SOURCE OF DATA
The data for this microdata file come from the December 1998 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.
December 1998 supplement. In addition to the basic CPS questions, interviewers asked supplementary questions on internet and computer use.
Sample Design. The present CPS sample was selected from the 1980 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. A redesigned CPS sample based on the 1990 census is currently being phased-in. The phase-in procedure started in April 1994 and will be completed in July 1995. In July 1995, there will be 818 geographic areas in sample.
Estimation procedure. This survey's estimation procedure inflates weighted sample results to independent estimates of the civilian noninstitutional population of the United States by 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 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:
For the December 1998 basic CPS, the nonresponse rate was 6.9% and for the suppplement the nonresponse rate was an additional 5.3% for a total supplement nonresponse rate of 11.8%.
CPS undercoverage results from missed housing units and missed persons within sample households. Compared to the level of the 1990 Decennial Census, overall CPS undercoverage is 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. The post-stratification ratio estimate described previously 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 age-sex-race-origin-state group.
A common measure of survey coverage is the coverage ratio, the estimated population before the post-stratification ratio estimate 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.
| Table A. CPS Coverage Ratios | |||||||
| Non-Black | Black | All Persons | |||||
| Age | M | F | M | F | M | F | Total |
| 0-14 | 0.929 | 0.964 | 0.850 | 0.838 | 0.916 | 0.943 | 0.929 |
| 15 | 0.933 | 0.895 | 0.763 | 0.824 | 0.905 | 0.883 | 0.895 |
| 16-19 | 0.881 | 0.891 | 0.711 | 0.802 | 0.855 | 0.877 | 0.866 |
| 20-29 | 0.847 | 0.897 | 0.660 | 0.811 | 0.823 | 0.884 | 0.854 |
| 30-39 | 0.904 | 0.931 | 0.680 | 0.845 | 0.877 | 0.920 | 0.899 |
| 40-49 | 0.928 | 0.966 | 0.816 | 0.911 | 0.917 | 0.959 | 0.938 |
| 50-59 | 0.953 | 0.974 | 0.896 | 0.927 | 0.948 | 0.969 | 0.959 |
| 60-64 | 0.961 | 0.941 | 0.954 | 0.953 | 0.960 | 0.942 | 0.950 |
| 65-69 | 0.919 | 0.972 | 0.982 | 0.984 | 0.924 | 0.973 | 0.951 |
| 70+ | 0.993 | 1.004 | 0.996 | 0.979 | 0.993 | 1.002 | 0.998 |
| 15+ | 0.914 | 0.945 | 0.767 | 0.874 | 0.898 | 0.927 | 0.918 |
| 0+ | 0.918 | 0.949 | 0.793 | 0.864 | 0.902 | 0.931 | 0.921 |
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 census-based population controls) with estimates for 1993 and earlier years (which reflect 1980 census-based 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 1-percent 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.
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 Tables B and C.
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 the number of men who were part-time workers with the number of women who were part-time workers.
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 90-percent 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, sx, of an estimated number from this microdata file can be obtained using this formula:
Here x is the size of the estimate and a and b are the parameters in Tables B or C associated with the particular type of characteristic. When calculating standard errors from cross-tabulations involving different characteristics, use the set of parameters for the characteristic which will give the largest standard error.
Illustration
Suppose there were 2,516,000 unemployed men in the civilian labor force. Use the appropriate parameters from Table B and formula (1) to get
| Number, x | 2,516,000 |
| a parameter | -0.000018 |
| b parameter | 2,957 |
| Standard error | 95,000 |
| 90% conf. int. | 2,945,000 to 3,257,000 |
The standard error is calculated as
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 or C indicated by the numerator.
The approximate standard error, sx.p, of an estimated percentage can be obtained by use of the formula
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 or C associated with the characteristic in the numerator of the percentage.
