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Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond (2000)

Chapter:4 Future Model Develpment: The Role of Surveys

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Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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4

Future Model Development: The Role of Surveys

USER OVERVIEW

Evaluation studies of the Census Bureau's estimates of poor school-age children, produced as part of its Small Area Income and Poverty Estimates (SAIPE) Program, have established that the updated estimates are more accurate than outdated estimates from the decennial census (see Chapter 3). However, these same studies have also highlighted a need for further improvement in the estimates, particularly for subcounty areas. Research and development of the state and county models, as recommended by the panel, can help. However, marked improvement in the SAIPE estimates, particularly for school districts or other very small areas, will require new data sources. Possible new sources of household survey data, discussed in this chapter, may support significant improvements in the quality of the estimates in the next decade and beyond. (Improved administrative records data that may also play an important role are discussed in Chapter 5.)

Estimates from the SAIPE Program now reflect the income and poverty measurements in the Current Population Survey (CPS) March Income Supplement, which asks each March about the previous year's income for a sample of about 50,000 households. The state and county models are tied to the CPS in that the dependent variable in the regressions –the variable being predicted–is from 1-year CPS estimates in the state model and from 3-year average CPS estimates in the county model. Other data sources, including the 1990 census and administrative records,

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

provide predictor variables in the models, but the goal is to predict CPS-measured income and poverty. The school district model is tied to the CPS as well: 1990 census shares or proportions of poor school-age children for school districts within counties are applied to updated estimates from the CPS-based county model.

The use of the CPS as the dependent variable in the SAIPE models reflects a shift from the previous standard of measurement for many uses of small-area income and poverty estimates (e.g., allocating Title I funds), which was the decennial census long-form survey. The definitions of income and poverty are the same in the census and CPS, in that both use the official concept of income (before-tax money income for a calendar year), the official poverty thresholds for different size and type families, and the official unit of measurement (families and unrelated individuals as defined by the Census Bureau). However, differences in data collection procedures and other aspects of the two surveys result in somewhat different measurements. For example, the 1990 census estimate of U.S. median household income (for 1989) was 4 percent higher than the corresponding estimate from the March 1990 CPS, continuing a pattern from previous censuses (see Citro, 1996). Similarly, the 1990 census estimate of the proportion of U.S. poor school-age children was 6 percent lower than the corresponding March 1990 CPS estimate (National Research Council, 2000c:Ch.3).

The CPS is currently the source of official annual income and poverty statistics, and it has several advantages over the decennial census for that purpose. It is conducted more frequently than the census and so permits more regular updating of estimates. Also, the CPS is believed to provide more accurate measures of poverty and income than the census, primarily because it asks more questions about income and is conducted by personal and telephone interviewing instead of mailout/mailback techniques.1 A main drawback of the CPS, which the regression modeling procedure is intended to address, is the small size of the sample compared to the census long-form sample. This small sample size, together with the clustering of the CPS sample design, results in sizable sampling variability of the CPS state estimates and a lack of any sample in most counties and school districts.

Looking to the future, several household surveys could contribute to improved estimates from the SAIPE program, and, in addition, the sample size of the March CPS itself may increase. These surveys are:

1  

In the evaluations of the SAIPE estimates of poor school-age children, the 1990 census was used as a standard of comparison for SAIPE estimates produced for 1989 because of a lack of other sources for external evaluation (see Chapter 3). However, this use does not make the census a “better” standard of measurement than the CPS.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×
  • The 2000 census long form, which will provide small-area estimates of income and poverty for 1999 from a sample of about 18 million housing units (about one-sixth of total housing units, similar to the 1990 census long-form sample size);

  • The American Community Survey (ACS), which is currently under development and contains content similar to the census long form (see Chapter 2); and

  • The Survey of Income and Program Participation (SIPP), which plans to start a new panel in 2001 (see Chapter 2).

In the remainder of this chapter, we first compare the major features of the 2000 census long-form survey, ACS, March CPS, and SIPP. We then consider alternative uses for these surveys in the SAIPE Program, including: direct estimates for some areas; estimates to use as dependent variables in models; estimates to use as predictor variables in models; estimates for smaller areas of their shares or proportions of the poor population in larger areas; and estimates for controlling or calibrating other estimates on selected characteristics. The chapter ends with a summary of the panel's conclusions and recommendations on these uses.

To evaluate which uses would be feasible and desirable for one or more of the surveys, we focus on the reliability of survey estimates in terms of their error due to sampling variability; how frequently survey data are available and on what time schedule; and the quality of survey income measurements and how they compare with CPS measurements. Comparability is particularly important if another survey (e.g., the ACS) is to provide the basis for the dependent variables in the SAIPE models in place of the CPS. Depending on the extent of comparability, such a change could alter the standard of measurement and have unintended consequences for the use of estimates in formula allocations (see Chapter 6). However, using another survey for this purpose would be warranted if the change is judged likely to significantly improve the estimates.

Because no survey can provide direct estimates of sufficient reliability, timeliness, and quality to replace all of the SAIPE estimates, the panel concludes that SAIPE must continue to rely primarily on models for updated estimates for small areas. To determine how SAIPE models can best use the income and poverty data from surveys, the Census Bureau will need to learn more about measurement differences among them. To this end, the panel recommends exact matches and other comparisons of the CPS, ACS, and SIPP with the 2000 census records.

If it is implemented as planned, the ACS will provide subnational estimates that are available as frequently as estimates from the March CPS and are more reliable than those estimates. For states, the ACS estimates, averaged over a year, will be sufficiently reliable that they

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

could be used directly for SAIPE. For most smaller areas, the ACS estimates will not be sufficiently reliable to be used directly, even when averaged over several years, but they could be used in models. For the SAIPE county models, the panel recommends that the Census Bureau begin research and development now to explore the use of ACS estimates either to provide one of the predictor variables in CPS-based models or to serve as the dependent variable in the models.

The Census Bureau should also conduct research on using ACS estimates, in place of or possibly combined with estimates from the previous census, to form within-county shares or proportions for school districts and other subcounty areas to apply to updated county model poverty estimates. The shares approach for subcounty estimates is necessary until such time as appropriate administrative data are developed for subcounty areas that can support a statistical model similar to the state and county models.

If the ACS is to play a major role in the SAIPE Program along the lines suggested by the panel, the survey needs to have consistent levels of funding over the next decade that are sufficient for the planned sample sizes. Insufficient funding would likely lead to reduced sample sizes and other discontinuities in the data that could jeopardize the usefulness of the ACS for SAIPE and, more generally, make it difficult to assess the potential of ACS data for small-area estimation.

The panel sees a continuing role for indirect use of the census long-form estimates in the SAIPE Program. The Census Bureau should plan to use 2000 census estimates as predictor variables in the current SAIPE state and county models. The role of the 2000 census direct estimates is less clear. These estimates will be quite reliable for states, many counties, and some smaller areas and will have face validity with users. However, to use these estimates as the SAIPE estimates (for 1999) could result in inconsistencies in the time series of estimates. Also, 2000 census long-form estimates will be unreliable for many school districts and other small areas, and the estimates may not be available in time to meet the Census Bureau's current production schedule, which calls for 1999 SAIPE estimates to be released in fall 2002. The panel recommends that the Census Bureau review alternative approaches for the 1999 SAIPE estimates with key users, so that the Bureau's decisions about whether and how to use the 2000 census direct estimates for 1999 are well understood.

Finally, work is under way at the Census Bureau on experimental measures of poverty, based on the report of a National Research Council panel (1995a), which recommended revising the poverty threshold concept and the definition of family income and using income estimates from SIPP, which is believed to obtain better measures of income and poverty than the CPS. Should the Census Bureau decide to use SIPP for official

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

poverty statistics based on a revised concept (changes in the SIPP design and more timely data processing would be needed to make this feasible), then it would be important to consider how to adjust SAIPE estimates to agree with SIPP totals for selected characteristics, such as age, race, and geographic region.

The panel has outlined an ambitious program of research and development for the Census Bureau to determine the best uses of household survey data for SAIPE models. Such a program may be quite costly, and the Census Bureau will need to monitor progress carefully to try to identify the most promising approaches on which to focus scarce resources. Offsetting the costs is that many of the activities recommended– such as exact matches of survey and census records–will be helpful for many other uses of household survey data, in addition to SAIPE.

SURVEY FEATURES

This section describes the main features of the 2000 census long-form sample, ACS, March CPS, and SIPP, including content, sample size and design, data collection schedule and procedures, residence rules, response rates and other quality measures, and data processing and release. Table 4-1 summarizes the key features of each survey.

2000 Census Long Form

The 2000 census, like every census since 1960, included a long-form questionnaire that was administered to a sample of households. The long form contains the short-form questions that are asked of all households and additional questions. The added questions include total income and income by type from seven different sources (e.g., wages, Social Security) for the previous calendar year for each household member aged 15 or older. Both the short-form and long-form census questions are mandatory.

Design

The sample design for the 2000 census long form was somewhat modified from that used in the 1990 census. In 1990 the overall sampling rate was about 1 in 6, producing a sample of about 15.7 million occupied housing units. Variable sampling rates were used to provide somewhat more reliable estimates for small areas and to decrease respondent burden in more densely populated areas. Specifically, the sampling rate was 1 in 2 housing units for governmental areas with an estimated 1988 population of fewer than 2,500 people. For other areas, the sampling rate was

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

1 in 6 housing units in census tracts and block numbering areas with a precensus housing count of fewer than 2,000 housing units (fewer than about 5,200 people) and 1 in 8 housing units in larger census tracts and block numbering areas. The definition of areas for the 1-in-2 sampling rate included counties, towns, and townships, but not school districts (unless they happened to be coterminous with another governmental area).

