A Preliminary Evaluation of the Residence History Data in the 2010 

and 2011 Field Tests of the Reengineered Survey of Income and 

Program Participation 

Matthew Marlay and Peter Mateyka 

U.S. Census Bureau 

matthew.c.marlay@census.gov

peter.mateyka@census.gov

Abstract 

The Survey of Income and Program Participation (SIPP) collects longitudinal data on a variety of topics including 

employment and program participation. The strength of SIPP is that respondents are followed for two to four year 

periods, even when they change residences, enabling researchers to perform analyses not possible with crosssectional Census surveys. The longitudinal design of SIPP means collecting accurate residence history information 

is vital to the success of the survey. 

The Bureau is redesigning the SIPP survey instrument, and this redesign will have implications for how the survey 

collects residence history information. The current in production SIPP has a respondent recall period of four months 

and respondents are only allowed to report one move during each four month interview period, or wave. Detailed 

information about previous residences is collected early in the survey (Wave 2), but this information is not 

concurrent with data collected on respondents in later waves. The reengineered SIPP instrument uses an Event 

History Calendar (EHC) format with a respondent recall period of one year. Residence history information for the 

year is collected concurrently with data on jobs, program participation, and health insurance. Respondents can report 

living in up to five residences during the reference year of the survey, and detailed address information is collected 

for each residence reported. 

We analyze residence history data collected in the 2010 and 2011 field tests of the Re-Engineered Survey of Income 

and Program Participation (SIPP-EHC). We use two methods to evaluate the quality of the SIPP-EHC residence 

history data. First, we match the addresses collected in the 2011 field test of SIPP-EHC to spatial extracts from the 

Census Bureau's Master Address File/TIGER database. We report the percentage of addresses successfully matched 

at the place, county, and state level. Second, we compare the monthly mover rates reported in the test 2011 SIPPEHC (covering calendar year 2010) to the 2010 SIPP-EHC (covering calendar year 2009) to the 2008 SIPP 

(covering calendar year 2009). We primarily focus on whether there are time trends in the likelihood of respondents 

reporting moving in the two SIPP-EHC field tests, indicating memory decay or misreporting. 

Introduction 

The Census Bureau is currently reengineering the Survey of Income and Program Participation (SIPP). Like the 

current SIPP (called production SIPP), the primary objective of the Re-Engineered SIPP (called SIPP-EHC) is to 

collect accurate and comprehensive information on topics such as income, program participation, employment, and 

health insurance, with the additional goal of collecting this information in a more cost effective and less burdensome 

way. These improvements are achieved by shifting the four-month recall period of current production SIPP to a oneyear reference period and introducing an event history calendar (EHC) to aid respondent recall of retrospective 

history information. 

The purpose of this paper is to compare residence history data collected in the 2010 and 2011 SIPP-EHC field tests 

to data from the 2008 production SIPP. The collection of residence history information underwent several 

significant changes in SIPP-EHC. Most significantly, in production SIPP respondents are only allowed to report one 

move per four-month reference period. This move is, presumably, to the current residence. In SIPP-EHC residence 

history data is collected in the EHC, where the starting month and ending month (spells) of events are recorded on a 

visual calendar to aid respondent recall. Respondents are asked to report up to five residences they lived in during 

1 
the reference year. For each residence, respondents are asked to provide detailed address information. This 

represents a significant change from production SIPP, because identifying movers in SIPP-EHC will rest primarily 

on respondent reports, and respondents may be asked to recall address information for a previous residence. The 

latter is a particularly significant change, as it represents a shift from getting prospective address information to 

retrospective address information. Both of these changes increase the likelihood of respondent error in reporting 

residence history information. 

In this paper we evaluate the frequency of reported residences and the accuracy of reported address information in 

the SIPP-EHC field tests compared to the 2008 production SIPP. We assess the accuracy of the detailed address 

information in SIPP-EHC by matching the reported addresses to a national address database, and we assess the 

frequency of reported residences by comparing the number of moves reported in SIPP-EHC to a matched time 

period and geography sample from the 2008 production SIPP. 

The first section of our paper describes production SIPP and SIPP-EHC and how each survey collects residence 

history information. In the second section, we focus on the quality of reported address data in the 2011 SIPP-EHC 

field test. Next, we compare mover rates in the two SIPP-EHC field tests to the 2008 SIPP sample. The final section 

briefly discusses future research. 

Residence History Information in Production SIPP 

The production SIPP is a longitudinal survey that is nationally representative of the noninstitutionalized U.S. 

population, with an initial sample size of about 50,000 households. SIPP panels generally last between two and four 

years, with interviews conducted every four months (called waves). Production SIPP sample members are divided 

into four roughly equal groups, called rotation groups. Each month, one rotation group is interviewed about the 

previous four-month reference period, meaning that the calendar months of the reference period will not completely 

overlap for any two rotation groups within a given production SIPP wave.

1

 Interviews include both core questions, 

asked in each wave, and topical module items, asked only in specific waves. 

Residence history data in production SIPP are collected both in the Migration History topical module, asked during 

the second wave of the 2008 panel, and as part of the core data collected every wave. The Migration History topical 

module collects data on the year and month in which a respondent moved into his or her current residence, the 

month and year moved into the previous residence, the tenure status of both current and previous residences, and 

information on place of birth and nativity status. This topical module is asked only during the second wave of a 

panel and is limited to respondents who are age 15 and over. 

Each wave production SIPP follows original sample members 15 years of age or older when they change addresses, 

as well as children who continue to live with an original sample member. If respondents move, field representatives 

rely on several methods to locate them. After the initial interview with a household, field representatives ask for the 

name and contact information of someone who can provide the new address in the event that an entire household 

moves. Field representatives may also contact neighbors, employers, mail carriers, real estate companies, rental 

agents, or postal supervisors to locate original sample members who have moved. In the event that a respondent has 

moved over 100 miles away from his original residence, the field representative will attempt to contact the 

respondent by phone. Respondents who cannot be located are dropped from the survey. 

