Source and Accuracy of the Data for the March 1998 Current Population Survey Microdata File

Table of Contents

SOURCE OF DATA

Basic CPS
March supplement
Sample design
Estimation procedure

ACCURACY OF THE ESTIMATES

Nonsampling variability
CPS Coverage Ratios
Comparability of data
Note when using small estimates
Sampling variability
Standard errors and their use
Standard errors of estimated numbers
Standard errors of estimated percentages
Parameters for Computation of Standard Errors for Labor Force Characteristics
Standard error of a difference
Standard error of a mean for grouped data
Standard error of a ratio
Standard error of a median
Accuracy of state estimates
Computation of standard errors for state estimates
Computation of a factor for groups of states
Computation of standard errors for data for combined years
a and b Parameters for Standard Error Estimates for Persons and Families
Factors for State Standard Errors and Parameters and State Populations

SOURCE OF DATA

The data for this survey came from the March 1998 Current Population Survey (CPS), conducted by the Bureau of the Census. The March survey uses two sets of questions, the basic CPS and the supplements.

Basic CPS. The monthly 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.

March supplement. In addition to the basic CPS questions, interviewers asked supplementary questions in March about money income received in the previous calendar year, educational attainment, household and family characteristics, marital status and geographical mobility.

To obtain more reliable data for the Hispanic population, the March CPS sample was increased by about 2,500 eligible housing units. These housing units were interviewed the previous November and contained at least one sample person of Hispanic origin. In addition, the sample included persons in the Armed Forces living off post or with their families on post.

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 adjusts weighted sample results to agree with independent estimates of the civilian noninstitutional population of the United States by age, sex, race, Hispanic/non-Hispanic origin, and state of residence. The adjusted estimate 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 estimation procedure for the March supplement included a further adjustment so husband and wife of a household received the same weight. 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 overall possible samples of the differences between the sample estimates and the desired value.)

Nonsampling variability. Several sources of nonsampling error include the following:

• Inability to get information about all sample cases.
• Definitional difficulties.
• Differences in 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).

For the March 1998 basic CPS, the nonresponse rate was 7.8% and for the suppplement the nonresponse rate was an additional 7.2% for a total supplement nonresponse rate of 14.4%.

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 the 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 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 March supplemental income 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 and later years with estimates from earlier years.

Caution should also be used when comparing data from this microdata file, which reflects 1990 census-based population controls, with microdata files from March 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.

Based on the results of each decennial census, the Bureau of the 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.

Note when using small estimates. Because of the large standard errors involved, summary measures probably do not reveal useful information when computed on a base smaller than 75,000.

Take care in the interpretation of small differences. 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 data. Instead of providing an individual standard error for each estimate, generalized sets of standard errors are provided for various types of characteristics. Thus, the tables show levels of magnitude of standard errors rather than the precise standard errors.

Table B shows parameters to use for basic CPS monthly labor force estimates. Table C shows parameters for March supplement data including the Hispanic supplement.

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 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 be used to perform hypothesis testing. This is a procedure for distinguishing between population parameters using sample estimates. The most common type of hypothesis is that the population parameters are different. An example of this would be comparing the percentage of Whites with a college education to the percentage of Blacks with a college education.

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. For example, to conclude that two parameters are different at the 0.10 level of significance, 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 texts 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 Employment and Earnings, a monthly report 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:

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

Illustration No. 1

Suppose there were 5,360,000 unemployed females in the civilian labor force. Use the appropriate parameters from Table B and Formula (1) to get

Number, x                          5,360,000
a parameter                        -0.000018
b parameter                            2,957
standard error                       124,000
90% conf. int.        5,156,000 to 5,564,000

The standard error is calculated as

the 90-percent confidence interval is calculated as 5,360,000 ± 1.645 x 124,000.

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.

Illustration No. 2

Suppose there are 8,419,000 high school graduates aged 20 to 24 years old. Use the appropriate parameters from Table C and Formula (1) to get

Number, x                        8,419,000
a parameter                      -0.000012
b parameter                          2,369
Standard error                     138,000
90% conf. int.      8,192,000 to 8,646,000

The standard error is calculated as

The 90-percent confidence interval is calculated as 8,419,000 ± 1.645´138,000.

