1

Rotation Group Bias in Smoking Prevalence Estimates 

Using TUS-CPS 

Younghwan Song 

Department of Economics, Union College, Schenectady, NY 12308 

songy@union.edu

Proceedings of the 2013 Federal Committee on Statistical Methodology (FCSM) Research Conference 

Abstract 

This paper examined if smoking prevalence estimates based on the Tobacco Use Supplement to the Current 

Population Survey (TUS-CPS) suffer from rotation group bias due to panel attrition and panel conditioning. The 

TUS-CPS has been administered as part of the Current Population Survey (CPS), which has eight rotation groups of 

households in each month that are repeatedly interviewed based on a sample rotation scheme. Previous research has 

found that even though all eight rotation groups in the CPS are independent random samples of the population, some 

estimates, such as unemployment rates, tend to be significantly higher in the first rotation group than among other 

rotation groups. The multivariate probit regression results of this paper showed that although panel attrition is 

prevalent in all years of the TUS-CPS, for the six waves of TUS-CPS before 2003 there is no evidence that smoking 

prevalence estimates were significantly affected by the rotation scheme of the CPS. For the three waves of TUSCPS since 2003, however, the results showed that smoking prevalence has been underestimated due to panel

attrition and panel conditioning. It appears that rotation group bias in these waves was caused by the substantially 

increased number of additional questions smokers had to answer. 

Keywords: rotation group bias, panel attrition, panel conditioning 

1. Introduction 

The adverse health effects from smoking caused more than 443,000 deaths, or nearly one of every five deaths, each 

year from 2000 to 2004 in the United States (Centers for Disease Control and Prevention 2008; National Center for 

Health Statistics 2007). In an effort to curtail this public health problem, decreasing the prevalence of cigarette 

smoking among adults to less than 12% has been one of the national health objectives for 2020 (U.S. Department of 

Health and Human Services 2013). To assess progress toward this objective, it is essential to precisely measure 

current smoking prevalence in the U.S. adult population. 

One survey often used to provide such estimates is the Tobacco Use Supplement to the Current Population Survey 

(TUS-CPS). The Current Population Survey (CPS) is a monthly labor force survey conducted by the U.S. Census 

Bureau for the Bureau of Labor Statistics and is the source of official government statistics on employment and 

unemployment. The TUS-CPS, sponsored by the National Cancer Institute, is a survey of tobacco use that has been 

administered as part of the CPS in select months since 1992. Because it is based on a large, nationally 

representative sample of the CPS—individuals living in approximately 56,000 households, selected on the basis of 

area of residence across the country—the TUS-CPS has also been extensively used in tobacco research for small 

geographic regions, such as states (Biener et al. 2004; Shopland et al. 1996), and small groups, such as those defined 

by immigrant status, employment status, age, education, and race/ethnicity (Acevedo-Garcia et al. 2005; Baluja et al. 

2003; Fagan et al. 2007; Green et al. 2007; Shavers et al. 2005).

The CPS is designed so that each household whose address is selected for the sample is repeatedly interviewed 

following a 4–8–4 sample rotation scheme: the household is interviewed for 4 consecutive months, not surveyed for 

8 consecutive months, and then re-interviewed for 4 consecutive months before being finally dropped from the 

survey. This rotation scheme was introduced as a way to reduce response burden and also as a compromise between 

a permanent sample (from which a high response rate would be difficult to maintain) and a completely new sample 

each month (which results in more variable estimates of change) (U.S. Census Bureau 2006). Each month a new 

group of households enters the sample for the first time and another group of households retires from the sample 
2

permanently. In any particular month, as a result, there are eight groups of households and the number of times each 

household’s address is in the sample varies from 1 (Rotation Group 1) to 8 (Rotation Group 8). Typically, most 

CPS interviews for the first and the fifth rotation groups are conducted in person because the CPS sample is strictly 

a sample of addresses and the U.S. Census Bureau needs to confirm that the respondents are, in fact, residing in the 

sample household. However, if the respondent requests during the initial personal contact, telephone interviews are 

conducted even for these rotation groups. Most interviews for the remaining rotation groups are conducted over the 

telephone, with the approval of the respondent. Telephone interviewing is generally preferred because it is much 

more time and cost efficient. In the interests of timeliness and efficiency, the CPS also allows proxy responses: any 

knowledgeable adult household member can be the respondent for other household members and the respondent can 

change from interview to interview. Yet the majority of the CPS data is collected by self-response (U.S. Census 

Bureau 2006). 