Production SIPP only collects data on one move per person per four-month reference period. If a respondent has 

moved multiple times during the reference period, address information is only collected for the most recent move. 

This reduces the burden on respondents to report detailed address information about previous residences, but it also 

may lead to an underestimation of the true mover rate across a production SIPP panel. Residence history information 

is contained in two variables: (1) an address ID variable, which indicates the month a respondent changes 

residences, and (2) a mover flag, which indicates whether the respondent moved in a given month and, if so, the 

distance of the move (intracounty, intercounty same state, interstate). 

 

1

 Appendix table A.1 graphically displays the rotation groups, waves, and reference months for the 2008 SIPP panel. 

2 
Residence History Information in SIPP-EHC

When fully implemented in 2014, the SIPP-EHC will be a nationally representative sample of the U.S. 

noninstitutionalized population with four waves of data, spanning four calendar years. The 2010 and 2011 SIPPEHC field tests consist of smaller samples drawn from self-representing primary sampling units (PSUs) and highpoverty geographies within the production SIPP sampling frame. The 2010 field test initial sample included 7,982 

households, of which 5,185 yielded complete interviews. The sample consisted mainly of large cities in California, 

New York, and Texas, and, not surprisingly, 41.0 percent of the total sample lived in these three states. The sample 

predominantly consists of younger individuals (median age of 30), renters (67.0 percent), and Hispanics (51.0 

percent), further suggesting that the sample households are not representative of the U.S. population.2

 The 2011 field 

test initial sample included 4,055 households, of which 2,596 yielded complete interviews. The 2011 sample, while 

slightly more geographically diverse than the 2010 sample, is again not nationally representative with a median age 

of 30, 64.5 percent renter, and 44 percent Hispanic.3

In SIPP-EHC, the event history calendar (EHC) is located in the middle of the instrument, after the demographics 

section. The EHC collects data on residence history, marital history, education, employment, program participation, 

and health insurance. To aid respondent recall, the calendar is structured to visually display timelines on the same 

screen for each of these domains. The calendar for SIPP-EHC has a one-year reference period with months as the 

unit of analysis. For each domain, respondents report beginning and end month spells. These spells are then visually 

displayed in the EHC, allowing the field representative to easily identify events that coincide, such as the start of a 

new job and a change of residence and to use these events to help respondents remember subsequent events. In 

addition to visually displaying spell lengths, the survey also asks about landmark events that may have occurred 

during the year (such as a birth, a marriage, or anything else that may stand out as a significant event in the 

respondent’s life). 

Within each domain spell, the instrument asks additional questions of each respondent. For example, full address 

information is collected on each residence where the respondent lived at least one month during the reference year, 

along with type of living quarters, tenure status (own/rent), receipt of rent subsidy, receipt of housing voucher, and 

the respondent reported reason for moving to the residence.4

 Up to five addresses may be reported in the EHC. Since 

many residence spells will be left-censored by the one-year reference period, respondents are also asked for the year 

and month they moved to the address that they lived in during the first month (January) of the reference year. 

There are a number of advantages to the collection of residence history in the SIPP-EHC. First, the universe is 

broader. In production SIPP the Migration History topical module only goes to respondents who are at least 15 years 

of age, in SIPP-EHC all respondents get the questions. Additionally, respondents are asked to report up to five 

addresses in the EHC, rather than the production SIPP method of only allowing one move per four-month reference 

period. In production SIPP, a large number of respondents have left the sample before the Wave 2 Migration History 

topical module. This sample attrition combined with production SIPP’s current method of using rotation groups, 

makes calculating yearly or seasonal migration rates difficult. With a year-long reference period, calculating annual 

and seasonal migration rates will be significantly easier in SIPP-EHC. 

While there are clear advantages to collecting residency data in the EHC, this shift in data collection method raises a 

number of questions about the accuracy of the residence history information collected in SIPP-EHC. Collecting 

residence information will be more dependent on receiving accurate address information from respondents. The 

current production SIPP method of only allowing one move per reference period ensures that address information 

reported by respondents will likely be for a current address. In SIPP-EHC respondents will be asked to report 

detailed address information about previous residences. Additionally, with a yearlong reference period, it is probable 

 

2

 The same numbers for the January, 2009 full SIPP sample are 31.4 percent renter, 15.7 percent Hispanic, and a 

median age of 36. 

3

 The same numbers for the January, 2010 full SIPP sample are 32.1 percent renter, 16.0 percent Hispanic, and a 

median age of 36. 

4

 The respondent is asked to report all residence spells during the reference year that lasted at least the majority of 

one month. These residence spells can include time in group quarters, medical institutions, emergency or transitional 

shelters, unoccupied tent or trailer sites, and cars or vans. 

3 
that some respondents may be asked to provide proxy address information for people with whom they did not live 

with for the entire reference period. 

Research Goals 

Based on the differences between the collection of residence history data in production SIPP and SIPP-EHC, we 

have identified two research questions: (1) When respondents in SIPP-EHC report detailed previous residence 

information, do they provide usable information? (2) Are there differences in the number of reported moves in 

production SIPP and the SIPP-EHC? To test the first research question, we match the addresses collected in the 

2011 SIPP-EHC field test to a national address database. We report the percentage of addresses successfully 

matched at the block, place, county, and state level. To test the second research question, we compare the monthly 

mover rates reported in the 2010 SIPP-EHC (covering calendar year 2009), the 2011 SIPP-EHC (covering calendar 

year 2010), and the 2008 production SIPP (covering calendar years 2009 and 2010). We primarily focus on whether 

there are time trends in the likelihood of respondents to report moving in the two SIPP-EHC field tests, indicating 

memory decay or misreporting. We conclude by describing the 2012 SIPP-EHC field test and discussing future 

research projects. 

