The U. Labor Department's Bureau of Labor Statistics, or BLS to economic nerds, has used the same method for calculating the unemployment rate since Related: Rust belt voters got Trump elected. Now they want jobs. BLS has painstaking protocols so the figures are not tampered with. Its officials lock the rooms where they crunch the numbers. They so prize secrecy that they put their trash cans outside their sealed offices so office workers don't accidentally get a hint of the numbers before they're made public.
The monthly report is based on two surveys. One is the household survey, which produces the unemployment rate, and the establishment survey, which produces the number of jobs added or lost each month.
Related: U. Each month, the Census Bureau contacts 60, households selected to reflect geographic, industrial and agricultural diversity.
Households are put on a rotation, with some surveyed for 4 consecutive months, and others phased in and out each month. It asks respondents 11 questions. The answers to the questions determine whether a person has a job of if they are currently looking for a job. The data provided by CPS are measurements of the number of people. This distinction matters as individuals may hold more than one job. The CES data are the principal source of historical employment data for the projections program.
However, the CES data do not include the self-employed or workers in private households, and do not include most of the agricultural sector. The EP program uses the CPS person-based measure of employment in a manner that assumes one job per person. For the remainder of this section, references to workers should be understood as a reference to a single job.
Many assumptions underlie the BLS projections of the aggregate economy and of industry output, productivity, and employment. Often, these assumptions bear specifically on econometric factors, such as the aggregate unemployment rate, the anticipated time path of labor productivity, and expectations regarding the Federal budget surplus or deficit.
Other assumptions deal with factors that affect industry-specific measures of economic activity. BLS models industry employment as a function of industry output, wages, prices, and time. BLS projects industry employment using the estimated historical relationship between these variables. Industry employment is projected in both numbers of jobs and hours worked for wage and salary jobs as well as for self-employed workers.
A system of equations projecting employment for wage and salary jobs is solved independently over the projections decade for each industry. The individual industry estimates of employment must be consistent with the total level of employment derived from the solution of the macroeconomic model.
A separate set of equations, describing average weekly hours for each industry, is estimated as a function of time and the unemployment rate. The two sets of equations are then used to predict average weekly hours over the projections decade. An identity relating average weekly hours, total hours, and employment yields a count of wage and salary jobs by industry. The number of self-employed workers is derived by first extrapolating the ratio of the self-employed to the total employment for each industry.
The resulting extrapolation is a function of time and the unemployment rate. The extrapolated ratio is used to derive the number of self-employed workers, given the number of wage and salary jobs in each industry.
Total hours for self-employed workers are calculated by applying the estimated number of annual average weekly hours to the employment levels for each industry.
Finally, total hours for each industry are derived by summing hours for wage and salary jobs and hours for self-employed workers. Together with industry output projections, employment results provide a measure of labor productivity. BLS analysts examine the implied growth rates in the projected productivity numbers for consistency with historical trends. At the same time, analysts attempt to identify industries that may deviate from past behavior because of changes in technology or other factors.
Where appropriate, changes to the implied productivity are made by modifying the employment demand. The final estimates of projected employment for about industries are then used as inputs to determine the occupational employment over the projections decade.
This matrix describes the employment of detailed occupations within detailed wage and salary industries and different classes of workers, including those who are self-employed or employed by a private household.
These employment levels are provided for a base year and a projected year, which is 10 years in the future. The matrix does not include employment for every possible industry—occupation combination. Some data are not released to protect the confidentiality of the businesses or individuals providing the data and others are not released for quality reasons. All employment data in the National Employment Matrix are presented in thousands, rounded to one decimal place.
The detailed data in the matrix may not sum to summaries because of rounding or data that are not released. Wage and salary employment in industries covered by OEWS is by far the larger of the two groups. These jobs are grouped into industries and occupations that generally match those released in the OEWS data.
The data used in the matrix to describe the base year for these jobs come from three sources. Job counts by industry come from CES. In places where the matrix is more detailed than CES industry data, the Quarterly Census of Employment and Wages is used to develop weights to provide further detail.
Industry employment is split into individual occupations using OEWS data that describe what share of industry employment is held by which occupations industry staffing patterns. Self-employed workers, workers employed by private households, and agricultural workers excluding the logging industry account for a small share of total employment.
Because these workers are not captured by the OEWS establishment survey, the matrix uses data from CPS, which is a household survey that collects data directly from the workers. As a result of collecting data from workers, CPS data used by the matrix are a count of workers, not jobs, which is different from the measure used for CES wage and salary jobs.
Projected-year employment data for wage and salary jobs, including all agricultural workers, and workers employed by private households are developed using a conceptual framework that divides industry employment between occupations based on expected, structural changes in the demand for those occupations within a given industry.
To project these changes in occupational demand, BLS economists thoroughly review qualitative sources such as articles, expert interviews, and news stories, as well as quantitative resources such as historical data and externally produced projections. These reviews identify structural changes in the economy which are expected to change an occupation's share of industry employment. The sum of shares of industry employment for all the occupations in an industry must add up to percent for the occupational employment within an industry to match the overall industry's projected employment.