In 2000 the overall sampling rate was also about 1 in 6, producing a sample of about 18 million housing units, but the variable rates were somewhat different from 1990. In 2000 the sampling rate was 1 in 2 for governmental areas with fewer than 800 housing units (fewer than about 2,100 people); 1 in 4 for governmental areas with 800-1,200 housing units (about 2,100-3,100 people); 1 in 6 for census tracts with fewer than 2,000 housing units (fewer than about 5,200 people); and 1 in 8 in larger census tracts. This design adds one more sampling rate, so that governmental areas with populations only slightly larger than areas with a 1-in-2 sampling rate will have a smaller increase in the proportional sampling error of their estimates compared with the 1990 sample design. For determining sampling rates in 2000, governmental areas were defined to include school districts in addition to counties, towns, and townships.

Data Collection

Data collection in the census is mainly by self-enumeration: a respondent for each household fills out a questionnaire received in the mail. Enumerators follow up those households that fail to return a questionnaire and collect the information through direct interviews. The follow-up enumerators are usually temporary workers who are given limited training.

Residence Rules

Residence rules for reporting household members in the census are that people who “usually” live at a residence should be reported and that people who are temporarily visiting should be excluded, unless they have no other permanent home. The usual residence for college students is their college residence and not their home residence; similarly, the usual residence for people who work away from home is their workplace residence if they live there most of the time. The usual residence for people with two homes is their permanent residence and not their vacation home.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×
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Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

TABLE 4-1 Key Features of Major Household Surveys

Feature

2000 Census Long Form

American Community Survey

March Current Population Survey

Survey of Income and Program Participation

Type of Survey, Frequency

Mandatory survey, part of census every 10 years since 1960

Mandatory monthly survey, tested in 4 sites in 1996, 8 sites 1997-1998, 31 sites 1999-2001, national survey 2000-2002, full implementation planned beginning in 2003

Voluntary monthly labor force participation survey, begun in 1940s; income supplement every March

Voluntary panel survey: each of 1984-1993 panels covered about 2.5 years; 1996 panel covered 4 years; 2000 panel to cover 1 year; 2001 panel to cover 3 years

Income Data

Total income; income from seven sources for previous calendar year

Total income; income from seven sources for previous 12 months

Detailed questions on about 28 sources for previous calendar year

Detailed questions on about 65 sources for each month or for 4-month period preceding interview

Sample Size and Design

Systematic sample of household addresses and residents of group quarters: average sampling rate of 1-in-6; rates of 1-in-2 or 1-in-4 for small governmental units and 1-in 8 for large census tracts; total sample size about 18 million housing units

Similar design to 2000 census long form; planned sample size before nonresponse of 3 million housing units (including vacant units) per year; design alternatives being considered that would oversample rural and hard-to-enumerate areas

Clustered sample of household addresses with state-representative design: addresses are in the sample for 4 months, out for 8 months, and in again for 4 months; total sample size of 50,000 occupied households plus 2,500 Hispanic households interviewed in previous November

Clustered sample of household addresses: original sample of occupied households was 12,500-23,500 for 1984-1993 panels; 37,000 for 1996 panel, with oversampling of low-income households; 11,000 for 2000 panel; 37,000 planned for 2001 panel

Data Collection Mode

Mail survey, personal follow-up for nonresponse

Mail survey, telephone follow-up, and then personal follow-up for one-third of mail and phone nonrespondents

1st and 5th interviews in person; other six interviews by phone

1st, 2nd, and one interview in each subsequent year of a panel in person; other interviews by phone

Residence Rules

Usual residence; college students in dorms counted at college location

“Current” or 2-month residence rule

Usual residence; college students in dorms counted at parents' address

Similar to CPS; members of originally sampled households followed for life of panel

Response Rates

1990 mail response rate 74% for occupied households; net undercount of 1.8% after follow-up; 19% of aggregate income imputed

Mail response rate 61% in 4 test sites, plus 8% from phone follow-up, plus 9% from one-third follow-up of remaining nonrespondents, for weighted response rate of more than 95%; item response may be better than census, but not coverage

94-95% households respond, but some do not respond to income supplement or for all household members; coverage estimated at 92% of census; 20% of aggregate income imputed

91-95% households respond to 1st wave, but sample attrition occurs; cumulative response only 69% by wave 8 of 1996 panel; coverage similar to CPS; 11% of aggregate income imputed

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

Publication

Long-form data planned to be released in 2002; planned to be controlled to short-form data adjusted for undercount; data are published for such small areas as census tracts

Annual reports planned of 12-month averages for areas with 65,000 or more people, 5-year averages for areas with fewer than 20,000 people; goal is to publish 6 months after data collection

Income and poverty data published for nation and population groups 6 months after data collection; limited data published for states on basis of 3-year averages

No regular publication series; special reports published for nation and population groups; historically 1-2 year (or more) lag from data collection to publication

Proposed Changes

Long form may not be included in 2010 or later censuses

May replace census long form

Recently received funding to expand sample size for state estimates of low-income children not covered by health insurance

Funding being requested to expand sample size and number of panels and for state-representative design

Response Rates

Household response rates to the census mailout have declined between 1970, when mailout-mailback techniques were first used, and 1990. In 1990 approximately 74 percent of U.S. households returned their questionnaires with some or all of the requested information; the response rate for households receiving long forms was somewhat lower (70%) than that for households receiving short forms (75%). Data from the balance of the population were obtained by personal interviews (National Research Council, 1995b:189-190).

As in all censuses, some people were uncounted in 1990, and there were also duplications and other erroneous enumerations. The net undercount in 1990 (gross undercount minus gross overcount) was estimated at 1.8 percent for the total population, but there were substantial differences among population groups. For example, the net undercount was estimated at 5.7 percent for blacks and 1.3 percent for nonblacks. The net undercount also varied significantly by age: almost two-thirds of the estimated omitted population consisted of children under age 10 and men aged 25-39 (Robinson et al., 1993:13). The undercount was higher in large cities than in other areas, and it was disproportionately concentrated in the inner areas of those cities. It is likely that undercount rates were higher for lower income groups.

Item nonresponse rates in 1990 were generally higher for income than for most other items. When household income information is missing, the Census Bureau uses statistical techniques to impute it on the basis of

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

nearby households with similar characteristics. On average, 19 percent of aggregate household income was imputed for 1990 (National Research Council, 1995b:387).

Publication

Processing and release of the long-form sample data occur later than for the short-form, and long-form estimates on such characteristics as age, race, and sex are controlled to match the corresponding estimates from the short form for various levels of geography. For 2000, the long-form data are planned to be controlled to short-form data that have been corrected for measured population undercount.

Long-form data, including income and poverty estimates, are provided for areas as small as census tracts, school districts, and block groups. Typically, long-form data products are released beginning in year 2 and continuing through year 3 after the census year.

American Community Survey

The American Community Survey is planned to be a large-scale, continuing monthly sample survey of housing units in the United States, conducted primarily by mail. Its content will be similar to that of the decennial census long-form sample, including questions that permit constructing income and poverty estimates for households in small areas. The income questions ask about total income and income from seven

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

different sources for the 12 months preceding the interview month. It is planned that the ACS will be mandatory, like the census, rather than a voluntary survey (although some or all of the ACS questions could be made voluntary in the future). If the ACS is successfully implemented, there will likely be no long form in the 2010 and subsequent censuses.

Development and Design

The ACS was tested in four sites in 1996 and in eight sites in 1997-1998. Beginning in 1999 and extending through 2001, the ACS will be conducted in 31 sites, chosen to facilitate comparison with the 2000 census long-form data for census tracts and other areas. In 25 of the 31 sites, about 0.4 percent of housing units are being sampled each month, which will generate a sample of about 5 percent of housing units for each of the 3 years, or 15 percent for the 3-year period. In the other 6 sites, for budgetary reasons, the 3-year sample will be about 9 percent in 5 of the sites and 3 percent in 1 site. For each year from 2000 to 2002, a nationwide survey, using the ACS questionnaire, will sample about 700,000 housing units, using a clustered sample design.

Beginning in 2003, the full ACS sample will be 250,000 housing units each month throughout the decade, for an annual sample size of about 3 million housing units spread across all counties in the nation. Over a 5-year period, the addresses selected for the ACS sample will cumulate to about 15 million housing units, similar to but somewhat smaller than the expected 2000 census long-form sample size of about 18 million housing units. Some of the ACS sample housing units will be vacant, and the sample size that is available for analysis will be further reduced by the ACS data collection procedures (see below).

Each month's ACS sample will be drawn from the Census Bureau's Master Address File (MAF) for the entire nation. The MAF is a comprehensive residential address list developed for the 2000 census that the Census Bureau intends to update on a continual basis following the census (see Chapter 5). The current design calls for the ACS to use a sample design similar to that of the 2000 census long form, with higher sampling rates for small governmental units (including school districts) and lower sampling rates for large census tracts. The sampling rates would be applied by systematic sampling from the MAF.

Some alternative sampling rates are being considered for the ACS. One scheme would make sampling rates decline as a smooth function of population size rather than vary by population size categories, until reaching a maximum sampling rate for very small areas. The maximum rate, cumulated over 5 years, could be higher than the highest long-form sampling rate in order to provide more reliable data for rural communities.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

Another scheme would sample hard-to-enumerate areas at a higher rate than other areas.

Data Collection

The ACS will be conducted by a mail questionnaire, similar to the census long form, to all households in the sample. A replacement questionnaire will be mailed to nonresponding households about 3 weeks later. After about another 3 weeks, nonresponding households will be contacted to the extent possible by the use of computer-assisted telephone interviewing (CATI). In the final stage of follow-up, a one-third sample of the remaining nonrespondent households will be drawn, and field representatives will be sent to interview these households in person, using computer-assisted personal interviewing (CAPI) techniques.

Residence Rules

Residence rules for the ACS are somewhat different from the census because of the ACS's design as a continuing survey. The ACS uses a “current” or “2-month” residence rule: if a person in a sample unit at the time of survey contact is staying there for more than 2 months, he or she is a current resident of that unit whether or not the unit is also the person 's usual residence under census rules. If a person who usually lives in the unit is away for more than 2 months at the time of contact, he or she is not a current resident of that unit. Anyone staying in the unit at the time of contact who has no other place where they usually stay is considered a resident of the unit.