Quality of Address Information in SIPP-EHC 

To evaluate the quality of the detailed address information provided by respondents in SIPP-EHC, we use ArcGIS, a 

powerful geographic information software package, to spatially match the respondents’ address information to street 

addresses and Census boundary information. The boundary information comes from the 2010 Census Topologically 

Integrated Geographic Encoding and Referencing (TIGER)/Line files, a database that includes spatial extracts from 

the Census Bureau's Master Address File/TIGER database, containing features such as roads, railroads, rivers, as 

well as legal and statistical geographic areas. These shapefiles are designed specifically for use with geographic 

information software such as ArcGIS. 

We decided to use ArcGIS mainly because of time constraints. Normally, addresses from SIPP are geocoded down 

to the block level using the Census Bureau’s proprietary autocoding software, which matches addresses using the 

same TIGER/Line files. Eventually, this software will also be used to geocode addresses from the production SIPPEHC. 

Geocoding software includes tolerance settings that allow for user control over sensitivity to spelling and number 

agreement in reported addresses. Matches do not have to be exact because the coding algorithm in this software 

allows for equivocations, such as using sound-alike values of letters (for example, m=n, f=ph, etc.) and reversing 

consecutive letter combinations (ie=ei). Each equivocation is assigned a numeric value representing a confidence 

level in the match, with exact matches receiving the best score (usually 100). The responses have to match reference 

file entries (i.e. TIGER/Line files) with a relatively high level of confidence in order for an automated computer 

match to be accepted. Geocoding software further provides the user with the ability to match addresses manually 

that do not fit the criteria for an automated computer match. This process, called clerical coding, is standard practice 

for large Census Bureau Surveys like SIPP, and the American Community Survey (ACS). Because of differences in 

tolerances settings and coding algorithm between ArcGIS and the Census autocoder, our results may not fully 

approximate the results from the Census autocoder. Our intent is to eventually compare SIPP-EHC address match 

rates from ArcGIS to those of the Census autocoder to evaluate the quality of geocoded addresses across the two 

systems. 

Table 1 shows the match rates for addresses from the 2011 SIPP-EHC, both overall and for SCIF (Survey Control 

Input File) and non-SCIF addresses.5

 SCIF addresses are original sample addresses, while non-SCIF addresses are 

respondent-provided addresses for previous residences. We created an address file by pulling all unique addresses 

provided by all households in the 2011 SIPP-EHC who had completed interviews. We removed from the file any 

address whose information was totally blank; we included any address that had at least one piece of address 

information present, even if it was only the country (the instrument does not ask for detailed address information for 

 

5

 The SCIF is the file with the original interview address for each case. 

4 
any address outside of the United States and Puerto Rico). 6 After these deletions, our working address file contained 

3,295 addresses, of which we were able to match about 91 percent (3,008) to a street address in the TIGER/Line 

reference files. About 79 percent of addresses (2,589) were exact matches with no corrections needed. Another 11 

percent were matchable after making minor corrections, such as adding street directional designations (e.g., W, E) or 

correcting street suffixes (e.g., substituting “Drive” for “Road”). For the remaining 1.7 percent of matched 

addresses, we were able to identify them using external data such as the United States Postal Service webpage or 

Google Maps. 

We were not able to match 8.7 percent (287) of addresses to the street level. However, this does not mean that we 

do not know anything about them: Some addresses are matched to the street with no street number (2.1 percent, 70), 

while others matched at the city/county level (3.8 percent, 126). Additionally, 35 addresses (1.1 percent) are outside 

of the U.S., so the instrument did not ask for the detailed information for those instances.7

 Only 1.4 percent of 

addresses (46) contain unusable data with incorrect street names, and 0.3 percent (10) only include state or country 

codes. The matches are determined both by automatic or computer coding (89.5 percent) and by computer-assisted 

clerical coding (10.5 percent).8

 

The majority of the addresses are derived from the sampling frame (2,579) and not reported by respondents (SCIF).9

As expected, the match rate for original sample addresses (99.9 percent) is extremely high. Of these addresses, 93.0 

percent are exact matches, 5.3 percent are matched through minor corrections, and 1.6 percent through investigation. 

Of the two unmatched addresses, one has an invalid street address, the other only a post office box. 

The respondent provided addresses (non-SCIF) in the final column of Table 1 represent the retrospective address 

information collected in the EHC. Of these 716 addresses, we were able to successfully match 431 (60.2 percent) of 

them. However, only about one quarter (26.5 percent) are exact matches, while 31.4 percent matched with minor 

corrections and 2.2 percent matched after more detailed investigation. Even though nearly 40.0 percent of these 

addresses cannot be matched at the street level, most of them can be matched to the street with no street number (9.8 

percent) or at the city/county level (17.5 percent). An additional 4.9 percent are out of the country. Addresses with 

unusable data include 6.3 percent with incorrect street names and 1.4 percent with only a state or country code. 

Table 2 includes the Census Bureau’s block-level autocoding results for movers from waves 2 through 8 of the 2008 

production SIPP. These data are presented for comparison purposes and are most comparable to the respondentprovided address information in Table 1. However, we recommend caution when interpreting these results. First, the 

results are produced using the Census Bureau’s autocoder rather than ArcGIS, so apparent differences in the results 

may be due to the method of geocoding used. Second, because production SIPP only allows one move per reference 

period, these addresses are for current residences at the time of interview, rather than previous addresses as reported 

in SIPP-EHC, so we would expect them to be of generally higher quality. Finally, the data in Table 1 are coded to 

the street level rather than the block level, and data unmatched at the street level are matched to street with no

number, city, and county, but not block. 

Overall, the percentage of addresses successfully coded at the block level range between 89.5 percent and 91.7 

percent across waves 2 and 8.10 Many of these addresses are matched using automated (computer) coding (78.7-84.2 

percent), with the remaining matched using clerical coding. The autocoding results from production SIPP appear to 

yield a greater proportion successfully coded addresses. However, this is somewhat expected given SIPP-EHC’s 

greater reliance on respondent-reported previous addresses. Overall, about 87.5 percent of the respondent reported 

 

6

To protect respondent confidentiality and to prevent any inadvertent disclosure of information, before geocoding 

the addresses we removed all personally identifying information that might tie the addresses back to specific 

respondents. Thus, our file consisted only of a list of fully anonymized addresses. 

