Source and Accuracy Statement for the February 1996 CPS MicrodataFile for Displaced Workers, Job Tenure, and Occupational Mobility

SOURCE OF DATA

The data for this microdata file come from the February 1996 Current Population Survey (CPS). This month's survey uses two sets of questions, the basic CPS and the supplement. The Bureau of the Census conducts the basic CPS every month and asks supplementary questions during certain months.

Basic CPS. The basic CPS collects primarily labor force data about the civilian noninstitutional population. Interviewers ask questions concerning labor force participation about each member 15 years old and over in every sample household.

February 1996 supplement. In addition to the basic CPS questions, interviewers asked supplementary questions on displaced workers, job tenure, and occupational mobility.

Sample Design. The present CPS sample was selected from the 1990 Decennial Census files with coverage in all 50 states and the District of Columbia. The sample is continually updated to account for new residential construction. The United States was divided into 2,007 geographic areas. In most states, a geographic area consisted of a county or several contiguous counties. In some areas of New England and Hawaii, minor civil divisions are used instead of counties. A total of 754 geographic areas were selected for sample. About 50,000 occupied households are eligible for interview every month. Interviewers are unable to obtain interviews at about 3,200 of these units. This occurs when the occupants are not found at home after repeated calls or are unavailable for some other reason.

Since the introduction of the CPS, the Bureau of the Census has redesigned the CPS sample several times. These redesigns have improved the quality and accuracy of the data and have satisfied changing data needs. The most recent changes were completely implemented in July 1995.

Estimation procedure. This survey's estimation procedure inflates weighted sample results to independent estimates of the civilian noninstitutional population of the United States by state, age, sex, race, Hispanic/non-Hispanic origin, and state of residence. This adjustment is called the post-stratification ratio estimate. The independent estimates are calculated based on information from four primary sources:

· The 1990 Decennial Census of Population and Housing.

· An adjustment for undercoverage in the 1990 census.

· Statistics on births, deaths, immigration, and emigration.

· Statistics on the size of the Armed Forces.

The independent population estimates include some, but not all, undocumented immigrants.

ACCURACY OF THE ESTIMATES

Since the CPS estimates come from a sample, they may differ from figures from a complete census using the same questionnaires, instructions, and enumerators. A sample survey estimate has two possible types of error: sampling and nonsampling. The accuracy of an estimate depends on both types of error, but the full extent of the nonsampling error is unknown. Consequently, one should be particularly careful when interpreting results based on a relatively small number of cases or on small differences between estimates. The standard errors for CPS estimates primarily indicate the magnitude of sampling error. They also partially measure the effect of some nonsampling errors in responses and enumeration, but do not measure systematic biases in the data. (Bias is the average over all possible samples of the differences between the sample estimates and the desired value.)

Nonsampling variability. There are several sources of nonsampling errors including the following:

· Inability to obtain information about all cases in the sample.

· Definitional difficulties.

· Differences in the interpretation of questions.

· Respondents' inability or unwillingness to provide correct information.

· Respondents' inability to recall information.

· Errors made in data collection such as recording and coding data.

· Errors made in processing the data.

· Errors made in estimating values for missing data.

· Failure to represent all units with the sample (undercoverage).

CPS undercoverage results from missed housing units and missed persons within sample households. Overall CPS undercoverage is estimated to be about 8 percent. CPS undercoverage varies with age, sex, and race. Generally, undercoverage is larger for males than for females and larger for Blacks and other races combined than for Whites. As described previously, ratio estimation to independent age-sex-race-Hispanic population controls partially corrects for bias due to undercoverage. However, biases exist in the estimates to the extent that missed persons in missed households or missed persons in interviewed households have different characteristics from those of interviewed persons in the same age­sex­race­origin-state group.

A common measure of survey coverage is the coverage ratio, the estimated population before post-stratification divided by the independent population control. Table A shows CPS coverage ratios for age-sex-race groups for a typical month. The CPS coverage ratios can exhibit some variability from month to month. Other Census Bureau household surveys experience similar coverage.