Response Rates

Responses were obtained from about 78 percent of the originally designated sample for the four initial ACS test sites: 61 percent of households responded by mail, 8 percent responded to the telephone follow-up, and 9 percent responded to the personal follow-up. That last 9 percent were most of the 11 percent of households that were designated for personal follow-up (one-third of those that did not respond by mail or telephone; see Love and Diffendal, 1998). Because of subsampling at the final stage of follow-up, the weighted response rate in the four initial ACS test sites was more than 95 percent.

Preliminary results from the 1996 ACS test sites showed lower item nonresponse rates than in the 1990 census, at least for some items (Salvo and Lobo, 1998; Tersine, 1998). But the ACS, like other household surveys, may cover the population less well than the census, based on one

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

analysis that found more small households and fewer large households in the 1996 ACS than in the 1990 census (Ferrari, 1998). This result could indicate that the ACS is missing a larger proportion of people in interviewed households than are missed in the census.2

Publication

Once it is fully implemented, publication plans for the ACS call for the Census Bureau to issue annual reports containing yearly averages of the monthly data for areas with 65,000 or more people. Such areas include all states, about 25 percent of counties, and about 4 percent of school districts. For smaller areas, the Census Bureau also plans annual publication of multiyear averages: 3-year averages for areas with 20,000-65,000 people and 5-year averages for areas with fewer than 20,000 people.3 On this basis, 5-year averages will be required for about 47 percent of counties and about 82 percent of school districts.

Although delivery schedules are not known with certainty, yearly averages from the full ACS should be available within a year after the ACS is fully implemented in 2003 (i.e., in 2004). However, 3-year averages will not be available until 2006 at the earliest, and 5-year averages will not be available until 2008 at the earliest. Once sufficient years of data are cumulated to provide the planned 1-, 3-, or 5-year averages, as appropriate, each set will be updated yearly and published within 6 months after the close of a calendar year.

CPS March Income Supplement

The Current Population Survey is a voluntary monthly labor force participation survey, begun in the 1940s, that includes supplemental questions in many months. For the annual March Income Supplement, the

2  

In addition to within-household undercoverage, which occurs when some but not all household members are listed in the interview, there is undercoverage due to whole household misses, which this study did not address. Smith (1999) compared ACS estimates for the counties in the 1996 and 1997 test sites before adjustment to population controls with the population estimates for those counties and found some degree of undercoverage for most of the counties relative to the population estimates. These comparisons include both within-household and whole-household misses.

3  

The population cutoffs for requiring averages of 1 to 5 years correspond to about a 12 percent coefficient of variation for a 10 percent estimate with a typical design effect (Alexander, 1998). Estimates of poor school-age children more typically represent 3 percent of the total population, and, thus, an estimate for them will have a coefficient of variation nearly double that for a group with a similar design effect that is estimated to be 10 percent of the population.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

CPS asks household respondents about income received during the previous calendar year, using a detailed set of questions for identifying about 28 different sources.

Design

The monthly CPS sample, beginning in 1996, includes about 50,000 households, or 1 in 2,000–a reduction in sample size of about 17 percent from the early 1990s. Part of the CPS sample is changed each month in a rotation plan: each sampled address is in the survey for 4 months, out of the survey for 8 months, and in the survey for another 4 months, so that three-fourths of the sample addresses are common from one month to the next, and one-half are common for the same month a year earlier. Each March, to obtain more reliable income data for the Hispanic-origin population, all November CPS households with one or more Hispanic persons are reinterviewed if they still include a Hispanic person. This procedure adds about 2,500 Hispanic households to the sample in March.

The CPS uses a multistage probability sample design, which is revised about once every 10 years on the basis of the results of the latest census. A design based on the 1990 census was phased in between April 1994 and July 1995: it included 792 sample areas consisting of about 1,300 counties, chosen to represent all 3,143 counties and independent cities in the 50 states and the District of Columbia.4

The CPS has a state-representative design, which generally results in larger CPS sample sizes for larger states, but with the largest states having CPS sample sizes that are smaller than their proportionate share of the U.S. population and the smallest states having proportionately larger sample sizes. For example, California, with 12.2 percent of the U.S. population, has 9.9 percent of the CPS sample; Wyoming, with 0.18 percent of the U.S. population, has 1.3 percent of the CPS sample. This sample design means that income and poverty estimates in large states are generally more precise than those in smaller states. The largest states, however, have larger relative errors due to sampling variability than would be expected if the CPS sample were allocated to the states in proportion to their population; the reverse is true for smaller states.5

4  

In January 1996 the number of sample areas was reduced from 792 to 754.

5  

To meet national-level reliability criteria for the unemployment rate, the sample size in a few large states (e.g., California, Florida, New York, Texas) is somewhat greater than what would be required by a state-based design. A full description of the CPS design is provided by U.S. Census Bureau and Bureau of Labor Statistics (2000); see also the joint Bureau of Labor Statistics and Bureau of the Census CPS web site: www.bls.census.gov/ cps/mdocmain.html.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

In fall 1999 the Census Bureau received an appropriation to adjust the March CPS sample size and design to provide reliable annual estimates at the state level of the numbers of low-income children lacking health insurance coverage by family income, age, and race or ethnicity. The Bureau has not yet decided what changes to make to the CPS for this purpose.

Data Collection

Data collection for the CPS is carried out by permanent, experienced interviewers. The first interview and fifth interviews at an address are usually conducted in person; the other six interviews at an address are usually conducted by telephone; CAPI and CATI are used. One household member who is aged 15 or older is allowed to respond for other members.

Residence Rules

Residence rules in the CPS are similar to the census, except that the “usual” residence concept is applied more broadly. For example, college students who are counted at the location of their college residence in the census are included in the CPS household of their family.

Response Rates

Response rates in the CPS are high: typically, about 94-95 percent of households respond to each month's CPS.6 However, some interviewed households do not provide information for all members, so there is little data beyond basic demographic characteristics for about 9 percent of members of interviewed households. In addition, some people who respond to the basic CPS labor force questionnaire do not respond to the March Income Supplement. To adjust for whole household nonresponse to the basic CPS, the Census Bureau increases the weights of similar responding households. To adjust for person nonresponse to the basic CPS, it imputes a complete data record for another person with similar demographic characteristics.

Like other household surveys, the CPS exhibits population undercoverage at higher rates than the census itself. For March 1994, the ratio of the CPS-estimated population to the census-based population control total (all ages) was 92 percent; for black men aged 30-44 years, the cover-

6  

Household response rates declined 1-2 percentage points beginning in 1997.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

age ratio was as low as 68 percent (U.S. Census Bureau, 1996:Table D-2). It is estimated that about two-thirds of CPS undercoverage is due to missed people in otherwise interviewed households (i.e., people whose existence is not known to the interviewer); the remainder is due to missed housing units. CPS undercoverage is corrected by ratio adjustments to the survey weights that bring the CPS estimates of population in line with updated national population controls by age, race, sex, and Hispanic origin. Beginning with the March 1994 CPS, the population controls for survey weights reflect an adjustment for the undercount in the census. However, the ratio adjustments do not correct for other characteristics, such as income, on which the undercovered population might be expected to differ from the covered population in each adjustment cell.

There is substantial item nonresponse in the March Income Supplement. About 20 percent of aggregate household income is imputed (about the same percentage as in the census; see National Research Council, 1993:Table 3-6). Imputation techniques are used to provide values for people who fail to respond to the income supplement entirely, as well as for people who fail to answer one or more questions on the supplement.

Publication

Publication of detailed official income and poverty estimates from the CPS for the nation as a whole, geographic regions, and population groups occurs each year about 6 months after data collection in March. Limited statistics are also published for states on the basis of 3-year averages.

Survey of Income and Program Participation
Design

The Survey of Income and Program Participation is a continuing voluntary panel survey. The first panel of households (the 1984 panel) began in October 1983. From 1985 to 1993, a new sample (panel) was introduced each February. Adult members of originally sampled households in each panel were followed and interviewed every 4 months for 32 months, although some panels had fewer than 8 interview “waves” because of budget restrictions and some panels had 9 or 10 waves. The 1996 panel, begun in April, followed both adult and child members of originally sampled households every 4 months for 4 years. A new 1-year panel, which may be extended to 3 years if funding is obtained, began in February 2000, and a new 3-year panel will begin in February 2001.

SIPP is focused on income measurement. The core questionnaire, administered at each interview wave, obtains detailed information for

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

each month of the reference period on sources and amounts of income from earnings and public and private transfer payments and information for the 4-month period on income from assets. In total, about 65 separate sources of cash income are identified, together with benefits from 7 inkind programs. Additional detail on program participation and related topics (e.g., child care, health) is collected in various supplements (topical modules). Four waves of a SIPP panel are required to calculate annual income and poverty statistics for a calendar year.

The SIPP sample covers the U.S. civilian noninstitutionalized population and members of the armed forces living off-post or with their families on-post. Sample size for the 1984-1993 panels varied from 12,500 to 23,500 households in the initial wave of interviewing. The sample size for the initial wave of the 1996 panel was 37,000 households: it included households in all states but was not designed to provide reliable estimates at the state level. The 1996 sample included an oversample of addresses in which the residents had family incomes below 150 percent of the poverty level in 1989, based on information from the 1990 census. Proxy characteristics, such as housing tenure and family type, were used for over-sampling addresses for which only short-form census information was available. In rural areas, some addresses were oversampled on the basis of 1990 census poverty-related characteristics for the census block in which they were located.

The sample size for the 1-year 2000 panel is 11,000 households, while the planned sample size for the 3-year 2001 panel is 37,000 (or possibly more) households; another large-size panel will begin in 2004. If funding is obtained to enable SIPP to become the basis of official income and poverty statistics in place of the March CPS, then the smaller 2000 panel will be extended for 3 years, and overlapping 3-year panels about the size of the 2000 panel will begin in 2002 and 2003. In addition, the design of the sample will be modified to represent all states and provide estimates for the largest states that have about the same level of error due to sampling variability as the current March CPS. Such a sample redesign, however, cannot be made until after the 2000 census results have been analyzed and used to redesign the samples for all major household surveys, which could take several years.