7

 This number is higher than the 0.4 percent of respondents in the 2009 ACS that reported living in a foreign country 

on a question about their residence one year ago. 

8

 Automatic coding includes addresses that were not matched and could not be subsequently matched through 

clerical coding. 

9

 17.6 percent of respondents in the 2011-SIPP EHC reported moving at least one time during the reference year. 

10 The wavely totals in Table 2 include only addresses with at least two digits of address information, such as a state 

code. 

5 
addresses in Table 1 can be coded to at least the county level (matched to street, matched to street with no number, 

matched to city, and matched to county). This suggests that respondents in SIPP-EHC generally reported usable 

address information for previous residences. 

Table 3 further breaks down the coding results from Table 1 by state. 801 of the 3,295 addresses are from California. 

New York is second with 454 addresses, Texas third with 376, and Florida fourth with 289. Focusing on total 

addresses, Georgia (85.4 percent) has the lowest overall match rate, while New Jersey (98.2 percent) has the highest 

rate. However, the overall match rates are heavily influenced by the percentage of respondent reported (non-SCIF) 

addresses. For example, only 60.4 percent of the addresses from Arizona are original sample addresses, the lowest of 

any state, compared to 88.0 percent in Maryland. Focusing only on the four states with the most respondent-reported 

addresses – California, New York, Texas, and Florida – there is some variation in the percentage of respondentreported addresses that we successfully matched. About half of respondent-reported addresses in California (48.7 

percent) are unmatched, compared to fewer than 40 percent in New York (34.4 percent), Texas (31.8 percent), and 

Florida (32.6 percent). 

These results show that the quality of the reported address data is of high quality in both the production SIPP and the

SIPP-EHC. It is clear that the SIPP-EHC data leads to fewer automatic matches, but that is not unexpected given 

the retrospective nature of the survey and its reliance on the respondent to recall very specific pieces of 

information.11

Mover Rates in SIPP and SIPP-EHC 

In this section we compare mover rates in the 2010 and 2011 SIPP-EHC test data to those from the 2008 production 

SIPP. Because of differences in how movers are tracked across the two surveys, sample attrition, and sample 

limitations in SIPP-EHC, we did a number of things to make the two data sources as comparable as possible. First, 

we matched the data to time period. Specifically, we compare the 2010 SIPP-EHC mover rates (covering the 2009 

calendar year) to a 2009 calendar year file from the Waves 2-5 of the 2008 production SIPP. Likewise, we compare 

the 2011 SIPP-EHC data to a 2010 calendar year file from Waves 5-8. Second, we present the production SIPP 

mover rates for both the overall sample and a sample matched to the SIPP-EHC sample geography to account for the 

lack of geographic diversity in SIPP-EHC. 12 Finally, only moves in which the respondent remained in sample are 

included in the production SIPP mover estimates. This is because it is not always possible to identify the month of 

move for respondents who move and cannot subsequently be located in the production SIPP sample, making 

comparisons to SIPP-EHC difficult. Consequently, our production SIPP mover estimates do not represent the total 

number of movers in production SIPP, but the number of movers that remain in universe and are successfully 

located by field representatives. 

Preliminary weights for the 2010 and 2011 SIPP-EHC were created by the Demographic Surveys Division. The 

SIPP-EHC weights adjust the sample to correspond to the weighted 2008 production SIPP matched geography 

samples. More specifically, the 2010 SIPP-EHC weights are based on the January, 2009 production SIPP matched 

sample population. The 2011 SIPP-EHC weights are based on the January, 2010 production SIPP matched sample 

population. For example, the weighted population for the geography matched production SIPP sample for January, 

2010 is 29,931,714 and the weighted population total for the 2011 SIPP-EHC sample from January is 29,832,882. 

However, the weighting does not completely mitigate the effects of sample attrition in the production SIPP. Because 

we use the January weight, our production SIPP mover estimates for later calendar months are subject to the effect 

of sample attrition. The two SIPP-EHC field tests are essentially Wave 1 interviews, so there is no sample attrition 

 

11 Another consideration is the use of edited data. All of the results presented from SIPP-EHC come from unedited 

data. However, we have written edits for the residence history section that should improve the quality of the data

and therefore lead to an even higher match rate. For instance, if one person in a household provides an incomplete 

address, but another household member provides a full address (and the two lived together during the relevant 

period), then we will replace the incomplete information with the complete information, allowing us to match an 

address that was previously unmatchable. 

12 In analyses not shown, we compared the two SIPP-EHC samples to the matched time period and geography SIPP 

samples on tenure, race, Hispanic origin, language (speak non-English in the home) and age and found the matched 

SIPP samples to be highly comparable to the SIPP-EHC field tests. For example, 66.7 percent of respondents in the 

SIPP 2010 matched sample are renters, compared to 67.0 percent in the 2010 SIPP-EHC. 

6 
across the one-year reference period. For production SIPP, there is significant sample loss across the waves needed 

to construct the 2009 and 2010 calendar year files. For example, by Wave 8, 38 percent of original sample 

households have left the production SIPP sample. Furthermore, by Wave 8, about 4.5 percent of original sample 

households are lost because they cannot be located after a move.13

 

To partially account for sample attrition, we calculate mover estimates for the entire calendar year and by calendar 

month. The monthly mover rates are calculated based on the number of respondents in sample in the month of 

interest, somewhat reducing the impact of sample attrition in production SIPP. Despite the impact of sample loss on

comparability across the two surveys, this issue also highlights the strength of SIPP-EHC data. One calendar year of 

data is collected in SIPP-EHC before normal sample attrition that inevitably occurs in follow-up interviews. 

Table 4 describes the overall mover rates for the weighted 2010 SIPP-EHC and matched production SIPP samples. 