Although the TUS-CPS has been regarded as a “gold standard” for state estimates (Biener et al. 2004), its nationallevel estimates for smoking prevalence have been consistently lower than those from the National Health Interview 

Survey (NHIS), another data set commonly used to measure smoking prevalence in the U.S. adult population (Rodu 

and Cole 2009). For example, the national-level estimate for smoking prevalence from the 2011 NHIS was 19%, 

whereas the estimate from self-responses of the 2010–11 TUS-CPS was 16.1% (Centers for Disease Control and 

Prevention 2012; National Cancer Institute 2013). The NHIS, conducted by the National Center for Health

Statistics, an arm of the Centers for Disease Control and Prevention, is based on a cross-sectional representative 

sample of the civilian noninstitutionalized population of the U.S. Unlike the TUS-CPS, the NHIS does not rely on a 

sample rotation scheme and collects all data through personal interviews. In addition, proxy responses are rarely 

allowed in the NHIS. Considering these differences in survey methodologies between the two surveys, it is natural 

to investigate how they affect the estimates of smoking prevalence in the TUS-CPS. 

The effects of survey mode (telephone or in-person) and respondent type (self or proxy) on the estimates of smoking 

prevalence have received extensive attention in the literature (Baron-Epel et al. 2004; Beland and St-Pierre 2008; 

Caraballo et al. 2001; Donovan et al. 1997; Gilpin et al. 1994; Harakeh et al. 2006; Hyland et al. 1997; Navarro 

1999; Simile et al. 2006). Recently Soulakova et al. (2009) investigated whether survey mode and respondent type 

affect the current smoking prevalence estimates in the 1992 through 2003 TUS-CPS. After controlling for various 

sociodemographic characteristics, they found that the smoking prevalence obtained from proxy-responses is lower 

than that obtained from self-responses. They also concluded that the smoking prevalence obtained from telephone 

responses underestimates the current smoking rate by 3 percentage points. 

One important survey methodological factor that Soulakova et al. (2009) failed to examine is the sample rotation 

scheme of the CPS and its effect on smoking prevalence estimates in the TUS-CPS. Because each rotation group in 

the CPS is an independent random sample of the population, it is possible to get eight separate estimates of the 

population characteristic of interest for a given month, and these estimates from different rotation groups supposedly 

should not differ systematically. However, some estimates, such as unemployment rates, tend to be significantly 

higher in the first rotation group than among other rotation groups (Bailar 1975; Brooks and Bailar 1978; Hansen et 

al. 1955; McCarthy 1978; Shack-Marquez 1986; Solon 1986; U.S. Census Bureau 2006; Williams and Mallows 

1970). Such differences in estimates are called rotation group bias or time-in-sample bias. Some factors have been 

suggested for why responses may vary systematically with time in the sample (McCarthy 1978): conditioning of 

respondents or interviewers by repeated interviews; differences in the lengths and contents of the questionnaire 

among rotation groups (the first interview is longer than others because demographic information about all members 

of the household has to be obtained); differences in the mode of interview; differences in the respondents for the 

household; and differences in the characteristics of nonrespondents among rotation groups. It has also been shown 

that the estimators using the full sample are biased unless these systematic biases across rotation groups cancel each 

other out (Bailar 1975; Solon 1986). 