Illustration
Suppose that of approximately 104,209,000 households, 42.3 percent had a computer in the household. Use the appropriate parameter from Table C and formula (2) to get
| Percentage, p | 42.3 |
| Base, x | 104,209,000 |
| b parameter | 2,068 |
| Standard error | 0.22 |
| 90% conf. int. | 41.9 to 42.7 |
The standard error is calculated as
The 90-percent confidence interval of the percentage of households with computers is calculated as 42.3 ± 1.645×0.22
Standard error of a difference. The standard error of the difference between two sample estimates is approximately equal to
Illustration
Suppose there were 2,516,000 unemployed men 20 years of age or older and 2,333,000 unemployed women 20 years of age or older. Use the appropriate parameters from Table B and formulas (2) and (3) to get
| x | y | difference | |
| Number | 2,516,000 | 2,333,000 | 183,000 |
| a parameter | -.000018 | -.000018 | - |
| b parameter | 2,957 | 2,957 | - |
| Standard error | 85,600 | 82,500 | 118,900 |
| 90% conf. int. | 2,468,00 to
2,656,800 |
2,197,300 to
2,468,700 |
-12,600 to
379,800 |
The standard error of the difference is calculated as
Accuracy of state estimates. The redesign of the CPS following the 1980 census provided an opportunity to increase efficiency and accuracy of state data. All strata are now defined within state boundaries. The sample is allocated among the states to produce state and national estimates with the required accuracy while keeping total sample size to a minimum. Improved accuracy of state data has been achieved with about the same sample size as in the 1970 design.
Since the CPS is designed to produce both state and national estimates, the proportion of the total population sampled and the sampling rates differ among the states. In general, the smaller the population of the state the larger the sampling proportion. For example, in Vermont approximately 1 in every 400 households was sampled each month. In New York the sample was about 1 in every 2,000 households. Nevertheless, the size of the sample in New York is four times larger than in Vermont because New York has a larger population.
Computation of standard errors for state estimates . Standard errors for a state may be obtained by computing national standard errors, using formulas described earlier, and multiplying these by the appropriate f from Table D. An alternative method for computing standard errors for a state is to multiply the a and b parameters in Table B or C by f 2 and then use these adjusted parameters in the standard error formulas.
Illustration
Suppose there were 6,987,000 households in New York, 37.4 percent of which had a computer. Use the appropriate parameter from Table C and Formula (2) to get
Percentage, p 37.4
Base, x 6,987,000
b parameter 2,068
Standard error 0.83
Table D shows the f factor for New York to be 0.94. Thus, the standard error on the estimate of the percentage of persons 18 and older in New York state who had completed college is approximately 0.78 = 0.94×0.83.
CHECK
To obtain state parameters for New York, multiply the parameters in Table C by f 2 in Table D for the state of interest. For educational attainment for Total or White in New York this gives a = -.000011×0.89 = -0.000010 and b = 2,369×0.89 = 2,108.
Computation of a factor for groups of states. The factor adjusting standard errors for a group of states may be obtained by computing a weighted sum of the squared factors for the individual states in the group and taking the square root of the result. Depending on the combination of states, the resulting figure can be an overestimate.
The squared factor for a group of n states is given by
Illustration
Suppose a factor for the state group Illinois-Indiana-Michigan was required. The appropriate squared factor would be
|
Table B. Parameters for Computation of Standard Errors for Labor Force Characteristics - December 1998 |
||
| 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 |
|
|
| 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.