For additional information on nonsampling error including the possible impact on CPS data when known, refer to Statistical Policy Working Paper 3, An Error Profile: Employment as Measured by the Current Population Survey, Office of Federal Statistical Policy and Standards, U.S. Department of Commerce, 1978 and Technical Paper 40, The Current Population Survey: Design and Methodology, Bureau of the Census, U.S. Department of Commerce.

Comparability of data. Data obtained from the CPS and other sources are not entirely comparable. This results from differences in interviewer training and experience and in differing survey processes. This is an example of nonsampling variability not reflected in the standard errors. Use caution when comparing results from different sources.

A number of changes were made in data collection and estimation procedures beginning with the January 1994 CPS. The major change was the use of a new questionnaire. The questionnaire was redesigned to measure the official labor force concepts more precisely, to expand the amount of data available, to implement several definitional changes, and to adapt to a computer-assisted interviewing environment. The supplemental questions were also modified for adaptation to computer-assisted interviewing, although there were no changes in definitions and concepts. Due to these and other changes, one should use caution when comparing estimates from data collected in 1994 with estimates from earlier years.

Caution should also be used when comparing estimates obtained from this microdata file (which reflects 1990 census­based population controls) with estimates for 1993 and earlier years (which reflect 1980 census­based population controls). This change in population controls had relatively little impact on summary measures such as means, medians, and percentage distributions. It did have a significant impact on levels. For example, use of 1990 based population controls results in about a 1­percent increase in the civilian noninstitutional population and in the number of families and households. Thus, estimates of levels for data collected in 1994 and later years will differ from those for earlier years by more than what could be attributed to actual changes in the population. These differences could be disproportionately greater for certain subpopulation groups than for the total population.

Since no independent population control totals for persons of Hispanic origin were used before 1985, compare Hispanic estimates over time cautiously.

Based on the results of each decennial census, the Bureau of he Census gradually introduces a new sample design for the CPS. During this phase-in period, CPS data are collected from sample designs based on different censuses. While most CPS estimates have been unaffected by this mixed sample, geographic estimates are subject to greater error and variability. Users should exercise caution when comparing estimates across years for metropolitan/ nonmetropolitan categories.

For more information on the introduction of the new questionnaire, the modernized data collection methods, and the introduction of new population controls based on the 1990 census, see "Revisions in the Current Population Survey Effective January 1994" in the February 1994 issue of Employment and Earnings published by the Bureau of Labor Statistics.

Note when using small estimates. Because of the large standard errors involved, summary measures (such as medians and percent distributions) probably do not reveal useful information when computed on a base smaller than 75,000. Take care in the interpretation of small differences. For instance, even a small amount of nonsampling error can cause a borderline difference to appear significant or not, thus distorting a seemingly valid hypothesis test.

Sampling variability. Sampling variability is variation that occurred by chance because a sample was surveyed rather than the entire population. Standard errors, as calculated below, are primarily measures of sampling variability, but they may include some nonsampling error.

Standard errors and their use. A number of approximations are required to derive, at a moderate cost, standard errors applicable to estimates from this microdata file. Instead of providing an individual standard error for each estimate, two parameters, a and b, are provided to calculate standard errors for each type of characteristic. These parameters are in Table B.

The sample estimate and its standard error enable one to construct a confidence interval. A confidence interval is a range that would include the average result of all possible samples with a known probability. For example, if all possible samples were surveyed under essentially the same general conditions and using the same sample design, and if an estimate and its standard error were calculated from each sample, then approximately 90 percent of the intervals from 1.645 standard errors below the estimate to 1.645 standard errors above the estimate would include the average result of all possible samples.

A particular confidence interval may or may not contain the average estimate derived from all possible samples. However, one can say with specified confidence that the interval includes the average estimate calculated from all possible samples.

Standard errors may also be used to perform hypothesis testing. This is a procedure for distinguishing between population parameters using sample estimates. One common type of hypothesis is that two population parameters are different. An example of this would be comparing males who left a job involuntarily to females who left a job involuntarily.