Data Collection

Data collection for SIPP is carried out by permanent, experienced interviewers. The first and second interviews and one interview in each subsequent year of a panel are conducted in person, using CAPI techniques. Other interviews are conducted by telephone from interviewers ' homes. Household members age 15 or older are supposed to respond for themselves, but proxy responses are accepted. About 35 percent of inter-

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

views for adults in each wave are by proxy; for panels conducted before 1996, 60-65 percent of adult sample members had at least one proxy interview (U.S. Census Bureau, 1998c).

Residence Rules

With regard to residence, SIPP follows members of originally sampled households. Sample members, including adults and children, who move to new households at subsequent waves are followed, and information is obtained about the coresidents in their new households according to the CPS “usual residence” rules. Sample members who become institutionalized are tracked and interviewed subsequently if they return to a household setting.

Response Rates

Response rates to the first wave of a SIPP panel are somewhat lower than CPS response rates: about 5-8 percent of eligible households in the 1984-1991 SIPP panels did not respond to the first interview wave and were dropped from the sample; the household nonresponse rate for the first wave in the 1992 and 1993 panels was 9 percent; for the 1996 panel it was 8 percent. By wave 8, the cumulative household nonresponse rate in the 1984-1991 panels was 21-22 percent; in the 1992 and 1993 panels it was 25 percent. By wave 8 of the 1996 panel, the cumulative nonresponse rate was 31 percent. About three-quarters of household nonresponse is due to refusals, and one-quarter is due to losing track of sample household members who move (U.S. Census Bureau, 1998c:Ch.5).

People who drop out of SIPP tend to differ from those who stay in the survey: attrition is more likely to occur among young adults, males, minority groups, never-married people, poor people, and people with low educational attainment (see, e.g., Lamas, Tin, and Eargle, 1994). There is also evidence that the current noninterview weighting adjustments do not fully compensate for differential attrition across population groups (see, e.g., King et al., 1990).

Like the CPS and other household surveys, SIPP covers the population less well than the census. Coverage ratios (survey population estimates divided by census-based population estimates) are similar for the CPS and SIPP.

SIPP has lower item nonresponse rates than the March CPS: overall, only 11 percent of total regular money income obtained for calendar year 1984 from the first four waves of the 1984 SIPP panel was imputed, compared with 20 percent in the March 1985 CPS. The SIPP and March CPS imputation rates for 1984 for earnings were 10 percent and 19 percent,

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

respectively; for public and private transfers, 12 percent and 21 percent, respectively; and for property income, 24 percent and 32 percent, respectively (Jabine, King, and Petroni, 1990:Table 10.8; see also National Research Council, 1993:Tables Table 3-4, Table 3-5).

Publication

Data processing for SIPP involves complex operations, particularly to produce calendar-year and longitudinal panel files. Historically, this has often resulted in delays of 1, 2, or more years between collection of data from an interview wave or all waves in a panel and release of data files and publications. There is no regular publication series for SIPP; publications are released on topics of interest, such as program participation, and include estimates for population groups and the total population by region and metropolitan or nonmetropolitan residence.

USES OF SURVEYS FOR SAIPE

This section notes five different ways in which household surveys could be used for updated SAIPE income and poverty estimates.

First, a survey could provide direct estimates for some or all small areas. For this use, the survey estimates should be available on a frequent, timely basis, at least every 2 years given the current SAIPE production schedule. They should also have acceptably low levels of error due to sampling variability. To reduce sampling variability, survey estimates could be averaged for more than 1 year.

Second, survey estimates for a single year, or averaged over more than 1 year, could be used to form the dependent variable in SAIPE models. If another survey were used for this purpose in place of the March CPS, comparability of the survey income and poverty measurements with the CPS measurements would be desirable, to reduce the likelihood of anomalies in the time series of estimates. For this use, survey estimates must be available on a frequent, timely basis.

Third, survey estimates for a single year or averaged over more than 1 year could be used to provide predictor variables in models. Indeed, models could be developed that include predictor variables from estimates for more than one survey for more than 1 year, using time-series or multivariate modeling techniques (see Chapter 3). As an example, a hypothetical county-level model could include as predictor variables estimates from the 1990 and 2000 censuses and multiple years of the ACS.

For use in prediction, the survey estimates must be available for all areas for which model-based estimates are required. Also, the survey

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

estimates should have low or moderate levels of sampling variability. If the estimates are highly unreliable, they will have weak predictive power, although the predictions will not necessarily be biased. Comparability of income and poverty measurements with the survey used to form the dependent variable is not critical for this use: a predictor variable need not measure exactly what is measured by the dependent variable to be a good predictor. Availability of estimates on a frequent, timely basis is desirable but not critical, as somewhat outdated estimates may nonetheless be reasonably good predictors of current income or poverty levels.

Fourth, survey estimates for smaller areas of their shares or proportions of populations of larger areas could be used to develop small-area income and poverty estimates by applying the proportions to updated model-based (or direct) estimates for larger areas. This approach is similar to the way in which 1990 census within-county shares of poor school-age children for school districts were applied to 1995 county model estimates to produce the 1995 school district estimates.

For this use, survey estimates must be available for all areas for which estimates of shares are required. Ideally, they should be available on a frequent, timely basis and have relatively low levels of sampling variability, which could be facilitated by averaging estimates across more than 1 year or by some type of smoothing procedure (see Chapter 3). Comparability of income and poverty measurements with the survey used to form the dependent variable for the model estimates to which the shares will be applied is desirable but not critical.

Fifth, survey estimates could be used to control or calibrate estimates from other sources on selected characteristics. For example, model-based estimates for states and counties, produced by using one survey to form the dependent variable, could be adjusted to agree with key national or large-area estimates from another survey (e.g., estimates by region, metropolitan versus nonmetropolitan status, minority status). This approach could be followed when the estimates from the survey used for calibration are believed to be of exceptionally good quality but are not reliable for states or smaller areas.

EVALUATING ALTERNATIVE USES

This section discusses several critical considerations for determining feasible and desirable roles for the 2000 census long form, ACS, March CPS, and SIPP in the SAIPE Program. These considerations are: sampling variability, timeliness, and comparability and quality of income and poverty measurements.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×
Sampling Variability

A key consideration in using survey data for small-area estimates is sample size and the resulting level of error due to sampling variability. The census long-form sample is very large by comparison with the CPS and other existing household surveys–about 1 in 6 households in the census compared with about 1 in 2,200 households in the March CPS and about 1 in 3,000 households in the 1996 SIPP panel. Moreover, the census long form provides for oversampling of small governmental units. Consequently, it provides estimates with much smaller sampling variability than do other surveys for areas of all population sizes. In fact, direct estimates are rarely published from such surveys as the CPS and SIPP for subnational areas, even states. Yet despite the large sample size for the census long form, sample estimates from it exhibit a large degree of variability due to sampling error for many school districts and other very small areas.

The ACS will be a far larger household survey than has ever been fielded by a federal statistical agency on a continuing basis–250,000 residential addresses each month, with each month's sample drawn independently. (Over a 5-year period, no address can be in the sample more than once.) By comparison, the CPS sample includes 50,000 households each month, of which a large fraction were in the sample of the previous month (75%) or of the same month in the previous year (50%).7 SIPP is even smaller than the CPS, and it also experiences considerable attrition, which not only reduces the sample size over the life of each panel, but also introduces bias into income and poverty estimates.

Nonetheless, the ACS estimates, when averaged over a year to provide a sample size of 3 million or about 1 in 36 housing units, will exhibit considerably higher sampling variability than estimates from the 2000 census long-form sample. Even when cumulated for 5 years, the ACS estimates will be more variable than the long-form estimates –not only is the 5-year ACS sample size somewhat smaller than the long-form sample size (about 1 in 7 compared to 1 in 6 households), but also, by design, the ACS follows up in the field only one-third of the households that do not respond by mail or telephone. As noted above, in the four initial ACS test

7  

The rotation design of the CPS, in which households are in the sample for 4 months, out of the sample for 8 months, and in the sample again for another 4 months, is advantageous in reliability terms for measuring changes in such statistics as the monthly unemployment rate from month to month or year to year. However, the design reduces the effective sample size for estimates that are based on averaging multiple months of data relative to a design in which each month's sample is independent of other months.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

sites, the total number of households with completed interviews was about 78 percent of the originally designated sample, due largely to the procedure whereby only one-third of mail and telephone nonrespondents are followed up in person. Moreover, this procedure results in a variation in survey weights (3 to 1, other things equal), which further reduces the effective sample size to about 62 percent of the originally designated sample (78% reduced by a factor to take account of the loss of precision from variable weights; see Kish, 1992).

Below we compare the sampling variability of direct estimates of poor school-age children for states from the 2000 census long-form sample, ACS, and CPS and for counties and school districts of different population sizes from the census and ACS. SIPP estimates would be more variable than CPS estimates.

State Estimates

Table 4-2 shows median coefficients of variation (in percent) for the March 1996 CPS estimates of the proportion poor of school-age children in 1995 for states classified by 1996 population size. Also shown are the estimated coefficients of variation for the proportion poor of school-age children in 1995 based on the 1990 census long-form sample design and on a 1-year average of a fully implemented ACS sample.

From the CPS, the median coefficient of variation for the school-age poverty rate is 7 percent for the largest three states, with total population of 18 million or more; it is 24 percent for the eight smallest states, with total population less than 1 million. For comparison, a common design goal for published survey estimates is a coefficient of variation of 10 percent or less.

From the census long form, the median coefficient of variation for the largest three states is 0.3 percent; it is 2.5 percent for the eight smallest states. The difference between the coefficients of variation for the largest and smallest states is greater for the census than for the CPS because the CPS sample is designed to be state representative. (As noted above, there are proportionately more sample households in smaller states and proportionately fewer sample households in larger states than would occur in an equal probability sample design.) Nonetheless, the census estimates for states are clearly superior to the CPS estimates in terms of error due to sampling variability.