|
Table C. Parameters for Computation of Standard Errors for Internet and Computer Use Estimates December 1998 | ||||||
| Characteristic | Total or White | Black | Hispanic | |||
| a | b | a | b | a | b | |
| PERSONS | ||||||
| Educational Attainment | -0.000011 | 2,369 | -0.000109 | 2,680 | -.000148 | 3052 |
| Persons by Family Income | -0.000026 | 4,901 | -0.000260 | 5,611 | -.000556 | 9456 |
| Income | -0.000012 | 2,454 | -0.000120 | 2,810 | -.000249 | 4736 |
| Marital Status, Household & Family Characteristics | -0.000019 | 5,211 | -0.000221 | 7,486 | -.000443 | 12616 |
| Poverty | -0.000039 | 10,380 | -0.000307 | 10,380 | -.000617 | 17493 |
| FAMILIES, HOUSEHOLDS, OR UNRELATED INDIVIDUALS | ||||||
| Income | -0.000013 | 2,241 | -0.000119 | 2,447 | -.000354 | 4124 |
| Marital Status, Household & Family, Educational Attainment, Population by Age or Sex | -0.000012 | 2,068 | -0.000077 | 1,871 | -.000261 | 3153 |
| Poverty | 0.000102 | 2,442 | 0.000102 | 2,442 | .000172 | 4115 |
| Table D. Factors for State Standard Errors and Parameters and State Populations: 1998 | |||
| State | f |
f2 |
Population |
| Alabama | 1.00 |
1.01 |
3,307,000 |
| Alaska | 0.39 |
0.15 |
432,000 |
| Arizona | 0.98 | 0.96 | 3,468,000 |
| Arkansas | 0.77 | 0.59 | 1,921,000 |
| California | 1.13 | 1.27 | 23,969,000 |
| Colorado | 0.96 | 0.93 | 2,953,000 |
| Connecticut | 1.00 | 1.00 | 2,520,000 |
| Delaware | 0.47 | 0.22 | 565,000 |
| Dist. Of Col. | 0.41 | 0.16 | 423,000 |
| Florida | 0.99 | 0.97 | 11,304,000 |
| Georgia | 1.18 | 1.40 | 5,620,000 |
| Hawaii | 0.60 | 0.36 | 870,000 |
| Idaho | 0.51 | 0.26 | 895,000 |
| Illinois | 1.00 | 0.99 | 8,925,000 |
| Indiana | 1.17 | 1.37 | 4,467,000 |
| Iowa | 0.84 | 0.71 | 2,184,000 |
| Kansas | 0.80 | 0.64 | 1,922,000 |
| Kentucky | 0.96 | 0.92 | 3,009,000 |
| Louisiana | 0.97 | 0.94 | 3,231,000 |
| Maine | 0.60 | 0.36 | 974,000 |
| Maryland | 1.17 | 1.38 | 3,885,000 |
| Massachusetts | 0.90 | 0.81 | 4,742,000 |
| Michigan | 0.96 | 0.92 | 7,288,000 |
| Minnesota | 1.05 | 1.11 | 3,523,000 |
| Mississippi | 0.80 | 0.64 | 2,041,000 |
| Missouri | 1.17 | 1.37 | 4,060,000 |
| Montana | 0.44 | 0.20 | 679,000 |
| Nebraska | 0.65 | 0.42 | 1,241,000 |
| Nevada | 0.66 | 0.44 | 1,259,000 |
| New Hampshire | 0.62 | 0.38 | 897,000 |
| New Jersey | 0.90 | 0.82 | 6,150,000 |
| New Mexico | 0.63 | 0.40 | 1,285,000 |
| New York | 0.94 | 0.89 | 14,002,000 |
| North Carolina | 0.97 | 0.94 | 5,609,000 |
| North Dakota | 0.40 | 0.16 | 480,000 |
| Ohio | 1.01 | 1.02 | 8,548,000 |
| Oklahoma | 0.84 | 0.71 | 2,487,000 |
| Oregon | 0.93 | 0.86 | 2,518,000 |
| Pennsylvania | 0.98 | 0.95 | 9,288,000 |
| Rhode Island | 0.55 | 0.30 | 754,000 |
| South Carolina | 1.00 | 1.01 | 2,852,000 |
| South Dakota | 0.41 | 0.17 | 543,000 |
| Tennessee | 1.16 | 1.34 | 4,152,000 |
| Texas | 1.10 | 1.21 | 14,313,000 |
| Utah | 0.65 | 0.43 | 1,429,000 |
| Vermont | 0.42 | 0.18 | 456,000 |
| Virginia | 1.21 | 1.47 | 5,078,000 |
| Washington | 1.22 | 1.49 | 4,243,000 |
| West Virginia | 0.62 | 0.38 | 1,454,000 |
| Wisconsin | 1.09 | 1.19 | 3,934,000 |
| Wyoming | 0.34 | 0.12 | 366,000 |
NOTE: For foreign-born characteristics for Total and White, the a and b parameters should be multiplied by 1.3. No adjustment is necessary for foreign-born charactertistics for Blacks and Hispanics.
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