We present production SIPP numbers for both the full sample and 2010 SIPP-EHC geography matched sample. The 

mover estimates from the production SIPP samples are for the 2009 calendar year, matching the reference period of 

the 2010 SIPP-EHC. Again, please interpret these numbers with some caution due to sample attrition across the 

production SIPP surveys. 14 The percentage of the 2010 SIPP-EHC sample that reported moving in 2009 is 14.7 

percent. 15 This number is substantially greater than the 9.2 percent of respondents in the full production SIPP 

sample, and 10.3 percent in the production SIPP matched geography sample, who moved and were subsequently 

successfully located. Respondents in the 2010 SIPP-EHC also include a greater number of repeat movers than in the 

2008 production SIPP. About 1.7 percent of all respondents in the 2010 SIPP-EHC report multiple moves during 

2009, compared to only 0.9 percent of respondents in production SIPP and 0.7 percent in the matched production 

SIPP sample. 

In Table 5, we present the results for the 2011 SIPP-EHC compared to 2008 production SIPP. 17.6 percent of 

respondents in the 2011 SIPP-EHC report moving in 2010, quite a bit higher than the 8.8 percent of respondents in 

the full sample and 10.9 percent in the matched production SIPP sample who moved and were successfully located. 

Similar to the results from the 2010-SIPP EHC, the number of respondents who report multiple moves in the 2011 

SIPP-EHC (2.1 percent) is greater than the number of multiple movers identified in the 2010 calendar year of the 

2008 production SIPP (1.2 percent). 

Figure 1 shows mover trends by month for calendar year 2009. The figure shows three lines: (1) the 2008 SIPP, (2) 

the matched geographic subset from the 2008 SIPP, and (3) the 2010 SIPP-EHC.

16 Given the results from the 

previous section, we expect the monthly mover proportions to be greater in SIPP-EHC than in production SIPP. 

Additionally, differences between the surveys should be fairly uniform across months, regardless of seasonal move 

 

13 For the 2009 and 2010 SIPP calendar year files, we investigated the extent to which sample attrition altered the 

composition of the full and matched samples. We found that, for both calendar years, and both the full and matched 

samples, the sample composition at the end of the year tended to be slightly older and more likely to be homeowners 

compared to earlier months. This suggests that the SIPP samples lose younger respondents and/or renters, two 

groups that move frequently. 

14 The denominator for the 2008 SIPP mover estimates includes all respondents who were in sample for at least one 

month during the given calendar year. Because of sample attrition, this estimate inevitably underestimates the true 

mover rate across the sample. For example, 101,118 respondents in SIPP were in universe for at least one month 

between February and December of 2009. However, the most respondents in universe for a given calendar month 

during this time was 97,045 in February, and the lowest was 90,393 in December. 

15 The estimates in this report (which may be shown in text, figures, or tables) are based on responses from a sample 

of the population and may differ from the actual values because of sampling variability or other factors. As a result, 

apparent differences between the estimates for two or more groups may not be statistically significant. All 

comparative statements have undergone statistical testing and are significant at the 90 percent confidence level 

unless otherwise noted. For further information on statistical standards and the computation and use of standard 

errors, go to http://www.census.gov/sipp/source.html or contact Tracy Mattingly of the Census Bureau’s 

Demographic Statistical Methods Division on the internet at Tracy.l.Mattingly@census.gov. 

16 A tabular version of this figure is included as Appendix Table A.3. 

7 
patterns.17 The results suggest the differences between the proportions of movers by month between the SIPP-EHC 

sample and the 2009 calendar year production SIPP matched file are greater at later calendar months. For example, 

the percent of the 2010 SIPP-EHC sample that reported moving in the month of February (1.0 percent) is not 

statistically different from the 1.2 percent of the 2009 production SIPP calendar year matched geography sample that 

reported moving in February. However, by December, 1.8 percent of the 2010 SIPP-EHC sample reports a move, 

compared to 0.9 percent of the production SIPP 2009 calendar year matched sample. Moreover, there is limited 

evidence of a seasonal move trend in the 2010 SIPP-EHC data. While the reported number of moves appears to 

increase in the summer months, the proportion of movers remains high in November and December, where the 

monthly proportion of movers declines in the two production SIPP samples in these months. This pattern of high 

mobility in November, and December in the 2010 SIPP-EHC and relatively low mobility in earlier months such as 

February and March does not correspond to expected seasonal move patterns and may indicate memory decay or 

respondent fatigue. 

Figure 2 is arranged identically to Figure 1 and presents mover trends by month for calendar year 2010.18 Like the 

pattern observed in the 2010 SIPP-EHC, later months in the 2011 SIPP-EHC reference year (i.e. October and 

November) have greater numbers of movers than the matched production SIPP sample, while the number of movers 

in the earlier months (February/March) is not statistically different from the matched production SIPP sample. 

However, the two production SIPP samples show fairly low percentages of movers, even in summer months, 

suggesting sample attrition may be distorting the production SIPP mover trends. There is generally more evidence of 

the seasonality of moves in the 2011 SIPP-EHC data, than in the 2010 data. For example, the proportion of the 

sample that reported a move decreased from 2.4 percent in October to 1.6 percent in December. 

Overall, the two SIPP-EHC field tests yielded more movers than the production SIPP samples. While this may be 

largely because of our decision to omit movers in production SIPP who left the sample, our findings suggest that the 

amount of usable mover data appears to be greater in SIPP-EHC than in production SIPP. However, there is also 

some evidence that respondents in SIPP-EHC report fewer moves in earlier calendar months than in later months. 

This could indicate recall problems or respondent fatigue with reporting detailed address information. 

Future Research 

We plan to undertake a number of future research projects to further evaluate the quality of address information 

collected in SIPP-EHC. 

(1) The residence history information in SIPP-EHC can be used to create a distance moved recode variable 

indicating whether a reported move was intracounty, intercounty within the same state, or interstate. We 

plan to compare the actual distance moved, calculated using the geocoded addresses, to this recode 

variable. We are primarily interested in identifying short-distance interstate moves, and long-distance 

intracounty moves and how frequently these situations occur. 