The levels of sampling variability for 1-year ACS state estimates meet commonly accepted standards as well. Thus, the median coefficient of variation for 1-year ACS estimates is just under 1 percent for the largest three states, and it is 7 percent for the eight smallest states.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

TABLE 4-2 Coefficients of Variation (CV) for Estimates of the Percentage Poor of School-Age Children in 1995 for States Categorized by Population Size

     

Median Coefficient of Variation for States in Population Size Category (in percent)

State Population Size, 1996a

Number of Statesa

Median Percent Poor School-Age Children 1995

March 1996 CPS

1990 Census Long Form

1-Year Average ACS

Less than 1 million

8

18.3

23.7

2.5

6.8

1 million to less than 3 million

14

14.6

21.0

1.6

4.4

3 million to less than 6 million

16

14.3

21.3

0.8

2.2

6 million to less than 10 million

6

14.5

15.1

0.7

2.0

10 million to less than 18 million

4

17.8

11.8

0.5

1.3

18 million or more

3

22.5

7.1

0.3

0.9

NOTES: Percentages poor of children aged 5-17 for which coefficients of variation (the standard error of an estimated divided by the estimate) are calculated are those for 1995, as estimated from the March 1996 CPS. These percentages are used together with generalized variance functions to estimate the CVs that would be obtained with the 1990 census long-form sample design and a 1-year average of a fully implemented ACS sample.

aIncludes the District of Columbia.

SOURCE: The CVs for the March 1996 CPS are calculated from NationalResearch Council (2000c:Table 6-8, using one-half the differencebetween cols. 3 and 1, divided by col. 1); 1990 census long-formCVs are calculated by using a formula adapted from Siegel and Fisher(1998:1), assuming a design factor of 1.5; ACS CVs are calculatedby using a formula from U.S. Census Bureau (1999a), assuming a 3percent annual sample and a design factor of 1.6.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×
County and School District Estimates

Table 4-3 shows illustrative coefficients of variation for estimates of a school-age poverty rate of 18 percent from a sample size equivalent to that of the 1990 long form and from sample sizes equivalent to those for the ACS, averaged over 1, 3, and 5 years of full implementation, for areas of different population sizes. These coefficients of variation incorporate approximate design effects for the long-form census sample and the ACS. The calculations assume average sampling rates and do not allow for differences in sampling rates across areas. Given the oversampling of very small governmental jurisdictions in the census and ACS, the coefficients of variation from the census and ACS will be lower for these smaller areas than those in the table, and, conversely, they will be somewhat higher for larger areas.

For areas with 50,000 or more total population (27% of counties in 1990, but only 6% of school districts), the estimates of poor school-age poverty rates from the census long form have fairly low levels of error due to sampling variability, with coefficients of variation of less than 10 percent. Estimates from the census long form also have reasonably small coefficients of variation for areas with 20,000 or more population (53% of counties in 1990, 18% of school districts) –the coefficient of variation for these areas is 12 percent or less. For areas with 25,000 or more population, estimates from the ACS, when they are averaged over 5 years, have coefficients of variation that are reasonably small (13% or less), but the coefficients of variation for 1-year estimates from the ACS are more than twice as high as those for 5-year estimates, and the coefficients of variation for 3-year ACS estimates are about 29 percent higher than those for 5-year estimates.

For estimates for smaller areas, error due to sampling variability increases for both surveys. The coefficient of variation for estimates from the 1990 census long-form sample is 14 percent for areas with 15,000 population and 35 percent for areas with 2,500 population. 8 The coefficient of variation for estimates from the ACS averaged over 5 years is 15 percent for areas with 20,000 population and 42 percent for areas with 2,500 population. Almost one-half of counties (47%) and four-fifths of school districts (82%) have 20,000 or fewer people; 31 percent of school districts have 2,500 or fewer people, although such districts account for relatively small proportions of the population. Thus, the 82 percent of districts with 20,000 or fewer people include only 31 percent of total population; the 31 percent of districts with fewer than 2,500 people include only 3 percent of total population (see Table 4-3).

8  

The coefficient of variation would be smaller for areas with 2,500 population if the area were oversampled; see discussion below.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×
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Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

Table 4-3 Illustrative Coefficients of Variation for Estimates of an 18 Percent Poverty Rate for School-Age Children for Counties and School Districts by Population Size

 

Cumulative Percentage Distribution

 

Coefficient of Variation for Estimate from a Sample of the Size of the American Community Survey (percent)

 

Counties

 

School Districts

County or School District Population Size

Number of Areas

Number of People

Number of Areas

Number of People

1-Year Average

3-Year Average

5-Year Average

Coefficient of Variation for Estimate from a Sample of the Size of the 1990 Census Long Form (percent)

250,000

6.6

56.6

N.A.

N.A.

9.4

5.4

4.2

3.5

100,000

14.6

72.5

1.9

26.8

14.9

8.6

6.7

5.5

50,000

26.8

83.4

5.7

44.4

21.1

12.2

9.4

7.8

25,000

46.5

92.1

15.7a

63.4

29.8

17.2

13.3

11.0

20,000

53.4

94.1

17.7

68.9

33.3

19.3

14.9

12.3

15,000

63.2

96.3

24.2

76.4

38.5

22.2

17.2

14.2

7,500

83.3

99.1

41.9

89.2

54.4

31.4

24.3

20.0

5,000

90.5

99.6

52.7

93.6

66.6

38.5

29.8

24.6

2,500

96.2

99.9

69.1

97.4

94.2

54.4

42.1

34.7

NOTES: The coefficients of variation are calculated in each instance by assuming that 17 percent of the total population are school-age children and the poverty rate for school-age children is 18 percent. The calculations assume average sampling rates and do not allow for differences in sampling rates across geographic areas. County population size percentages are from Census Bureau data for 3,141 counties in 1990; school district population size percentages are from Census Bureau data for 9,243 school districts defined for 1990 in the Bureau 's 1980-1990 evaluation file. The evaluation file excludes 5,983 districts that existed in 1990–districts that were coterminous with a county, districts that did not cover both elementary and secondary grades, and districts for which all or part had no counterpart in 1980. The subset of school districts in the evaluation file closely resembles the entire set of 1990 school districts in terms of the distribution of total population.

aInterpolated.

N.A. Not available.

SOURCE: ACS 1-year average CVs are calculated using the formula inU.S. Census Bureau (1999a) for a 3 percent annual sample and a designfactor of 1.6. ACS 3-year average and 5-year average CVs are calculatedby applying a factor of 0.578 and 0.447, respectively, to the 1-yearaverage CVs. The 1990 census long-form CVs are calculated from theformula for the standard error of a proportion adapted from Siegeland Fisher (1998:1, which gives the formula for the standard errorof a number), assuming a design factor of 1.5.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

The decision to treat school districts as governmental units for purposes of oversampling in the 2000 census and ACS will reduce the sampling variability of estimates of income and poverty for small school districts. For a small school district of fewer than 2,000 people that was not in an oversampled governmental unit in 1990 and hence was sampled at a rate of 1 in 6, if that district is sampled at a rate of 1 in 2 in 2000, the coefficient of variation will be reduced by a factor of 0.45. However, the coefficient of variation will be still be high for such a small area; it will change from about 39 percent to about 18 percent. In the ACS, oversampling of a small school district will reduce the coefficient of variation for 5-year averages from about 47 percent to about 21 percent.9

Timeliness

The second key consideration in using survey data to produce small-area income and poverty estimates for such purposes as annual fund allocation is how regularly data can be provided and on what time schedule. The SAIPE Program is currently on a production cycle of releasing estimates every year for states and every 2 years for counties and school districts. The current lag between the release date and the income reference year is 3-4 years (e.g., 1997 estimates are scheduled to be released in fall 2000). We recommend (see Chapter 3) research and development to reduce the extent of the lag.

The 2000 census long form will provide only one observation, for the 1999 income reference year. The long-form data will likely not be available until 2002, so that the 1999 estimates may not be available in time to use, either directly or indirectly, for the SAIPE estimates for 1999, which are scheduled for release in late 2002. However, the data will be available subsequently to use in models.

The March CPS is conducted annually and data processing is completed within 6 months of data collection. The reason for the lag in producing estimates from the current CPS-based SAIPE models is that other data needed for the models, such as food stamp data, are not available to the Census Bureau on a timely basis.

SIPP is conducted yet more frequently than the March CPS (each panel is interviewed every 4 months); however, 4 waves of SIPP data are

9  

For areas of 2,000 to 3,000 people that are sampled at a rate of 1 in 4, the coefficient of variation would be reduced by a factor of 0.6.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

required to produce calendar-year income and poverty estimates. Also, SIPP has not yet been able to meet a regular production schedule, and data products are often provided 1, 2, or more years after data collection.

The ACS will be conducted monthly. It remains to be seen how soon it will achieve a regular, timely production schedule, but the intent is that income and poverty estimates can be updated on an annual basis and made available in the year following data collection. If ACS direct estimates can be used for SAIPE, averaged over 1 or more years, they will be more timely than the current model-based CPS estimates. However, if ACS estimates are used indirectly in models, reducing the time lag in the estimates will require efforts to improve the timely availability of other data used by the models or perhaps changes in how the data are used (e.g., perhaps using an earlier year of food stamp data as a predictor variable; see Chapter 3).

Comparability and Response Quality

The third key consideration for use of survey data for small-area estimates is the quality of the survey responses on variables used to measure income and poverty and the comparability of the information with that provided by other surveys. In particular, because the SAIPE Program estimates are currently based on the March CPS as the dependent variable in prediction models, it is important to understand how using another survey for this role, such as the ACS, would affect the consistency of the time series of SAIPE.

