and labor surveys are easier to understand conceptually. On the negative side, a statistically-valid survey is usually quite expensive. Depending upon changes in the economy, the labor survey results could be valid for only one month or up to a year. Thirdly, labor surveys are conducted by different vendors or groups using different methodologies, and the results are not easily compared to other surveys or over time, and cannot be summed to provide a statewide perspective to labor supply.
Another method that can be used to estimate labor supply is a "handbook" approach. The LMIC uses this approach to develop labor supply estimates for counties and Metropolitan Statistical Areas. The "handbook" approach attempts to measure labor supply for nonfarm wage and salaried jobs using data that already exists. One of the advantages of the "handbook" approach is that it is a cost-effective way of making labor supply estimates for areas that exhaust the state's geography.
The "unemployed" component of labor supply estimates using the handbook method
People without jobs make up a large share of the labor supply. The "handbook" approach includes a procedure to estimate the number of people who are not working and would be available for new jobs. The starting point for this part of the methodology is data on those who meet the official definition of "unemployed." The estimated level of unemployed residents is available through the Local Area Unemployment Statistics (LAUS) program administered by the U.S. Bureau of Labor Statistics. These estimates are published as the "unemployment" level for each geographic area within South Dakota each month on this website.
The "employed but willing to change jobs" component of labor supply estimates using the handbook method
Estimates for the number of workers with jobs who may be willing to change jobs can be made based on historical hiring trends. Since the goal is to determine the labor supply for nonfarm wage and salaried jobs, the starting point is the nonfarm worker numbers which LMIC estimates by county each month. Using that county data, the "handbook" approach determines the relative share of nonfarm workers who may be willing to change jobs. This is where historical hiring patterns come into play. New hire rates for wage and salaried workers covered by unemployment insurance produced by the Local Employment Dynamics (LED) program, administered by the U.S. Census Bureau, are utilized for this purpose.
New hire rates indicate the percentage of all covered workers who show up on an employer's quarterly payroll as a new hire. In other words, they have not been employed by that business in previous quarters. So new hire rates for the reference quarter in each of the most recent four years for which data is available are used to calculate current quarterly new hire rates. For example, for the months of January, February and March 2017, average quarterly new hire rates for the first quarter reference period for the years 2013-2016 were used to estimate current quarterly new hire rates – as an indicator of the relative share of 'nonfarm workers who may be willing to change jobs.' The new hire methodology incorporates seasonal and cyclical employment patterns within the state workforce throughout the year.
The final step in estimating the "employed but willing to change jobs" is to adjust for multiple job holders. Since nonfarm wage and salaried worker estimates are counts of workers at jobs, a single individual can be counted more than once.
After the adjustment for multiple jobs, the 'labor supply employed but willing to change jobs' represents a count of individuals who are working and willing to change jobs. Here is an example of the calculation of the 'employed but willing to change jobs' for Beadle County.
|x||New Hire Rate||x||Adjusted for
Employed But Willing to Change Jobs
The "discouraged workers" component of labor supply estimates using the handbook method
Also included is an estimate of those who are currently not active in the labor force, but want a job, have searched for work in the past year and are currently available to work. This is an estimate of persons who are not currently employed but do not meet the official definition of "unemployed" but are available to work – sometimes referred to as discouraged workers*.
Adding them all together
In the final step of estimating labor supply, the 'unemployed,' the 'employed but willing to change jobs' and the 'discouraged worker' estimates are added together. Here is an example of the final calculation of labor supply for Beadle County.
Employed But Willing to Change Jobs
|Discouraged Workers||=||Labor Supply
*Discouraged workers are a subset of persons marginally attached to the labor force. The marginally attached are those persons not in the labor force who want and are available for work, and who have looked for a job sometime in the prior 12 months, but were not counted as unemployed because they had not searched for work in the four weeks preceding the survey. Among the marginally attached, discouraged workers were not currently looking for work specifically because they believed no jobs were available for them or there were none for which they would qualify. The statewide discouraged worker estimate is derived from unpublished data gathered through the Current Population Survey (CPS) conducted by the U.S. Census Bureau. These estimates, particularly labor force data for smaller states, are subject to rather large sampling error.