Given that the TUS-CPS is based on the CPS, it is natural to suspect that smoking prevalence estimates based on the 

TUS-CPS will exhibit rotation group bias. Among the various suggested factors for rotation group bias, this paper 

focuses on differences in the characteristics—in particular, smoking status—of nonrespondents among rotation 

groups (panel attrition) and panel conditioning. Previous research has shown that tobacco users are more likely to 

attrite in longitudinal surveys (Cunradi et al. 2005; Gray et al. 1996; Young et al. 2006). Using two rounds of a 

survey on health and lifestyles among adults in Great Britain in 1984–85 and 1991–92, Gray et al. (1996) found that 

regular smokers were more likely to attrite, partly due to their higher mortality. Using two rounds of survey data, in 
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1998 and 2000, from a cohort of young adults recruited for the U.S. Navy, Cunradi et al. (2005) also found that 

baseline tobacco users were more likely to be attritors than nonusers, even after controlling for other factors, 

including education and religious affiliation. Finally, using data from three age cohorts (18–23 years, 45–50 years, 

and 70–75 years) of the Australian Longitudinal Study on Women’s Health, initially collected in 1996 with a 

follow-up 2–4 years later, Young et al. (2006) found that in all age cohorts, women who were current smokers were 

more likely to become nonrespondents, even after health status and other socioeconomic factors were controlled for. 

One explanation for the higher attrition of smokers is because smokers are more likely to develop health problems 

that would decrease their ability to respond to follow-up surveys. Another explanation is that some characteristics 

associated with smoking make smokers less willing to participate in survey follow-up than nonsmokers. In fact, 

smokers are less likely to use a seat belt, brush or floss their teeth, and do physical exercise than nonsmokers 

(Hersch 1996). Also smokers are more impulsive than nonsmokers in that they choose small but immediate rewards 

over large but delayed rewards (Bickel et al. 1999; Khwaja et al. 2006; Lahiri and Song 2000; Odum et al. 2002; 

Reynolds et al. 2004). Furthermore, smokers have substantially less education (Levine et al. 1997), lower wages 

(Grafova and Stafford 2009; Munasinghe and Sicherman 2006), are less likely to vote at election time (Denny and 

Doyle 2007; Keller et al. 2002) and spend more time on activities that provide immediate gratification, such as 

watching television, but less time on activities that provide long-term returns, such as exercising and education, than 

nonsmokers (Song 2011). Regardless of the reasons, the higher attrition of smokers in panel surveys found in 

previous literature suggests that the successive rotation groups in the TUS-CPS could be subject to the same 

problem. Then, estimates using all rotation groups in the TUS-CPS are likely to underestimate “true” smoking 

prevalence. 

Recently Halpern-Manners and Warren (2012) showed evidence for panel conditioning effects in the CPS.1 To 

identify panel conditioning effects in the basic CPS, they examined unemployment rates comparing people who

differ only with respect to whether they were interviewed for the first time or second time in the CPS, by only using 

individuals in the first and second rotation groups in the same CPS month, who were household heads, selfrespondents, and matched across their first and second months in sample. They found that even in this restricted 

group, free of panel attrition, unemployment rates were consistently lower in the second rotation group because in an 

effort to minimize their survey burden after participating in the CPS for the first time, some respondents changed 

their labor force status from “unemployed” to “out of labor force.” As a result, estimates of unemployment rates 

based on all rotation groups in the basic monthly CPS are downwardly biased. They also noted that panel 

conditioning effects are frequently observed when survey waves are separated by 1 month or less, such as the basic 

monthly CPS survey, but fewer panel conditioning effects are expected when surveys are separated by longer 

periods of time. Except for a few months, respondents in general participate in the TUS-CPS only once. However, 

they are still part of the rotation scheme of the CPS and thus may change their responses to the TUS-CPS to 

minimize their burden after initial participation in the CPS. Then, estimates using all rotation groups in the TUSCPS are likely to underestimate “true” smoking prevalence. 

2. Descriptive Analysis of Rotation Group Bias 

The national level estimates of smoking prevalence for the population 18 years and older by the National Cancer 

Institute are based on three TUS-CPS data sets pooled from adjacent periods (National Cancer Institute 2013). So I 

first estimated smoking prevalence by rotation group for the following years of TUS-CPS using pooled data in 

adjacent periods: 1992‒ 93 (September 1992, January 1993, and May 1993), 1995–96 (September 1995, January 

1996, and May 1996), 1998–99 (September 1998, January 1999, and May 1999), 2000 (January 2000 and May 

2000), 2001–02 (June 2001, November 2001, and February 2002), 2003 (February 2003, June 2003, and November 

2003), 2006–07 (May 2006, August 2006, and January 2007), and 2010–11 (May 2010, August 2010, and January 

2011). (Only two TUS-CPS data sets were collected in 2000.) All estimates are weighted using the TUS-CPS 

nonresponse weights (PWNRWGT) and standard errors have been estimated using the balanced repeated replication 

method in Stata. 