Research has documented significant differences between income and poverty estimates from CPS and SIPP, as well as between estimates from CPS and the census long form. Comparisons of CPS and SIPP poverty rates find that SIPP rates are consistently lower than CPS rates, although the difference in the two rates was less in the 1996 panel than in earlier panels. In 1991, the SIPP poverty rate for the total population was 15 percent below the CPS rate, and differences for some groups were even larger. For example, the SIPP poverty rate for the elderly in 1991 was 27 percent below the CPS rate, apparently due largely to more reports of Social Security income in SIPP than CPS (Martini and Dowhan, 1996; see also Short et al., 1998). As noted above, comparisons of census and CPS estimates of income and poverty generally find that the census produces higher estimates of median household income and lower estimates of poverty than the CPS.10

10  

No direct comparisons have been performed of SIPP and census income and poverty estimates.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

Such differences in income and poverty estimates among these surveys are not surprising. As discussed above, even though they use the same income and poverty definitions, the surveys differ in many other ways that could affect the estimates. To date research has not been able to establish which factors are most important in producing differences in income and poverty estimates across surveys, nor to determine which estimates are of higher quality. Even for SIPP and CPS, for which the most measurement research has been conducted, it is not possible to assess the total error in their poverty or income estimates nor to fully understand differences between them. Generally, it is believed that CPS estimates are of higher quality than census estimates and that SIPP estimates may be of higher quality yet, due largely to SIPP's focus on income measurement, which has produced more complete reporting of many sources of income than the CPS or other surveys. Under its current design, however, SIPP suffers from cumulative attrition over the life of a panel, which likely contributes to biases, such as an upward bias in measured income and a downward bias in measured poverty, that increase in later waves of a panel. (See Citro, 1995, for an assessment of CPS and SIPP income data; see also U.S. Census Bureau, 1998c.)

Income and poverty estimates from the ACS will likely differ not only from CPS and SIPP estimates, but also from census estimates, even though the ACS is designed to be very similar to the census long form. A study that compared median household income in the 1996 ACS test sites and the 1990 census, adjusted to 1996 dollars, found that the ACS produced significantly lower medians than the census in all four sites (Posey and Welniak, 1998). The ACS median incomes were also lower than 1993 median incomes (adjusted to 1996 dollars) from the SAIPE program for three of the four sites. It is not possible to determine what proportion of these differences is due to differences in measurement among the data sources and what proportion is due to socioeconomic changes for the areas over time. We note below some of the key differences between the ACS, the 2000 census, the March CPS, and SIPP that are likely to affect data comparability.

Type of Income Questions

The ACS questionnaire and the census long form include only seven questions on separate sources of income in contrast to the March CPS and SIPP, which include many more such questions. Evidence suggests that asking more questions on income elicits more complete reporting because it prompts recall of small or occasionally received income amounts (see Ycas and Lininger, 1983:27; Martini and Dowhan, 1996). However, that evidence applies to voluntary surveys such as CPS and SIPP. Whether

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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income reporting in mandatory surveys, such as the ACS and the census long form, is impaired by having fewer questions is not known. One study of the 1960 census, which showed income estimates higher than those from the March 1960 CPS, attributed most of the difference to a shortage of higher income families in the CPS, possibly due to higher rates of nonresponse to income items by such families in the voluntary CPS (Miller, 1966).

Respondent and Mode

Both the ACS and the 2000 census use mailout/mailback techniques as the principal mode of response: one household member is asked to fill out the income and other questions for all household members. The March CPS also allows one household member to respond for other members. SIPP, in contrast, strives to obtain self-reports from each adult household member, although proxy responses are often accepted. Interviewing for both the CPS and SIPP uses a combination of personal and telephone techniques. Research to date is not conclusive on the effects of either interview mode or proxy response on income reporting.

In addition to encouraging self response, SIPP encourages respondents to consult records, such as pay stubs, in reporting income sources and amounts, on the assumption that record use contributes to more complete income reporting. In the 1990-1993 panels, about 20 percent of respondents used at least one type of record.

Reference Period

Both the 2000 census and the March CPS ask about receipt of income over the most recent calendar year at a time when many people have just completed or are preparing their income tax returns. SIPP asks about income receipt on a monthly basis for many income types and for the 4-month period prior to each interview for other income types. Estimates for calendar years can be constructed from the SIPP data. In contrast, the ACS asks about annual income for a reference period that is the 12 months prior to the interview month.

Because the ACS neither has the short recall period of SIPP nor refers to a specific calendar year (except for the ACS households interviewed in January), it may exhibit higher levels of income underreporting than the census, March CPS, or SIPP. A split-sample experiment with the ACS questionnaire in fall 1997 determined no significant differences in median total income of individuals between respondents who were asked to report income for the preceding calendar year and those who were asked to report income for the past 12 months (Posey and Welniak, 1998); how-

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

ever, more research will be needed to assess the effects of the ACS reference period on income and poverty statistics.

Because the ACS monthly samples have different income reporting periods, it will not be possible to construct 12-month averages of annual income and poverty rates that refer only to a specific calendar year. Each month's sample will reflect a different 12-month reporting period, and the 12 monthly samples for year t will be centered on July of year t − 1 through June of year t (spanning 24 months in all). Estimates can be constructed for which the reporting period is centered on a calendar year by averaging monthly samples for July of year t through June of year t + 1. However, such estimates will still represent an average of different reporting periods, and, consequently, may differ from the estimates that would be obtained from the March CPS or the census. 11

Residence Rules

The ACS current or 2-month residence rule (see “American Community Survey,” above) will classify some people differently from either the census or CPS and SIPP “usual residence” concept. For example, college students in dormitories will be counted at the dormitory location in the census; at the dormitory location or the location of their family residences in the ACS, depending on the outcome of applying the 2-month rule; and at the location of their families in the March CPS and SIPP. Whether college students are counted at school or home will affect not only the size of their families, but also their families' income level and poverty threshold.

When annual (or longer) averages are constructed from the ACS for small areas, an issue arises that is similar to the issue of different reporting periods for income. Namely, in localities that experience large shifts in population (either seasonal shifts as may occur in college towns and retirement communities or secular shifts due to changing economic conditions or other factors), monthly samples may differ substantially in the number and characteristics of residents for a locality. Estimates of the numbers of residents and their income and poverty status that are con-

11  

The Census Bureau is presently adjusting ACS income dollar amounts for inflation to represent a common calendar reference year. For example, a household interviewed in March of 1996 reported its income for March 1995 through February 1996. The Census Bureau adjusted that income to a 1996 reference calendar year by multiplying it by the 1996 average annual consumer price index for all urban consumers (CPI-U) for January-December 1996 and then dividing by the average CPI-U for March 1995-February 1996. This procedure, however, does not address the problem that monthly ACS samples may experience a different mix of economic conditions over the reporting period.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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structed from the ACS by averaging over the relevant 12 months to obtain estimates for a calendar income reference year may not correspond to the estimates obtained for the people who are contacted in March or April following that year in the CPS or census.

Survey Procedures

The 2000 census, ACS, March CPS, and SIPP differ in many aspects of their survey operations, which could affect such features of data quality as household response rates, questionnaire item response rates, completeness of coverage of the population, accuracy of reporting, and accuracy of editing and imputation procedures. As an example, the ACS hopes to achieve quality improvements, in comparison with the census, by having a permanent interviewing staff instead of the army of temporary enumerators who are employed for the short time period in which census follow-up enumeration is conducted. Preliminary results from the 1996 ACS test sites (see “American Community Survey, ” above) suggest that the ACS interviewers may achieve lower item nonresponse rates, but not necessarily better population coverage, than the census.12

ANALYSIS AND CONCLUSIONS

Having considered the reliability, timeliness, and likely quality of data from the 2000 census long form, ACS, March CPS, and SIPP and the alternative uses that could be made of them for the SAIPE program (e.g., providing direct estimates, serving as predictor or independent variables in models), we have reached several conclusions and recommendations. We present our analysis under four headings –general, role of the ACS, role of the 2000 census long form, and revised poverty measure–and then list our formal recommendations.

General
Model-Based Estimates

From our review of the 2000 census long-form, ACS, March CPS, and SIPP, it is clear that the SAIPE Program must continue to use models to

12  

It is possible that the smoothing used to produce calendar-year estimates from the ACS may prove to be advantageous. For example, population and income estimates from averages of monthly samples for a college town may better represent the “typical” experience of that town over a year than estimates that are based on the population in March or April of the following year.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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produce indirect estimates of income and poverty for small areas. None of these surveys can provide direct estimates that are of sufficient reliability, quality, and timeliness to replace all of the small-area estimates produced by SAIPE.

The 2000 census long-form estimates will be reliable for all states and many counties, in that they will have acceptably low levels of error due to sampling variability, but the census estimates are only available for income year 1999. Also, the census estimates will not be reliable for most subcounty areas, such as most school districts, even for income year 1999.

One-year average estimates from the monthly ACS, once it is fully implemented, will be reliable only for states and a small percentage of counties, while 5-year average estimates will be reliable for a larger percentage of counties. However, there will still be a sizable proportion of counties and many smaller areas for which the estimates will have low reliability. Also, 5-year average estimates will not begin to be available until very late in this decade, and they could be viewed as problematic for some program uses because they will reflect changes in income and poverty with a considerable lag. For example, two areas may have the same 5-year average poverty rate, but one area may have a sharply increasing poverty rate over the period and the other area a sharply decreasing poverty rate.13 Moreover, the quality of the ACS income and poverty estimates has yet to be established. Consequently, using the ACS to provide direct estimates for the SAIPE Program, except for states, does not seem warranted absent considerable evaluation work.

The March CPS provides high-quality annual estimates, but it does not currently provide reliable direct estimates for any subnational areas, except for the very largest states. However, the CPS may provide reliable state estimates in the future, given the recent appropriation to adjust the sample size and design to provide reliable state estimates of low-income children who lack health insurance coverage. The estimates from SIPP at present are neither reliable for any subnational area nor available on a

13  

For fund allocation, the use of 5-year averages would gradually shift funds from areas with declining poverty rates to areas with increasing poverty rates, which could be viewed as beneficial if localities value stability of funding more than faster response to changing levels of need (see Chapter 6; see also Waksberg, Levine, and Kalton, 1999). Also, 5-year averages from the ACS could be preferable to the currently available SAIPE model-based estimates because the ACS estimates could likely be produced on a faster time schedule and so use more current data. For example, it should be possible in late 2010 to produce 5-year average estimates for counties from ACS data for 2005-2009. In contrast, it is likely that estimates released in late 2010 from the current county model would be based on 3-year average data from the CPS for 2006-2008 because of the lags in obtaining administrative data for the model (see Chapter 3).