As a result, changes to one or more occupations' shares of industry can scale the shares of other occupations in that industry. To prevent unintended changes, the scaled shares of industry employment are reviewed extensively to ensure that changes in each industry are consistent with each other and that individual changes support the broader industry's narrative and projection.
Each occupation in the matrix is analyzed to identify factors that are likely to cause an increase or decrease in demand for that occupation within particular industries. This analysis incorporates judgments about new trends that may influence occupational demand, such as expanding use of new manufacturing techniques like 3D printing that might change the productivity of particular manufacturing occupations, or shifts in customer preferences between different building materials that may affect demand for specific construction occupations.
The results of this qualitative analysis form the quantitative basis for making changes to occupational shares of industry employment. The structural changes suggested by different trends are compared to determine if they will cause demand to grow or shrink, and if so, by how much. The effects of the projected trends are then combined into an overall numerical estimate which describes the change in an occupation's share of industry employment.
Projected-year data for self-employment are created using a modified version of the wage and salary employment method. Wage and salary employment is analyzed at an occupation-by-industry level but self-employment data at that same level result in estimates which are too small to analyze reliably.
Additional difficulties finding sufficient qualitative information about self-employment by occupation and industry make analysis at this level impractical even if robust data were available. To provide more usable estimates, self-employment data are initially projected at the occupation-by-industry level but aggregated to the occupational level for analysis.
That is, the details about individual occupations in specific industries for self-employed workers are combined to show the growth or decline of self-employed workers overall for each occupation. Although this broader view of self-employed workers does not provide the same detail as provided for wage and salary employment, the rate of growth or decline for self-employed occupations does incorporate the underlying industry detail, providing a reasonable estimate for analysis.
The methods used for wage and salary and self-employed worker projections are similar in their reliance on qualitative and quantitative research. Both examine available data and other resources.
Both make changes when the information available suggests a structural change is happening or is likely to occur before the projected year. Both incorporate changing industry demand for the occupation, but self-employment projections do this at a less detailed level than wage and salary. Projections of job growth provide valuable insight into future employment opportunities because each new job created is an opening for a worker entering an occupation.
However, opportunities also arise when workers separate from their occupations and need to be replaced. In most occupations, occupational separations provide many more openings than employment growth does. To project the magnitude of occupational openings, BLS calculates an estimate of the number of workers expected to exit the labor force, due to retirement or other factors, and an estimate of the number of workers expected to transfer to a different occupation.
The sum of labor force exits and occupational transfers represents the total projected separations for each occupation. This estimate does not count workers who change jobs but remain in the same occupation. To develop estimates of separations, BLS uses two separate calculations based on data from the Current Population Survey CPS , a household survey that collects demographic and employment information about individuals.
In the first calculation, data from the monthly CPS are used to estimate a regression model that measures the propensity of workers to exit the labor force. Both models are estimated using worker demographics and job specific information as independent variables. One study, in the American Journal of Preventive Medicine , found an association between increases in state minimum wages and slower growth in suicide rates. This relates to the labor force participation rate.
The rate stands now at The unemployment rate comes from some pretty simple math. The resulting fraction is converted to a percentage, and there you have the unemployment rate. BLS calculates the labor force participation rate by comparing the labor force to the civilian non-institutional population. The labor force participation rate has been declining since the Great Recession. Economists often chalk up declining labor participation rates to demographic changes.
Baby boomers are retiring and living longer , while the participation rate for men age 25 to 54 is declining, according to a September analysis from BLS economist Steven F.
Princeton economist Alan B. Kreuger comes to largely the same conclusions in a fall paper published in Brookings Papers on Economic Activity. Health problems may be playing a role. The Census of Agriculture from the U. Department of Agriculture offers more detail on farm labor. Alternative measures of unemployment are always higher than the official rate.
These measures tend to follow similar trends to the official unemployment rate, fluctuating up and down based on how the economy is doing. The broadest measure includes all people not in the labor force and underemployed people — those working part-time who want to work full-time.
This yields an unemployment rate roughly double the official figure. Again, none of these data include the 6. These data may still underrepresent the underemployed. The monthly unemployment rate and job numbers are certainly worth reporting as a barometer of economic health. High unemployment is bad on its face because it means a lot of people are not working who want to be. Low unemployment is usually good, but not always.
Inflation can rise if employers offer higher wages to compete in a tight labor market. If people are making more money, the theory is that goods producers respond by raising prices. The U. Federal Reserve in March pinned 4. What gives? Dylan Matthews at Vox goes into some depth on why the U. That means there may still be room for the unemployment rate to fall. Seasonal adjustment is another concept to understand. The headline numbers represent data that is seasonally adjusted, meaning BLS economists control for predictable seasonal fluctuations in employment.
For example, major holidays tend to spike employment as stores hire workers to keep up with increased demand. Any major fluctuations in the widely reported monthly data are not because of seasonal changes. BLS puts out a lot of data each month, but it can be hard to know where to start. Here are some quick links:. Historical household survey data. This is the place to find archival information from the household survey BLS uses to estimate the unemployment rate, along with 15 other data series.
Historical payroll survey data. And this is the place for archival information from the payroll survey BLS uses to estimate the number of jobs added or lost each month, along with 8 other data series.
0コメント