Table 1 provides estimates of current smokers for each year of TUS-CPS by rotation group. It shows evidence that 

estimates of current smokers based on the TUS-CPS vary by rotation group and the estimate for the first rotation 

group is higher than the estimates for other rotation groups. This is clearly demonstrated by means of a rotation 

 

1

 See Warren and Halpern-Manners (2012) for a general overview of panel conditioning in longitudinal data. 
4

group index: the ratio of the estimate based on a particular rotation group to the average estimate over all eight 

rotation groups, multiplied by 100 (Bailar 1975; U.S. Census Bureau 2006). If an equal percentage of current 

smokers are present in each rotation group, the index for each rotation group would be 100. In Table 1, the index of 

105.39 for the first rotation group in the 1992–93 TUS-CPS indicated that the rotation group in the sample for the 

first time gave an estimate 5.39% higher than the average for all rotation groups. Figure 1, which graphically 

presents these rotation group indices, illustrates that for all years of TUS-CPS, estimates of smoking prevalence are 

higher for those in their first month in the sample than for TUS-CPS respondents as a whole. Furthermore, it is also 

clear from Figure 1 that the difference in smoking prevalence between the first rotation group and the overall group 

became substantially larger since 2003. Between 1992–2002, the rotation group indices for respondents in their first 

month in the sample are about 105, but between 2003–2011, they are about 110.

3. Regression Analysis of Rotation Group Bias 

Significant variations in rotation group indices found in the above analysis, however, do not necessarily imply that 

there are systematic biases in estimates of smoking prevalence across rotation groups. The distribution of other 

covariates associated with smoking status, such as age, gender, race/ethnicity, and education of the respondents, can 

certainly vary across rotation groups. Furthermore, factors—such as the mode of interview and respondents for the 

household—vary across rotation groups (McCarthy 1978). To control for all these differences, I conducted

multivariate probit analysis for each period. The dependent variable was a dummy variable for current smokers. In 

addition to a dummy for the first rotation group, the following variables that are typically controlled for as 

determinants of smoking were included as the independent variables: three dummies for age categories (18 to 24, 25 

to 44, 45 to 64; the reference group is age 65 or older); a female dummy; five dummies for race/ethnicity (Black, 

Hispanic, American Indian/Alaskan Native, Asian/Pacific Islander, and other; the reference group is White); three 

dummies for education (high school, some college, and college or more; the reference group is less than high 

school); two dummies for employment status (unemployed and not in labor force; the reference group is employed); 

three dummies for marital status (married, widowed, and divorced/separated; the reference group is never married); 

dummies for family income levels, including family income missing; a dummy for home owner2; two dummies for 

metropolitan areas (non metropolitan area and not identified; the reference group is metropolitan area); three region 

dummies; dummies for TUS-CPS month in each period; a dummy for self-response (the reference group is proxy 

response); and two dummies for survey mode (personal and unknown; the reference group is telephone interview).3 

The marginal effects of the dummy for the first rotation group will show the magnitude of rotation group bias. 