(2) In addition to collecting detailed residence history information, SIPP-EHC also collects detailed address 

information for up to seven employers for each adult respondent, information not collected in production 

SIPP. In future research, we plan to evaluate the quality of the employer address information using the 

geocoding methods in this paper. 

(3) Because of time constraints, the addresses in this paper were geocoded using ArcGIS, rather than the 

Census auto-coder. We plan to run the SIPP-EHC tests data addresses through the Census auto-coder and 

compare the results to those from ArcGIS. This will allow us to assess the effectiveness of both geocoding 

platforms. 

(4) We plan to look at beginning and end months of spells for other topics in the SIPP-EHC to see if the pattern 

of more spells at later months of the reference year is unique to the residence history section, or more 

general to the EHC. 

 

17 Mover rates tend to be highest during the summer months and lowest during winter. See Marlay, Matthew C., and 

Alison K. Fields (2010). Seasonality of Moves and the Duration and Tenure of Residence: 2004. Current Population 

Reports, P70-122. U.S. Census Bureau, Washington, DC.

18 A tabular version of this figure is included as Appendix table A.4 

8 
The Census is currently preparing a 2012-SIPP-EHC field test. This version of the survey will be a Wave 2 

interview, following respondents from the 2011 SIPP-EHC field test for a second year. The 2012 field test will 

provide an opportunity to study the impact of sample attrition on Wave 2 interviews. One of the strengths of SIPPEHC is that the first wave of the survey will collect an entire year’s worth of information, rather than the four 

months of data collected in the first wave of production SIPP. This is important because sample attrition is greatest 

in production SIPP after the first wave, meaning a large number of respondents exit the survey only having provided

four months of information and without answering a number of the topical modules. However, the yearlong gap 

between waves in SIPP-EHC means successfully identifying movers between waves is particularly important in 

SIPP-EHC. The Census Bureau is planning to conduct a locating experiment on households that will be part of the 

2012 SIPP-EHC field test. The households will be split into three groups. The first group will receive a selfenclosable address change postcard indicating whether the whole household has moved, part of the household has 

moved, the household intends to move, and updated contact information. This group will also receive a small 

monetary incentive for responding to the address update. The second group will receive the address update postcard, 

but no incentive. The third group will be the control, and will not receive the address postcard. This experiment is 

intended to help the Bureau develop more effective methods for locating movers. 

Conclusion 

The purpose of SIPP-EHC is to reduce both the survey cost and respondent burden of collecting 4 years worth of 

longitudinal data on employment and program participation, while still providing data of similar content and 

structure to the current production SIPP. The shift to a year-long reference period in SIPP-EHC increases the 

reliance on respondents to recall information, particularly for the residence history section, where respondent in 

SIPP are never asked to recall retrospective address information. 

In this paper we evaluated the residence history data collected in SIPP-EHC by focusing on the quality and 

frequency of reported residence information compared to the 2008 production SIPP. Our results are cautiously 

optimistic. A majority of the detailed retrospective address information reported in the 2011 SIPP-EHC was 

successfully coded to at least the county-level, with almost 60 percent coded to the street-level. While these numbers 

are lower than the block-level coding success rates for the 2008 production SIPP, this was expected, because current 

SIPP does not collect retrospective address information. The comparisons of 2010 and 2011 SIPP-EHC mover rates 

to those of calendar year files from the 2008 production SIPP suggest that SIPP-EHC collects more usable mover 

data than production SIPP, as confirmed by higher overall mover rates. Evidence of seasonal move patterns in the 

2011 SIPP-EHC was observed, with more movers in the summer months than winter, although this pattern was not 

present in the 2010 SIPP-EHC. There was, however, some evidence of potential memory decay, or respondent 

misreporting of residences during the early months of the EHC in both tests surveys. 

9 
Table 1: Geocoding Results from 2011 SIPP-EHC 

From SCIF Total Yes No 

 Number Percent Number Percent Number Percent 

Address Status 3,295 100.0 2,579 100.0 716 100.0 

Able to Match 3,008 91.3 2,577 99.9 431 60.2 

Exact 2,589 78.6 2,399 93.0 190 26.5 

Minor corrections 362 11.0 137 5.3 225 31.4 

After investigation 57 1.7 41 1.6 16 2.2 

Unable to Match 287 8.7 2 0.1 285 39.8 

Invalid street address 46 1.4 1 0.0 45 6.3 

Street but no number 70 2.1 0 0.0 70 9.8 

City 116 3.5 1 0.0 115 16.1 

County 10 0.3 0 0.0 10 1.4 

State 7 0.2 0 0.0 7 1.0 

Country 3 0.1 0 0.0 3 0.4 

Non-U.S. address 35 1.1 0 0.0 35 4.9 

Match Type 3,295 100.0 2,579 100.0 716 100.0 

Automatic 2,950 89.5 2,386 92.5 564 78.8 

Manual 267 8.1 128 5.0 139 19.4 

Picked from 

 

map 78 2.4 65 2.5 13 1.8 

10 
Table 2: Autocoder Results from 2008 Production SIPP (Block-level) 

 

Total Matched 

Total Auto Clerical 

Wave Number Percent Number Percent Number Percent Number Percent

Wave 2 1,785 100.0 1,597 89.5 1,428 80.0 169 9.5 

Wave 3 2,172 100.0 1,962 90.3 1,741 80.2 221 10.2 

Wave 4 2,613 100.0 2,418 92.5 2,154 82.4 264 10.1 

Wave 5 2,181 100.0 1,974 90.5 1,741 79.8 233 10.7 

Wave 6 2,282 100.0 2,089 91.5 1,814 79.5 275 12.1 

Wave 7 2,195 100.0 1,987 90.5 1,727 78.7 260 11.8 

Wave 8 1,796 100.0 1,646 91.7 1,512 84.2 134 7.5 

11 
Table 3: Geocoding Results from 2011 SIPP-EHC by State 

From SCIF 

Overall Yes No 

 