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 for both numerator and denominator, depends on 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 factor or parameter from Table B or C indicated by the numerator.

Alternatively, Formula (2) will provide more accurate results:

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 No. 3

Suppose that of the 8,419,000 high school graduates aged 20 to 24, 12 percent were Black. Use the appropriate parameter from Table C and Formula (2) to get:

Percentage, p                 12.0
Base,                    8,419,000
b parameter                  2,680
Standard error                 0.6
90% conf. int.        11.0 to 13.0

Table B. Parameters for Computation of Standard Errors for Labor Force Characteristics: March 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	-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.

NOTE:These parameters are to be applied to basic CPS monthly labor force estimates.

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 characteristics for Blacks and Hispanics.

The standard error is calculated as

The 90-percent confidence interval for the percentage of high school graduates aged 20 to 24 who were Black is calculated as 12.0 ± 1.645´0.6.

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

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 No. 4

Suppose 8,419,000 persons 20 to 24 years old and 8,228,000 persons 25 to 29 years old had completed four years of high school and no more. Use the appropriate parameters from Table C and Formulas (2) and (3) to get

                       x                y          difference
Estimate             8,419,000        8,228,000         191,000
a parameter          -0.000012        -0.000012              -
b parameter              2,369            2,369               -
Standard error         138,000          137,000         194,000
90% conf. int.    8,192,000 to     8,003,000 to     -128,000 to
                     8,646,000        8,453,000         510,000

The standard error of the difference is calculated as

The 90-percent confidence interval around the difference is calculated as 191,000 + 1.645 ´ 194,000. Since this interval contains zero, we cannot conclude, at the 10-percent significance level, that the number of persons who completed four years of high school and no more is different for 20 to 24 year olds and 25 to 29 year olds.

Illustration No. 5

Suppose that of 6,285,000 employed males between 20-24 years of age, 1,516,000 or 24.1 percent were part-time workers, and of the 5,824,000 employed females 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                  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 female workers between 20-24 years of age is greater than the percentage of part-time male workers between 20-24 years of age.

Standard error of a mean for grouped data . The formula used to estimate the standard error of a mean for grouped data is

In this formula, y is the size of the base of the distribution and b is a parameter from Table B or C. The variance, S², is given by the following formula:

where , the mean of the distribution, is estimated by

c is the number of groups; i indicates a specific group, thus taking on values 1 through c.

p_i is the estimated proportion of households, families or persons whose values, for the characteristic (x-values) being considered, fall in group i.

is (Z _i-1 + Z _i)/2 where Z _i-1 and Z _i are the lower and upper interval boundaries, respectively, for group i. is assumed to be the most representative value for the characteristic for households, families, and unrelated individuals or persons in group i. Group c is open-ended, i.e., no upper interval boundary exists. For this group the approximate average value is

Standard error of a ratio. Certain estimates may be calculated as the ratio of two numbers. The standard error of a ratio, x/y, may be computed using

The standard error of the numerator, s_x , and that of the denominator, s _y , may be calculated using formulas described earlier. In Formula (8), r represents the correlation between the numerator and the denominator of the estimate.

For one type of ratio, the denominator is a count of families or households and the numerator is a count of persons in those families or households with a certain characteristic. If there is at least one person with the characteristic in every family or household, use 0.7 as an estimate of r. An example of this type is the mean number of children per family with children.

For all other types of ratios, r is assumed to be zero. If r is actually positive (negative), then this procedure will provide an overestimate (underestimate) of the standard error of the ratio. Examples of this type are the mean number of children per family and the poverty rate.

NOTE: For estimates expressed as the ratio of x per 100 y or x per 1,000 y, multiply Formula (10) by 100 or 1,000, respectively, to obtain the standard error.