The probit marginal effects of the first rotation group for each period are presented in Table 2. For comparison, 

Panel A of Table 2 reports the results where the only control variable is the dummy for the first rotation group, 

whereas Panel B of Table 2 reports the probit marginal effects of the model that includes the above-mentioned full 

control variables. Similar to the findings in Table 1, in Panel A of Table 2 the prevalence estimates of smoking are 

0.9 to 2.1 percentage points higher for the first rotation group than for Rotation Groups 2 through 8. And the 

differences became larger since 2003. Considering that estimates of smoking prevalence—the means of the 

dependent variable reported in Table 2—have been monotonically decreasing between 1992 and 2011, the 

magnitudes of the difference between the first rotation group and the rotation groups 2 through 8 as a fraction of the 

estimates of smoking prevalence have also become larger in recent years. In Panel B of Table 2, when the 

differences in characteristics of the respondents, the modes of interview and respondent types between the first 

rotation group and other rotation groups are controlled for, the gaps in smoking prevalence became smaller for all 

periods but still remained statistically significant for the years 2003, 2006–07, and 2010–11. In sum, the findings in 

Panels A and B of Table 2 indicated that for the years 1992–93, 1995–96, 1998–99, 2000, and 2001–02, rotation 

group bias observed in Panel A was due to the differences in characteristics of the respondents, the modes of 

interview and respondent types between the first rotation group and other rotation groups. In contrast, for the years 

2003, 2006–07, and 2010–11, these differences do not fully explain the difference between the first rotation group 

and other rotation groups. If only Rotation Group 1 was used, rather than using all rotation groups, to estimate 

smoking prevalence, the estimates would have been higher by 1.6 to 1.8 percentage points, about 3.6 to 3.9 million 

more smokers, for the years 2003, 2006–07, and 2010–11. 

 

2

 Balabanova et al. (1998) and Laaksonen et al. (2005) showed that home ownership is negatively correlated with 

smoking. 

3

 For 1992–93 TUS-CPS, a dummy for unknown respondent type is also included. 
5

4. Changes in the TUS-CPS 

Given the findings in the previous section that rotation group bias is still observed for the years 2003, 2006–07, and 

2010–11, even after controlling for other covariates, but not for the years 1992–93, 1995–96, 1998–99, 2000, and

2001–02, it is worthwhile to investigate if any of the changes over time in the TUS-CPS relate to that pattern. 

According to the National Cancer Institute (2013), all TUS-CPS contain generally the same information, covering 

current cigarette smoking status and amount smoked; smoking history, quit attempts, and intention to quit; 

medical/dental advice to quit; cigar, pipe, and smokeless tobacco use; workplace and home smoking restrictions; and 

attitudes toward smoke-free policies in public places. Since 2003, as shown in Table 3, the following information 

was also collected from smokers: use of menthol cigarettes; level of nicotine dependence; cost of cigarettes and 

purchase location; and harm reduction and other emerging products. As a result, the number of questions in the 

TUS-CPS increased substantially since 2003 and may have increased the burden on respondents, in particular those 

responding as smokers. For example, Table 3 shows that before 2003, the number of variables listed in the TUSCPS codebook did not change noticeably, staying between 69 and 85, except 27 for the 2000 TUS-CPS when an 

abbreviated version of the TUS-CPS was implemented. But since 2003 the number of variables listed in the TUSCPS codebook increased almost two- to four-fold, reaching 240 in 2003, 168 in 2006–07, and 323 in 2010–11. 

Although the last column of Table 3 shows that the person nonresponse rates to the TUS-CPS have not fallen 

significantly since 2003, these changes in survey questions since 2003 may have put more burden on individuals 

who respond as smokers and may have affected their behavior in participating in the TUS-CPS, through panel 

attrition and panel conditioning. And as a result, they may have caused rotation group bias for the years 2003, 

2006–07, and 2010–11 observed in the previous section. 

5. Panel Attrition in the TUS-CPS 

To examine if rotation group bias is due to higher attrition rates of smokers in the successive rotation groups in the 

CPS, using the rotation scheme of the CPS, I matched Rotation Group 1 from all the TUS-CPS to the subsequent 

CPS and tested if current smokers are less likely to be interviewed in the subsequent rotation groups in the CPS. For 

example, Rotation Group 1 from the January 2007 TUS-CPS can be matched to Rotation Group 2 in the February 

2007 CPS, Rotation Group 3 in the March 2007 CPS, and so on. Similar to the matching process used in Song 

(2011), I followed the guidelines from the U.S. Bureau of Labor Statistics (2010) and linked observations from 