 

12 

Matched Unmatched Matched Unmatched Matched Unmatched 

Total Number Percent Number Percent Total Number Percent Number Percent Total Number Percent Number Percent 

All Addresses 3,295 3,008 91.3 287 8.7 2,579 2,577 78.2 2 0.1 716 431 13.1 285 8.6 

Arizona 111 95 85.6 16 14.4 67 67 60.4 0 0.0 44 28 25.2 16 14.4 

California 801 725 90.5 76 9.5 645 645 80.5 0 0.0 156 80 10.0 76 9.5 

Connecticut 51 47 92.2 4 7.8 41 41 80.4 0 0.0 10 6 11.8 4 7.8 

Florida 289 270 93.4 19 6.6 231 231 79.9 0 0.0 58 39 13.5 19 6.6 

Georgia 41 35 85.4 6 14.6 30 30 73.2 0 0.0 11 5 12.2 6 14.6 

Illinois 138 131 94.9 7 5.1 113 113 81.9 0 0.0 25 18 13.0 7 5.1 

Indiana 53 48 90.6 5 9.4 37 37 69.8 0 0.0 16 11 20.8 5 9.4 

Louisiana 67 62 92.5 5 7.5 53 53 79.1 0 0.0 14 9 13.4 5 7.5 

Maryland 50 49 98.0 1 2.0 44 44 88.0 0 0.0 6 5 10.0 1 2.0 

Massachusetts 115 108 93.9 7 6.1 88 88 76.5 0 0.0 27 20 17.4 7 6.1 

Michigan 83 78 94.0 5 6.0 64 64 77.1 0 0.0 19 14 16.9 5 6.0 

Missouri 63 60 95.2 3 4.8 49 49 77.8 0 0.0 14 11 17.5 3 4.8 

New Jersey 57 56 98.2 1 1.8 48 48 84.2 0 0.0 9 8 14.0 1 1.8 

New York 454 432 95.2 22 4.8 390 390 85.9 0 0.0 64 42 9.3 22 4.8 

Ohio 141 132 93.6 9 6.4 110 110 78.0 0 0.0 31 22 15.6 9 6.4 

Oklahoma 63 57 90.5 6 9.5 48 47 74.6 1 1.6 15 10 15.9 5 7.9 

Pennsylvania 160 154 96.3 6 3.8 139 139 86.9 0 0.0 21 15 9.4 6 3.8 

Tennessee 68 63 92.6 5 7.4 49 49 72.1 0 0.0 19 14 20.6 5 7.4 

Texas 376 348 92.6 28 7.4 291 290 77.1 1 0.3 85 58 15.4 27 7.2 

Virginia 51 49 96.1 2 3.9 42 42 82.4 0 0.0 9 7 13.7 2 3.9 

Other States 25 9 36.0 16 64.0 0 0 0.0 0 0.0 25 9 36.0 16 64.0 
Table 4: 2010 SIPP-EHC Matched to 2008 SIPP, 2009 Calendar Year (Waves 2-5) 

Variable 

2009 Calendar Year, 

Waves 2-5 of 2008 SIPP 

2009 Calendar Year, 

Matched to 2010 

Geography 

2010 Unedited SIPPEHC 

Number Percent C.I. Number Percent C.I. Number Percent C.I.

1,2 Number of Moves 107,066 100.0 - 7,777 100.0 - 14,726 100.0 - 

0 97,695 90.9 0.2 6,993 89.7 0.8 12,568 85.3 0.5 

1 8,518 8.3 0.2 734 9.6 0.7 1,910 13.0 0.5 

2 or more 853 0.9 0.1 50 0.7 0.2 248 1.7 0.2 

1. Because of an instrument error that lead to erroneous respondent reports of moving in January, the estimates do 

not include January moves. 

2. In this table the reported sample sizes are unweighted, but the proportions are weighted to January, 2009 of the 

production SIPP matched geography sample. 

 

13 
Table 5: 2011 SIPP-EHC Matched to 2008 SIPP, Wave 5-8 2010 Calendar Year 

Variable 

2010 Calendar Year, 

Waves 5-8 of 2008 SIPP 

2010 Calendar Year, 

Matched to 2011 

Geography 

2011 Unedited SIPPEHC 

Number Percent C.I. Number Percent C.I. Number Percent C.I.

1,2 Number of Moves 101,118 100.0 - 12,282 100.0 - 7,003 100.0 - 

0 92,402 91.1 0.2 10,994 89.1 0.1 5,769 82.4 0.7 

1 7,912 8.0 0.2 1,153 9.7 0.7 1,087 15.5 0.7 

2 or more 804 0.8 0.2 135 1.2 0.6 147 2.1 0.3 

1. Because of an instrument error that lead to erroneous respondent reports of moving in January, the estimates do 

not include January moves. 

2. In this table the reported sample sizes are unweighted, but the proportions are weighted to January, 2010 of the 

production SIPP matched geography sample. 

14 
Figure 1 

0.0

0.5

1.0

1.5

2.0

Percent Moved

Movers by Month for the 2009 Calendar

Year

2008 SIPP

2008 SIPP Matched 2010

Geography

2010 SIPP‐EHC

15 
Figure 2 

 

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Percent Moved

Movers by Month for the 2010

Calendar Year

2008 SIPP

2008 SIPP Matched

2011 Geography

2011 SIPP‐EHC

16 
Appendix 

Table A.1: Rotation Groups, Waves, and Reference Months for the 2008 SIPP Panel 

2008 SIPP 

2008 2009 

May Jun Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Rotation 1 1-1 1-2 1-3 1-4 2-1 (I) 2-2 2-3 2-4 3-1 (I) 3-2 3-3 3-4 4-1(I) 4-2 4-3 4-4 5-1(I) 5-2 5-3 5-4 