Illustration No. 6

Suppose there are 641,000 male movers from abroad and 501,000 female movers from abroad. The ratio of male movers, x, to female movers, y, is 1.28. The standard error of this ratio is calculated as follows:

                         x                y              ratio
Estimate           641,000          501,000               1.28
a parameter      -0.000011        -0.000011                  -
b parameter          2,869            2,869                  -
Standard error      43,000           38,000               0.13
90% conf. int.  570,000 to       438,000 to       1.07 to 1.49
                   712,000          563,000

Using Formula (8) with r = 0, the estimate of the standard error is

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 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.

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.

Note: Median incomes and their standard errors calculated as below may differ from those in published tables showing income since narrower income intervals were used in those calculations.

Illustration No. 7

Suppose median income for families has the following distribution.

Total families                                 69,597
Under $5,000 ...................................1,890
$5,000 to $9,999 ...............................3,326
$10,000 to $14,999 .............................4,507
$15,000 to $24,999 ............................10,040
$25,000 to $34,999 .............................9,828
$35,000 to $49,999 ............................12,841
$50,000 to $74,999 ............................14,204
$75,000 to $99,999 .............................6,693
$100,000 and over. ............................ 6,268

Median income.................................$41,083

1.Using Formula (2) with b = 2,241, the standard error of 50 percent on a base of 69,597,000 is about 0.3 percent.

2.To obtain a 68-percent confidence interval on an estimated median, add to and subtract from 50 percent the standard error found in step 1. This yields percent limits of 49.7 and 50.3.

3.The lower and upper limits for the interval in which the median falls are $35,000 and $50,000, respectively.

Then, by addition, the estimated numbers of families with an income greater than or equal to $35,000 and $50,000 are 40,006,000 and 27,165,000, respectively.

Using Formula (9), the upper limit for the confidence interval of the median is found to be about

Similarly, the lower limit is found to be about

Thus, a 68-percent confidence interval for the median income for families is from $40,800 to $41,300.

4. The standard error of the median is, therefore,

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 No. 8

Suppose there were 11,200,000 persons 18 years old and over living in New York, 2,542,000 (22.7 percent) of whom had completed college. Use the appropriate parameter from Table C and Formula (2) to get

Percentage, p                  22.7
Base, x                  11,200,000
b parameter                   2,369
Standard error                  0.6

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 0.56 = 0.94´0.6.

To obtain state parameters for educational attainment in 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 = -.000012´0.89 = -0.000011 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

where POP_i is the state population and f _i² is obtained from Table D. The 1998 civilian noninstitutionalized population from the CPS for each state is also given in Table D.

Illustration No. 9

Suppose a factor for the state group Illinois-Indiana-Michigan was required. The appropriate squared factor would be

Multiply the a and b parameters by f ² , 1.05, to obtain parameters for the state group; multiply standard errors by f, 1.02, for standard errors for this state group.

Computation of standard errors for data for combined years. Sometimes estimates for multiple years are combined to improve precision. For example, suppose is a mean derived from n consecutive years' data, i.e.,where the x_i are the estimates for the individual years.

Use the formulas described previously to estimate the standard error,,of each year's estimate. Then the standard error of is

where

The correlation between consecutive years, r, is 0.35 for non-Hispanic households and 0.55 for Hispanic households. Correlation between nonconsecutive years is zero. The correlations were derived for income estimates but they can be used for other types of estimates where the year-to-year correlation between identical households is high.

Illustration No. 10

Suppose a mean for 1993-1995 for children without health insurance is 7,147,000 and the standard errors for the individual years are 213,000, 217,000, and 216,000.