Rotation Group 1 from the January 2007 TUS-CPS to Rotation Group 2 in the February 2007 CPS by using a set of 

household and individual identification variables.4 Although these identification variables produce unique matches, 

one also needs to check sex, race, and age because the CPS is a sample of housing addresses and not a sample of 

individuals. Successful matches should have the same values for sex and race, and acceptable ranges of age 

difference between the two surveys (Madrian and Lefgren 2000).5 By using the first rotation groups in each of the 

TUS-CPS, I examined whether the rate of successful match varied by smoking status. To control for the differences 

in other characteristics that may affect matching, I also conducted a multivariate probit analysis of matching. A set 

of control variables similar to those used in Panel B of Table 2 plus a dummy for availability of a telephone in the 

house are included as independent variables (Abraham et al. 2006). 

Table 4 reports the marginal effects of smoking in these Probit regressions. Consistent with the findings in the 

previous literature (Cunradi et al. 2005; Gray et al. 1996; Young et al. 2006), smokers are less likely to be matched 

to the subsequent CPS in almost all years. For example, the marginal effect of -0.025 in the first row of column 7 

indicates that smokers in Rotation Group 1 from the 1992–93 TUS-CPS (September 1992, January 1993, and May 

1993) are, on average, 2.5 percentage points less likely to be interviewed than nonsmokers 15 months later as 

Rotation Group 8 in the December 1993, April 1994, and August 1994 CPS, even after controlling for other 

covariates. Although the evidence of higher attrition rates for smokers in the extant literature is based on panel data 

at least 2 years apart, the results in Column 1 of Table 4 indicate that smokers in Rotation Group 1 in the 2006–07 

 

4

For the files matched to the 1992–93 TUS-CPS, the following variables are used in linking: HRHHID, 

HUHHNUM, and PULINENO. For the files matched to the 1995–96, 1998–99, 2000, 2001–02, and 2003 TUSCPS, the following variables are used in linking: HRHHID, HRSERSUF, HUHHNUM, HRSAMPLE, and 

PULINENO. For the files matched to the 2006–07 and 2010–11 TUS-CPS, the following variables are used in 

linking: HRHHID, HRHHID2, and PULINENO. 

5

 Due to the changes, race categories beginning in 2003 are not comparable to race categories before 2003. 

Therefore, in linking the 2001–02 TUS-CPS to the 2003 CPS, race was not used in determining successful matches. 
6

TUS-CPS are significantly less likely to be interviewed than nonsmokers, even in the CPS that is only 1 month after 

the TUS-CPS. 

Although the results in Table 4 provide evidence that smokers are more likely to attrite in subsequent rotation 

groups in the CPS, they are not providing convincing evidence that the pattern of rotation group bias observed in 

Table 2 is due to panel attrition. First, although Panel B of Table 2 shows significant marginal effects of Rotation 

Group 1 only for the years 2003, 2006–07, and 2010–11, Table 4 shows smokers are significantly more likely to 

attrite in the subsequent rotation groups in all years. Furthermore, the magnitudes of the marginal effects observed 

in Table 4 are too small to generate the magnitudes of the marginal effects of Rotation Group 1 of 0.9 to 1.6 

percentage points, compared with rotation groups 2 through 8, observed in Panel B of Table 2 for the years 2003, 

2006–07, and 2010–11. For example, with the smoking prevalence estimate of 20% in Rotation Group 1, the 

difference in attrition rates of 3 percentage points between smokers and nonsmokers6 would generate a difference 

less than 0.5 percentage points in smoking prevalence estimates between Rotation Group 1 and other rotation 

groups.7

 Finally, Table 4 only shows that smokers are more likely to attrite in subsequent rotation groups in the 

CPS, not in the TUS-CPS. Because the TUS-CPS is a supplement to the CPS, some of those who responded to the 

basic CPS questionnaires did not respond to the TUS-CPS questionnaires. For example, the household nonresponse 

rates for May and August 2006, and January 2007 on the basic CPS ranged from 7.6 to 9.1%, whereas the person 

nonresponse rates for the May and August 2006, and January 2007 TUS-CPS range from 14.8 to 19.3% (U.S. 

