Technical Note This release presents labor force and unemployment data for census regions and divisions and states from the Local Area Unemployment Statistics (LAUS) program. The LAUS program is a federal-state cooperative endeavor. Concepts Definitions. The labor force and unemployment data are based on the same concepts and definitions as those used for the official national estimates obtained from the Current Population Survey (CPS), a sample survey of house- holds that is conducted for the Bureau of Labor Statistics (BLS) by the U.S. Census Bureau. The LAUS program measures employment and unemployment on a place-of-residence basis. The universe for each is the civilian noninstitu- tional population 16 years of age and over. Employed persons are those who did any work at all for pay or profit in the reference week (the week including the 12th of the month) or worked 15 hours or more without pay in a family business or farm, plus those not working who had a job from which they were temporarily absent, whether or not paid, for such reasons as labor-manage- ment dispute, illness, or vacation. Unemployed persons are those who were not employed during the reference week (based on the definition above), had actively looked for a job sometime in the 4-week period ending with the reference week, and were currently available for work; persons on layoff expecting recall need not be looking for work to be counted as unemployed. The labor force is the sum of employed and unemployed persons. The unemploy- ment rate is the number of unemployed expressed as a percent of the labor force. The employment-population ratio is the proportion of the civilian noninstitutional population 16 years of age and over that is employed. Method of estimation. Estimates for 48 of the 50 states, the District of Columbia, the Los Angeles-Long Beach-Glendale metropolitan division, New York City, and the balances of California and New York State are produced using estimating equations based on regression techniques. This method, which under- went substantial enhancement at the beginning of 2005, utilizes data from several sources, including the CPS, the Current Employment Statistics (CES) survey of nonfarm payroll employment, and state unemployment insurance (UI) programs. Estimates for the State of California are derived by summing the estimates for the Los Angeles-Long Beach-Glendale metropolitan division and the balance of California. Similarly, estimates for New York State are derived by summing the estimates for New York City and the balance of New York State. Estimates for all nine census divisions are based on a similar regression approach that does not incorporate CES or UI data. Estimates for census regions are obtained by summing the model-based estimates for the component divisions and then calculating the unemployment rate. Each month, census division estimates are controlled to national totals; state estimates are then controlled to their respective division totals. Estimates for Puerto Rico are derived from a monthly household survey similar to the CPS. A detailed description of the estimation procedures is available from BLS upon request. Annual revisions. Labor force and unemployment data for prior years reflect adjustments made at the end of each year. The adjusted estimates incorporate updated population data from the U.S. Census Bureau, any revisions in the other data sources, and model reestimation. The population data (except for Puerto Rico) reflect, for the first time, the results of the 2010 Census. In most years, historical data for the most recent 5 years (both seasonally adjusted and not seasonally adjusted) are revised near the beginning of each calendar year, prior to the release of January estimates. Though the labor force estimates are changed for 5 years, the population estimates are adjusted back to the new decennial estimates base of April 2010. Reliability of the estimates The estimates presented in this release are based on sample surveys, administrative data, and modeling and, thus, are subject to sampling and other types of errors. Sampling error is a measure of sampling variability-- that is, variation that occurs by chance because a sample rather than the entire population is surveyed. Survey data also are subject to nonsampling errors, such as those which can be introduced into the data collection and processing operations. Estimates not directly derived from sample surveys are subject to additional errors resulting from the specific estimation processes used. In table 1, level estimates for states may not sum to level estimates for regions and divisions because of rounding. Unemployment rates and employment-population ratios are computed from unrounded levels and thus may differ slightly from rates and ratios computed using the rounded level estimates displayed in table 1. Use of error measures. In 2005, the LAUS program introduced several improvements to its methodology. Among these was the development of model- based error measures for the monthly estimates and the estimates of over- the-month changes. Annual average model-based error measures became avail- able for the first time after 2006. The introductory section of this release preserves the long-time practice of highlighting the direction of the move- ments in regional and state unemployment rates and employment-population ratios regardless of their statistical significance. The remainder of the analysis in the release--other than historical highs and lows--takes statis- tical significance into consideration. Model-based error measures are avail- able online at www.bls.gov/lau/lastderr.htm. BLS uses 90-percent confidence levels in determining whether changes in LAUS unemployment rates or employment- population ratios are statistically significant. The average magnitude of the over-the-year change in an annual state unemployment rate that is required in order to be statistically significant at the 90-percent confidence level is about 0.5 percentage point. The average magnitude of the over-the-year change in an annual state employment-population ratio that is required in order to be statistically significant at the 90-percent confidence level is about 0.6 percentage point. Measures of nonsampling error are not available. Additional information Information in this release will be made available to sensory impaired individuals upon request. 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