Rotation 2 1-1 1-2 1-3 1-4 2-1 (I) 2-2 2-3 2-4 3-1 (I) 3-2 3-3 3-4 4-1(I) 4-2 4-3 4-4 5-1(I) 5-2 5-3 

Rotation 3 

 

1-1 1-2 1-3 1-4 2-1 (I) 2-2 2-3 2-4 3-1 (I) 3-2 3-3 3-4 4-1(I) 4-2 4-3 4-4 5-1(I) 5-2 

Rotation 4 1-1 1-2 1-3 1-4 2-1 (I) 2-2 2-3 2-4 3-1 (I) 3-2 3-3 3-4 4-1(I) 4-2 4-3 4-4 5-1(I) 

 

2008 SIPP 

2010 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

Rotation 1 6-1(I) 6-2 6-3 6-4 7-1 (I) 7-2 7-3 7-4 8-1 (I) 8-2 8-3 8-4 

Rotation 2 5-4 6-1 6-2 6-3 6-4 7-1 (I) 7-2 7-3 7-4 8-1 (I) 8-2 8-3 

Rotation 3 5-3 5-4 6-1 6-2 6-3 6-4 7-1 (I) 7-2 7-3 7-4 8-1 (I) 8-2 

Rotation 4 5-2 5-3 5-4 6-1 6-2 6-3 6-4 7-1 (I) 7-2 7-3 7-4 8-1 (I) 

Note: In the number sequences, the first indicates the wave and the second indicates the reference month. (I) indicates the 

interview month. The reference period is the four months preceding the interview month. 

Source: U.S. Census Bureau, Survey of Income and Program Participation (SIPP) 2008 panel. 

17 
Table A.2: Geocoding Results from 2011 SIPP-EHC by Match Type

 

Match Type 

Total Automatic Manual Picked from Map 

Number Percent Number Percent Number Percent Number Percent 

Address Status 3,295 100.0 2,950 100.0 267 100.0 78 100.0 

Able to Match 3,008 91.3 2,666 90.4 264 98.9 78 100.0 

Exact 2,589 78.6 2,456 83.3 88 33.0 45 57.7 

Minor corrections 362 11.0 210 7.1 145 54.3 7 9.0 

After investigation 57 1.7 0 0.0 31 11.6 26 33.3 

Unable to Match 287 8.7 284 9.6 3 1.1 0 0.0 

Invalid street address 46 1.4 46 1.6 0 0.0 0 0.0 

Street but no number 70 2.1 67 2.3 3 1.1 0 0.0 

City 116 3.5 116 3.9 0 0.0 0 0.0 

County 10 0.3 10 0.3 0 0.0 0 0.0 

State 7 0.2 7 0.2 0 0.0 0 0.0 

Country 3 0.1 3 0.1 0 0.0 0 0.0 

Non-U.S. address 35 1.1 35 1.2 0 0.0 0 0.0 

18 
Table A.3: 2010 SIPP-EHC Matched to 2008 SIPP, 2009 Calendar Year (Waves 2-5) 

2009 Calendar Year, Waves 

2-5 of 2008 SIPP 

2009 Calendar Year, 

Matched to 2010 Geography 2010 Unedited SIPP-EHC 

Month of Move Total N Movers Percent Total N Movers Percent Total N Movers Percent

January1

 97,630 843 0.8 6,965 55 0.8 14,542 37222.6

February 97,045 832 0.9 6,869 80 1.0 14,560 144 1.0

March 96,010 867 0.9 6,794 64 0.9 14,577 150 1.0

April 94,996 979 1.1 6,694 102 1.5 14,595 195 1.3

May 94,169 923 1.0 6,650 70 1.2 14,609 167 1.1

June 93,084 1,067 1.2 6,573 89 1.4 14,627 248 1.7

July 91,972 997 1.1 6,444 63 0.9 14,646 220 1.5

August 91,089 1,120 1.3 6,368 91 1.6 14,662 277 1.9

September 90,541 1,065 1.2 6,313 99 1.6 14,679 248 1.7

October 90,145 846 1.0 6,291 79 1.1 14,698 274 1.9

November 90,351 774 0.9 6,388 42 0.8 14,709 240 1.6

December 90,393 821 0.9 6,433 59 0.8 14,726 269 1.8

1. In this table the reported sample sizes are unweighted, but the proportions are weighted to January, 2009 of the 

production SIPP matched geography sample. 

2. The estimate of January movers is unreliable because of an instrument error. 

19 
TABLE A.4: 2011 Re-SIPP Matched to 2008 SIPP, Wave 5-8 2010 Calendar Year 

Month of Move 

2010 Calendar Year, Waves 

5-8 of 2008 SIPP 

2010 Calendar Year, 

Matched to 2011 Geography 2011 Unedited SIPP-EHC 

Total N Movers Percent Total N Movers Percent Total N Movers Percent 

January1

 90,034 914 1.0 10,625 169 1.6 6,989 1232 1.8 

February 89,745 886 1.0 10,707 133 1.2 6,989 95 1.3 

March 88,911 902 1.1 10,632 154 1.5 6,989 113 1.6 

April 88,087 897 1.1 10,554 146 1.3 6,990 103 1.5 

May 87,622 965 1.1 10,561 131 1.4 6,990 90 1.3 

June 86,960 921 1.0 10,493 136 1.2 6,991 124 1.8 

July 86,291 955 1.1 10,427 119 1.2 6,992 133 1.9 

August 85,376 864 1.0 10,334 118 1.0 6,994 177 2.5 

September 84,966 885 1.0 10,295 131 1.1 6,996 143 2.0 

October 84,422 876 1.0 10,227 118 1.2 6,996 169 2.4 

November 84,411 688 0.8 10,210 95 0.9 7,002 149 2.1 

December 84,410 722 0.9 10,309 152 1.6 7,003 114 1.6 

1. In this table the reported sample sizes are unweighted, but the proportions are weighted to January, 2010 of 

the production SIPP matched geography sample. 

2. The estimate of January movers is unreliable because of an instrument error. 

20