Tests may be performed at various levels of significance. A significance level is the probability of concluding that the characteristics are different when, in fact, they are the same. To conclude that two parameters are different at the 0.10 level of significance, for example, the absolute value of the estimated difference between characteristics must be greater than or equal to 1.645 times the standard error of the difference.

The Census Bureau uses 90­percent confidence intervals and 0.10 levels of significance to determine statistical validity. Consult standard statistical textbooks for alternative criteria.

For information on calculating standard errors for labor force data from the CPS which involve quarterly or yearly averages, changes in consecutive quarterly or yearly averages, consecutive month-to-month changes in estimates, and consecutive year-to-year changes in monthly estimates; see "Explanatory Notes and Estimates of Error: Household Data" in the corresponding Employment and Earnings published by the Bureau of Labor Statistics.

Standard errors of estimated numbers. The approximate standard error, s_x, of an estimated number from this microdata file can be obtained using this formula:

Formula (1)

Here x is the size of the estimate and a and b are the parameters in Table B associated with the particular type of characteristic. When calculating standard errors from cross­tabulations involving different characteristics, use the set of parameters for the characteristic which will give the largest standard error.

Illustration

Suppose there were 6,000,000 unemployed men in the civilian labor force. Use the appropriate parameters from Table B and formula (1) to get

Number, x	6,000,000
a parameter	-0.000018
b parameter	2,957
Standard error	131,000
90% conf. int.	5,785,000 to 6,215,000

The standard error is calculated as

The 90- percent confidence interval is calculated as 6,000,000 ± 1.645´131,000.

A conclusion that the average estimate derived from all possible samples lies within a range computed in this way would be correct for roughly 90 percent of all possible samples.

Standard errors of estimated percentages. The reliability of an estimated percentage, computed using sample data from both numerator and denominator, depends on both the size of the percentage and its base. Estimated percentages are relatively more reliable than the corresponding estimates of the numerators of the percentages, particularly if the percentages are 50 percent or more. When the numerator and denominator of the percentage are in different categories, use the parameter from Table B indicated by the numerator.

The approximate standard error, s_x,p, of an estimated percentage can be obtained by use of the formula

Formula (2)

Here x is the total number of persons, families, households, or unrelated individuals in the base of the percentage, p is the percentage (0 £ p £ 100), and b is the parameter in Table B associated with the characteristic in the numerator of the percentage.

Illustration

Suppose of 13,894,000 displaced workers, 4,918,000 or 35.4 percent lost their jobs when a plant or company closed down or moved. Use the appropriate parameter from Table B and formula (2) to get

Percentage, p	35.4
Base, x	13,894,000
b parameter	2,985
Standard error	0.7
90% conf. int.	34.2 to 36.6

The standard error is calculated as

The 90 percent confidence interval of the percentage of displaced workers who lost their jobs when a plant or company closed down or moved is calculated as 35.4 ± 1.645´0.7.

Standard error of a difference. The standard error of the difference between two sample estimates is approximately equal to

Formula (3)

where s_x and s_y are the standard errors of the estimates, x and y. The estimates can be numbers, percentages, ratios, etc. This will represent the actual standard error quite accurately for the difference between estimates of the same characteristic in two different areas, or for the difference between separate and uncorrelated characteristics in the same area. However, if there is a high positive (negative) correlation between the two characteristics, the formula will overestimate (underestimate) the true standard error.

Illustration

Suppose that of the 6,285,000 employed men between 20-24 years of age, 1,516,000 or 24.1 percent were part-time workers, and of the 5,824,000 employed women between 20-24 years of age, 2,169,000 or 37.2 percent were part-time workers. Use the appropriate parameters from Table B and formulas (2) and (3) to get

	x	y	difference
Percentage, p	24.1	37.2	13.1
Number, x	6,285,000	5,824,000	-
b parameter	2,764	2,530	-
Standard error	0.9	1.0	1.3
90% conf. int.	22.6 to 25.6	35.6 to 38.8	11.0 to 15.2

The standard error of the difference is calculated as

The 90-percent confidence interval around the difference is calculated as 13.1 ± 1.645´1.3. Since this interval does not include zero, we can conclude with 90-percent confidence that the percentage of part-time women workers between 20-24 years of age is greater than the percentage of part-time men workers between 20-24 years of age.