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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timely basis. Thus, we conclude that some type of modeling must be used for most SAIPE estimates for the foreseeable future, which may involve using one or more or all of the available surveys—2000 census long form, ACS, March CPS, and SIPP.

Measurement Research

Although the 2000 census, ACS, CPS, and SIPP currently measure the same concepts of income and poverty, differences in their measurements can be expected due to the many differences in their design and operation. Detailed understanding of measurement differences is essential to determine the best ways to use the data from these surveys in the SAIPE Program. To date, only limited data and information are available for this purpose.

As part of a measurement research program, we urge the Census Bureau to conduct a planned exact match of the March 2000 CPS and the 2000 census long-form sample (exact CPS-census matches were performed for the 1950-1980 censuses). The Census Bureau should also conduct an exact match of the 1996 SIPP panel, for which the last year of interviews covers 1999 income, with the 2000 census.14 The Census Bureau should also carry out a planned set of aggregate comparisons between the 2000 ACS and the 2000 census. An exact ACS-census match for 2000 will not be possible because of a decision not to send long-form questionnaires to any of the ACS households in the sample around the time of the census, in order to minimize respondent burden and confusion between the two surveys. However, a planned exact match of the ACS with the census short-form may help evaluate within-household population coverage in the census and ACS and should be carried out.

Another useful set of comparisons would be exact matches of the 2000 census, 2000 ACS, 2000 March CPS, and 1996 SIPP with Internal Revenue Service (IRS) tax return records for 1999.15 Census-IRS, CPS-IRS, and SIPP-IRS matches have been performed in the past (see, e.g., Childers and Hogan, 1984; Coder, 1991, 1992; David et al., 1986). Such matches for income year 1999 could provide valuable information not only for comparing income reports among the household surveys as they relate to the IRS records, but also for assessing the performance of IRS

14  

A SIPP-census match might be restricted to SIPP rotation groups that were interviewed close to census day.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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data in small-area estimation models. One issue that could be addressed, for example, is the extent to which the IRS records cover the low-income population (see further discussion in Chapter 5). For matching purposes, it will be important to include 1999 tax returns that were filed late as well as returns that were filed on time.

The Census Bureau should explore ways to make the exact matches of census, IRS, and household survey data available to the research community–for example, by providing access to the files at the secure research centers that the Bureau has established in cooperation with several universities around the country. The availability of such files, with appropriate safeguards to protect the confidentiality of individual responses, would likely stimulate research on measurement error and modeling that would be beneficial to the SAIPE Program.

Role of the ACS
County Models

Careful evaluation of the strengths and weaknesses of the ACS, including in-depth comparisons with other surveys, will be needed to determine the best strategy for using ACS data for SAIPE estimates.16 For states, it appears possible to use direct estimates from the ACS, averaged over a year, once the survey has been fully implemented. For counties, our necessarily preliminary review of reliability, timing, and response quality issues suggests that two possible uses of ACS data merit serious consideration. Both uses, for which the Census Bureau should begin research and development now, involve indirect rather than direct estimation.

One approach is for the Census Bureau to continue to base county estimates on statistical models for which the March CPS estimates form the dependent variable and ACS estimates are used as one of the predictor variables, along with the other variables that are currently in the models. (For the school-age poverty model, these variables include IRS tax return data, food stamp data, census data, and population estimates.) For this purpose, the ACS estimates could be averaged over the same 3 years as the CPS estimates to make them consistent for the time period covered. This averaging would also reduce the sampling variability of the ACS estimates, which could improve the predictive power of the ACS variable in the models. It could also be possible to use ACS estimates for several years in a time-series or multivariate modeling approach (see Chapter 3).

15  

The Census Bureau obtains limited tax return information each year from the IRS, such as wages and salaries and adjusted gross income, for research and estimation purposes (see Chapter 5).

16  

See also National Research Council (2000b) for discussion of issues in using the ACS.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

Continuing to base the county models on the CPS, which is the official source of poverty statistics, could be advantageous because the CPS can be expected to have less bias in the measurement of annual income and poverty than the ACS. However, CPS-based models for income and poverty estimates for counties do have some limitations. Even when 3-year averages are used, the sampling variability of the CPS county estimates is high, so that very few counties receive a significant weight on the direct estimates when they are combined with the model estimates in the estimation procedure (see National Research Council, 2000c). Also, many counties are excluded from the modeling because they have no sample households (due to the clustered sample design), or, in the case of poverty estimates, no poor households (or no poor households with school-age children) in the sample. In contrast, the ACS uses an unclustered design with sample households in every county each month.

Hence, a second strategy to investigate is to construct statistical models for county income and poverty estimates in which the dependent variable is taken from the ACS estimates. An issue for evaluation is whether the dependent variable is best constructed as an annual average of 12 monthly samples centered on the calendar year (i.e., using months from July of year t to June of year t + 1) with appropriate inflation adjustments, or as an average of, say, 24 or 36 monthly samples centered on the calendar year. In either case, there would be less reliance on the models, compared with the CPS-based models, because ACS direct estimates would be available for all (or almost all) counties. The use of 2-year or 3-year average ACS estimates would place more weight on the direct estimates when they are combined with the model estimates than if average annual estimates, which have greater sampling variability, were used.17

Given the likely measurement biases for ACS income and poverty estimates, estimates from the ACS-based county models could perhaps be improved by calibrating them in some way to selected estimates from the March CPS. For example, counties could be grouped into broad categories on such dimensions as race, ethnicity, and geographic region, and raking factors could be developed that would achieve consistency between the ACS model-based estimates for each county group and the corresponding March CPS estimates. For this purpose, the CPS estimates could be based on weighted 3-year averages in order to reduce their sampling variability. Alternatively, calibration could be achieved by a bivariate model in which ACS and CPS estimates form the dependent variables in two linked equations (see Chapter 3).

17  

However, average annual estimates may have less bias than 2-year or 3-year average estimates.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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If a calibration procedure is adopted, it should then be applied to ACS estimates for states as well as counties, so as to achieve a consistent measurement standard for the direct state estimates and the model-based county estimates. The goal of any calibration procedure would be to reduce the mean square error of adjusted ACS estimates by taking advantage of the lower variance of the ACS data and the presumed lower bias of the March CPS data. If SIPP becomes the preferred source for national estimates of poverty (see “A Revised Poverty Measure,” below), there would be reason to calibrate the ACS estimates to the SIPP estimates and not to the March CPS estimates. Substantial research and development could be required to develop an appropriate calibration approach in either case.

School District Estimates

A possible role for the ACS (once it is fully implemented) that could improve the SAIPE estimates for school districts and other subcounty areas is to use ACS data to form within-county shares to apply to updated county poverty estimates.18 The advantage of this approach, in comparison with the current estimation procedure in which the most recent census data are used to develop within-county shares to apply updated county model estimates, is that the ACS estimates will be more current. Also, if the ACS estimates of shares are applied to estimates from an ACS-based county model, the two sets of estimates would reflect the same measurement standard.

However, the ACS estimates of shares will exhibit higher sampling variability than the census estimates of shares, particularly if the ACS estimates are averaged over, say, a 3-year rather than a 5-year period. For use in a shares model, statistical smoothing of the ACS estimates for subcounty areas within counties should be investigated to reduce their sampling variability (see Chapter 3).19 Another possibility for investigation is whether the ACS estimates could be combined in some way with 2000 census estimates to form within-county shares. If in the future it

18  

The shares would be each subcounty area's proportion of the total number of poor school-age children (or other population group) in the county.

19  

Whether smoothing county and subcounty estimates in order to reduce the mean square error of the latter would be successful with the ACS is not clear, given the sizable sampling variability of the ACS estimates for many counties. The other techniques suggested in Chapter 3 for reducing the variability of census long-form estimates for subcounty areas, which involve using short-form and long-form data in a simple or stratified ratio adjustment, are not applicable to the ACS. There is no ACS short form that is to be completed for all households.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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proves feasible to assign IRS tax return data to subcounty areas (see Chapter 5), then it might be possible to combine ACS estimates and IRS data for this purpose.

For the greatest improvement in subcounty estimates of income and poverty, it will likely be necessary to develop a statistical regression model for these areas that makes use of administrative data for predictor variables (see Chapter 5). However, development of appropriate administrative data is a long-range effort, so the Census Bureau should pursue the alternative of using ACS estimates, perhaps together with 2000 census estimates, in a within-county shares model.

ACS Funding

The ACS has the potential to play a major role in the SAIPE Program because of its large sample size and continuous operation. To do so, the ACS has to have consistent levels of sufficient funding over the next decade for the planned sample sizes. Reductions in funding would likely lead to reduced sample sizes and other discontinuities in the data that could jeopardize the usefulness of the ACS for SAIPE and make it difficult to evaluate how effective the ACS could be for SAIPE if carried out as now planned. More generally, if the ACS does not receive consistent funding, it will be difficult to properly assess its potential for small-area estimation for such important purposes as fund allocation and program evaluation.

Role of 2000 Census Long Form
Models

Estimates from the 1990 census long form are used-in somewhat different ways–as predictor variables in the current SAIPE state and county regression models, and these variables contribute importantly to the models (see National Research Council, 2000c:Ch.6). It makes sense to plan for a similar role for estimates from the 2000 census long form and perhaps to include predictor variables from both the 1990 and 2000 censuses for a time.20

Long-form census estimates may prove to be less effective predictors in the models for some years than others because of different economic

20  

Planning to use the 2000 census as a predictor variable in models for states as well as counties is necessary, given that it will not be feasible to use ACS direct state estimates at least until data are available for income reference year 2003 and the quality of the data has been determined.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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conditions. Economic changes may occur immediately following a census as well as later in a decade. This problem is naturally handled in a modeling framework, given that the model is refitted for each estimation year.