Using Formula (12), the standard error for the three years combined data is

Therefore, the standard error of the mean, using Formula (11), is

Table C. a and b Parameters for Standard Error Estimates for Persons and Families: March 1998
Characteristics	Total or White		Black		Hispanic
	a	b	a	b	a	b
PERSONS
Educational Attainment	-0.000012	2,369	-0.000139	2,680	-0.000088	1,811
Employment Characteristics	-0.000018	2,985	-0.000125	3,139	-0.000151	3,139
Persons by Family Income	-0.000026	4,901	-0.000260	5,611	-0.000330	5,611
Income	-0.000012	2,454	-0.000116	2,810	-0.000135	2,810
Marital Status, Household and Family Characteristics, Health Insurance
- Some household members	-0.000019	5,211	-0.000217	7,486	-0.000244	7,486
- All household members	-0.000024	6,332	-0.000320	11,039	-0.000359	11,039
Mobility Characteristics (Movers)
- Educational Attainment, Labor Force, Marital Status, Household, Family, and Income	-0.000011	2,869	-0.000083	2,869	-0.000093	2,869
US, County, State, Region or MSA	-0.000029	7,791	-0.000226	7,791	-0.000254	7,791
Poverty	-0.000039	10,380	-0.000301	10,380	-0.000338	10,380
Unemployment	-0.000018	2,957	-0.000212	3,150	-0.000102	3,150
FAMILIES, HOUSEHOLDS, OR UNRELATED INDIVIDUALS
Income	-0.000013	2,241	-0.000119	2,447	-0.000210	2,447
Marital Status, Household and Family Characteristics, Educational Attainment, Population by Age and/or Sex	-0.000012	2,068	-0.000077	1,871	-0.000155	1,871
Poverty	0.000102	2,442	0.000102	2,442	0.000102	2,442

NOTES:These parameters are to be applied to March supplemental data including the Hispanic supplement.

Multiply a and b parameters by 1.5 when tabulating nonmetropolitan. If the characteristic of interest is total state population, not subtotaled by race or ethnic origin, the a and b parameters are zero.

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 characteristics for Blacks and Hispanics.

Table D. Factors for State Standard Errors and Parameters and State Populations: 1998
State	f	f 2	Population
Alabama	1.00	1.01	3,365,000
Alaska	0.39	0.15	429,000
Arizona	0.98	0.96	3,450,000
Arkansas	0.77	0.59	1,928,000
California	1.13	1.27	24,374,000
Colorado	0.96	0.93	3,007,000
Connecticut	1.00	1.00	2,523,000
Delaware	0.47	0.22	570,000
Dist. Of Col.	0.41	0.16	410,000
Florida	0.99	0.97	11,530,000
Georgia	1.18	1.40	5,722,000
Hawaii	0.60	0.36	880,000
Idaho	0.51	0.26	911,000
Illinois	1.00	0.99	8,968,000
Indiana	1.17	1.37	4,490,000
Iowa	0.84	0.71	2,172,000
Kansas	0.80	0.64	1,949,000
Kentucky	0.96	0.92	3,040,000
Louisiana	0.97	0.94	3,282,000
Maine	0.60	0.36	975,000
Maryland	1.17	1.38	3,938,000
Massachusetts	0.90	0.81	4,741,000
Michigan	0.96	0.92	7,501,000
Minnesota	1.05	1.11	3,545,000
Mississippi	0.80	0.64	2,059,000
Missouri	1.17	1.37	4,093,000
Montana	0.44	0.20	678,000
Nebraska	0.65	0.42	1,241,000
Nevada	0.66	0.44	1,309,000
New Hampshire	0.62	0.38	906,000
New Jersey	0.90	0.82	6,219,000
New Mexico	0.63	0.40	1,305,000
New York	0.94	0.89	13,938,000
North Carolina	0.97	0.94	5,658,000
North Dakota	0.40	0.16	480,000
Ohio	1.01	1.02	8,563,000
Oklahoma	0.84	0.71	2,508,000
Oregon	0.93	0.86	2,554,000
Pennsylvania	0.98	0.95	9,274,000
Rhode Island	0.55	0.30	753,000
South Carolina	1.00	1.01	2,899,000
South Dakota	0.41	0.17	553,000
Tennessee	1.16	1.34	4,200,000
Texas	1.10	1.21	14,526,000
Utah	0.65	0.43	1,476,000
Vermont	0.42	0.18	457,000
Virginia	1.21	1.47	5,141,000
Washington	1.22	1.49	4,311,000
West Virginia	0.62	0.38	1,452,000
Wisconsin	1.09	1.19	3,933,000
Wyoming	0.34	0.12	364,000

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.

Annual Demographic Survey (March CPS Supplement) 1998 Data Quality Page

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