Standard Error of a Median. The sampling variability of an estimated median depends on the form of the distribution and the size of the base. One can approximate the reliability of an estimated median by determining a confidence interval about it. (See the section on standard errors and their use for a general discussion of confidence intervals.)

Estimate the 68-percent confidence limits of a median based on sample data using the following procedure.

1. Determine, using formula (2), the standard error of the estimate of 50 percent from the distribution.

2. Add to and subtract from 50 percent the standard error determined in step 1.

3. Using the distribution of the characteristic, determine upper and lower limits of the 68-percent confidence interval by calculating values corresponding to the two points established in step 2.

Use the following formula to calculate the upper and lower limits.

Formula (4)

where

X_pN = estimated upper and lower bounds for the confidence interval (0 £ p £ 1). For purposes of calculating the confidence interval, p takes on the values determined in step 2. Note that X_pN estimates the median when p = 0.50.

N = for distribution of numbers: the total number of units (persons, households, etc.) for the characteristic in the distribution.

= for distribution of percentages: the value 1.0.

p = the values obtained in step 2.

A₁, A₂ = the lower and upper bounds, respectively, of the interval containing X_pN.

N₁, N₂ = for distribution of numbers: the estimated number of units (persons, households, etc.) with values of the characteristic greater than or equal to A₁ and A₂, respectively.

= for distribution of percentages: the estimated percentage of units (persons, households, etc.) having values of the characteristic greater than or equal to A₁ and A₂, respectively.

4. Divide the difference between the two points determined in step 3 by two to obtain the standard error of the median.

Illustration

Suppose that the estimated median years on the lost job for all displaced workers is four years and suppose the following table gives the distribution of years.

Years on Last Job	Number of Persons (in thousands)	Percent Distribution	Cumulative Distribution
< 1	2,779	20.0	20.0
1 - 4	5,558	40.0	60.0
5 - 9	3,196	23.0	83.0
10 - 14	1,389	10.0	93.0
15 - 19	834	6.0	99.0
20+	138	1.0	100.0
Total	13,894

(1) Using b = 2,985 from Table B and formula (2), the standard error of 50 percent on a base of 13,894,000 is around 0.7 percent.

(2) To obtain a 68-percent confidence interval for a median, add to and subtract from 50 percent, the standard error found in step (1). This yields limits of 49.3 and 50.7 percent.

(3) It can be seen that 20.0 percent of the displaced workers had less than 1 year on their lost job, and 60.0 percent had less than 5 years on their lost job. By linear interpolation the lower and upper limits of the 68-percent confidence interval for the median are calculated as

(4) The standard error of the median is, therefore,

Table B. Parameters for Computation of Standard Errors for Labor Force Characteristics - 1996
Characteristic	a	b
Labor Force and Not In Labor Force Data Other than Agricultural Employment and Unemployment
Total 1	-0.000018	2,985
- Men 1	-0.000033	2,764
- Women	-0.000030	2,530
- Both sexes, 16 to 19 years	-0.000172	2,545
White 1	-0.000020	2,985
- Men	-0.000037	2,767
- Women	-0.000034	2,527
- Both sexes, 16 to 19 years	-0.000204	2,550
Black	-0.000125	3,139
- Men	-0.000302	2,931
- Women	-0.000183	2,637
- Both sexes, 16 to 19 years	-0.001295	2,949
Hispanic origin	-0.000206	3,896
Not In Labor Force (use only for Total, Total Men, and White)	+0.000006	829
Agricultural Employment
Total or White	+0.000782	3,049
- Men	+0.000858	2,825
- Women or Both sexes, 16 to 19 years	-0.000025	2,582
Black	-0.000135	3,155
Hispanic origin
- Total or Women	+0.011857	2,895
- Men or Both sexes, 16 to 19 years	+0.015736	1,703
Unemployment
Total or White	-0.000018	2,957
Black	-0.000212	3,150
Hispanic origin	-0.000102	3,576

¹ For not in labor force characteristics, use the Not In Labor Force parameters.

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