For smaller areas, such as school districts and other subcounty areas, it is likely that income and poverty estimates from the 2000 census will exhibit high sampling variability even with oversampling of small governmental units and the use of the most effective procedures to reduce variance. The census estimates will also be available for only one year. However, it will be necessary to use the 2000 long-form estimates to form within-county shares to apply to updated county estimates until it becomes possible to use the ACS for this purpose or until it becomes possible to develop a subcounty model similar to the state and county models. The development of such a model depends on obtaining appropriate subcounty administrative records data. If such data can be developed for use in a subcounty model, then the 2000 census estimates are a likely candidate to serve as one of the predictor variables. The sampling variability in the census estimates would weaken the predictive power of the census variable, but the model would produce unbiased predictions.

Direct Estimates for 1999

While it clearly makes sense to plan to use 2000 long-form estimates as predictor variables in SAIPE state and county regression models and, for the time being, in a county shares model for school districts and other subcounty areas, it is far from clear what use, if any, to make of the direct long-form estimates for income year 1999. On the one hand, direct estimates will be reliable for states and many counties, and they will have considerable face validity for users, so that not to use these estimates for income year 1999 seems problematic. However, their use would likely produce anomalies in the time series of estimates because the standard of measurement provided by the census direct estimates would not likely be the same as that underlying the estimates produced for prior years from the SAIPE CPS-based models nor that underlying the estimates produced for subsequent years from another model (e.g., one based on the ACS or CPS or either of these two surveys adjusted to SIPP controls). Moreover, the census estimates for 1999 will not be reliable for many counties and most subcounty areas, and they may not be available in time to meet the Census Bureau 's SAIPE schedule, which calls for 1999 estimates to be delivered to users by fall 2002 (although the census estimates could be used to produce revised 1999 SAIPE estimates when they become available).

We do not believe there is a clearly preferred answer for whether and

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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how to use 2000 long-form direct estimates for SAIPE for income year 1999. We urge the Census Bureau to consider several options, which include: using the direct long-form estimates (either for release on the SAIPE schedule or for later release); using the long-form estimates with a ratio adjustment to short-form data to reduce the sampling variability of the estimates (see Chapter 3); using the long-form estimates with a calibration to CPS aggregate estimates; not using the direct long-form estimates but, instead, using the current SAIPE models to produce indirect estimates for income year 1999. The Bureau should convene a meeting of key users to discuss these options so that the basis for the Bureau's decision is well understood.

A Revised Poverty Measure

U.S. poverty statistics for the total population and population groups are currently based on a measure in which annual before-tax money income for a family or unrelated individual is estimated from the March CPS and compared with the applicable poverty threshold for the family size. A report of the National Research Council (1995a) concluded that the current measure is not adequate to inform public policy and recommended that it be replaced with a revised measure, in which disposable after-tax money and near-money income would be estimated from SIPP and compared with an appropriate poverty threshold (see also Betson, Citro, and Michael, 2000). An earlier National Research Council report (1993) also recommended that SIPP become the basis for measuring poverty.

The revised poverty measure would differ from the current measure in how the thresholds are developed, updated, and adjusted for different size families and areas of the country. The revised measure would also differ in how family resources are measured from survey data. Starting with gross money income, as in the current measure, the revised measure would add the value of near-money in-kind benefits (e.g., food stamps, subsidized housing, school lunch, energy assistance), and subtract the following items: payroll taxes; net federal and state income taxes (for some recipients of the earned income tax credit, a positive amount would be added to income); expenses necessary for work, including work-related transportation and child care costs; child support payments to another family; and out-of-pocket medical expenditures.

Whether some or all of the recommendations in the 1995 report will be adopted is not known at this time. A report of the U.S. Census Bureau (1999c) illustrated the use of the revised poverty measure with March CPS data for 1990-1997, and the Bureau plans to regularly release revised estimates (labeled “experimental”) on its Internet web site at the same time

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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that the official estimates are released each fall. The Bureau is also working to make it possible to implement a revised measure with SIPP by adding some questions and seeking funding to revise the design so that a new panel is introduced each year, which could be used to equalize the bias due to sample attrition across years. Additional funding is also being sought for expanded sample size to support direct estimates for the largest states.

Using a revised official poverty measure in SAIPE would mean changing the measurement standard to a measure that is more appropriate for policy purposes–because it takes account of taxes and in-kind transfer programs and other family circumstances that are not reflected in the current measure. Such a change raises issues of implementation.

The 2000 census does not ask questions on in-kind benefits or nondiscretionary expenses (e.g., work expenses) that would be needed to calculate family resources under the revised measure. The ACS includes questions on in-kind benefits (food stamps, energy assistance, school lunch, and subsidized housing), but not on nondiscretionary expenses, and it is unlikely that the questionnaire could be expanded to provide all of the elements of the revised definition. In contrast, the revised definition of disposable money and near-money income can fairly readily be calculated from either the March CPS or the SIPP, although imputations for some kinds of expenses needed to calculate disposable money and near-money income are required (more so in CPS than in SIPP).

The limitations of the 2000 census and ACS income data constrain but do not preclude the use of these sources in estimating poverty for small areas with a revised measure. Direct estimates could not be obtained from either the 2000 census or the ACS that fully implemented a revised measure. However, if the CPS remains the official source of poverty statistics with a revised measure, then 2000 census or ACS estimates that are based on the current measure could be used as predictor variables in CPS-based regression models that use a revised measure. Poverty estimates reflecting the current measure from the 2000 census or the ACS could also be used to form within-county shares for subcounty areas to apply to updated county poverty estimates developed from CPS-based models for which the dependent variable reflected a revised measure.21 Finally, if ACS estimates are calibrated in some manner to March CPS estimates of poverty developed with a revised measure, then the calibrated ACS estimates could be used as a dependent variable in regression

21  

This use of census long-form or ACS estimates of shares would require the assumption that the distribution of poverty within counties is similar under the current and revised poverty measures.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

models. If SIPP replaces the CPS as the official source of poverty statistics, then such calibrations should be implemented with that survey instead of the CPS.

Conclusions and Recommendations

4-1 The Census Bureau's Small Area Income and Poverty Estimates (SAIPE) Program must continue to rely primarily on models for updated income and poverty estimates for small areas. None of the existing or planned surveys can produce direct estimates of sufficient reliability, timeliness, and quality to provide all of the SAIPE income and poverty estimates.

4-2 To inform decisions about the use of the 2000 census long form, American Community Survey, CPS March Income Supplement, and Survey of Income and Program Participation for SAIPE, the Census Bureau should conduct research to understand and document the differences in their measurement of income and poverty. For this purpose, the Census Bureau should conduct a series of exact matches and analyses:

  • the planned exact match of the March 2000 CPS and the 2000 census long form;

  • an exact match of interviews from the 1996 SIPP panel covering 1999 income and the 2000 census long form;

  • the planned set of aggregate comparisons of income and poverty estimates from the 2000 ACS and the 2000 census long form;

  • an exact match of the 2000 ACS and the 2000 census short form to examine differences in measurement of household composition and demographic characteristics that relate to income and poverty; and

  • exact matches of Internal Revenue Service tax returns for income year 1999 with the 2000 census long form, 2000 ACS, March 2000 CPS, and 1996 SIPP.

4-3 Research and development by the Census Bureau should begin now to explore two possible uses of ACS estimates in SAIPE models for counties: to form one of the predictor variables in statistical models for which the March CPS continues to provide the dependent variable and to serve as the dependent variable in county models. For the latter use, the ACS estimates might possibly be calibrated in some way to selected estimates from the March CPS.

4-4 The Census Bureau should conduct research on using ACS estimates for school districts and other subcounty areas, possibly combined

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
×

with 2000 census estimates, to form within-county shares or proportions to apply to updated county model poverty estimates.

4-5 If the ACS is to fulfill its potential to play a major role in the SAIPE Program, it is important that the survey have sufficient funding for planned sample sizes over the next decade. Reductions in funding could jeopardize its usefulness for SAIPE and, more generally, make it difficult to properly assess the potential uses of ACS data in small-area estimation.

4-6 The Census Bureau should plan to use 2000 census long-form estimates to form one of the predictor variables in the SAIPE state and county models.

4-7 For SAIPE estimates for income year 1999, it may be possible to use the direct estimates from the 2000 census long form, but whether this is feasible or desirable is not clear. The Census Bureau should consider the available options and discuss them fully with users.

4-8 If the recommendations of the National Research Council for changes in the official measure of poverty are adopted, the Census Bureau will need to consider the implications for the SAIPE Program. In particular, it may become feasible and desirable to use estimates from the Survey of Income and Program Participation for calibration purposes.

Suggested Citation:"4 Future Model Develpment: The Role of Surveys." National Research Council. 2000. Small-Area Income and Poverty Estimates: Priorities for 2000 and Beyond. Washington, DC: The National Academies Press. doi: 10.17226/9957.
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×
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×
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Recent trends in federal policies for social and economic programs have increased the demand for timely, accurate estimates of income and poverty for states, counties, and even smaller areas. Every year more than $130 billion in federal funds is allocated to states and localities through formulas that use such estimates. These funds support a wide range of programs that include child care, community development, education, job training, nutrition, and public health.

A new program of the U.S. Census Bureau is now providing more timely estimates for these programs than those from the decennial census, which have been used for many years. These new estimates are being used to allocate more than $7 billion annually to school districts, through the Title I program that supports educationally disadvantaged children.

But are these estimates as accurate as possible given the available data? How can the statistical models and data that are used to develop the estimates be improved? What should policy makers consider in selecting particular estimates? This new book from the National Research Council provides guidance for improving the Census Bureau's program and for policy makers who use such estimates for allocating funds.

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