Census Bureau 2008). Then, rotation group bias in the TUS-CPS may be observed not because current smokers are 

less likely to participate in the basic CPS but because they are less likely to respond to the TUS-CPS. To examine 

this possibility, I matched two TUS-CPS data a year apart and examined whether the probability of a successful 

match varied by smoking status. There are three pairs of TUS-CPS data that can be matched. Rotation Groups 1, 2, 

3, and 4 from the January and May 1999 TUS-CPS can be matched to Rotation Groups 5, 6, 7, and 8 from the 

January and May 2000 TUS-CPS; and Rotation Groups 1, 2, and 3 from the February 2002 TUS-CPS can be 

matched to Rotation Groups 5, 6, and 7 from the February 2003 TUS-CPS (Rotation Group 4 was interviewed for 

the basic CPS but not for the Tobacco Use Supplement in February 2002). For comparability to Table 4, I only 

examined Rotation Group 1 from these TUS-CPS. Following the same matching procedure used for matching the 

TUS-CPS to the basic CPS, I matched Rotation Group 1 from the January, May 1999 and February 2002 TUS-CPS 

to Rotation Group 5 in the January, May 2000 and February 2003 TUS-CPS and ran a multivariate probit analysis of 

matching, including the same set of control variables used in Table 4. 

Table 5 reports the marginal effects of smoking in these probit regressions. The first row of Table 5 shows that in 

matching Rotation Group 1 in the January and May 1999 TUS-CPS to Rotation Group 5 in the January and May 

2000 Basic CPS, smokers are 3.09 percentage points less likely to be matched than nonsmokers. And in Column 2 

of Table 5 the magnitude of the negative marginal effect of smoking does not change substantially in matching 

Rotation Group 1 in the January and May 1999 TUS-CPS to Rotation Group 5 in the January and May 2000 TUSCPS. In the second row of Table 5, the magnitude of the negative marginal effect of smoking becomes substantially 

larger in matching Rotation Group 1 in the February 2002 TUS-CPS to Rotation Group 5 in the February 2003 CPS 

than those observed in row 1 of Table 5. Furthermore, the magnitude of the negative marginal effect of smoking 

becomes even larger in matching Rotation Group 1 in the February 2002 TUS-CPS to Rotation Group 5 in the 

February 2003 TUS-CPS than in matching to the February 2003 Basic CPS. Therefore, the results in Table 5

provide evidence that the level of higher attrition rates of smokers in the successive rotation group for the 1999 

TUS-CPS is no less severe in matching to TUS-CPS than in matching to the Basic CPS. Furthermore, in matching 

to the February 2003 TUS-CPS, where the burden of survey increased dramatically, the attrition rate for smokers 

seems to have increased substantially than in matching to the February 2003 Basic CPS, which is already higher 

than the attrition rate observed in matching Rotation Group 1 in the January and May 1999 TUS-CPS to Rotation 

Group 5 in the January and May 2000 Basic CPS. If the same level of substantially increased attrition rates for 

 

6

 Because the marginal effects of Rotation Group 1 observed in Panel B of Table 2 are compared with rotation 

groups 2 through 8, I first calculated the simple average of the marginal effects of smoking in Table 4 for each year 

over columns 1 through 7. The averages, ranging from -0.0103 in 2000 to -0.0284 in 2010-11, are well under the 

difference in attrition rates of 3 percentage points between smokers and nonsmokers used in this example. 

7

If 100% of the nonsmokers and 97% of the smokers from Rotation Group 1 are interviewed in the subsequent 

month, the percent of smokers in the subsequent month would be (20*0.97)*100/(20*0.97+80)=19.52, which is 0.48 

percentage points less than 20. This number is invariant to overall follow-up interview rates. 
7

smokers were continued in the years after 2003 because of the increased burden of survey in the TUS-CPS, the 

pattern of rotation group bias observed in Panel B of Table 2 could have resulted. 

6. Panel Conditioning in the TUS-CPS 

To examine if rotation group bias is due to panel conditioning in the successive rotation groups in the CPS, I first 

selected people who differ only with respect to whether they were interviewed for the first time or second time in the 

TUS-CPS or CPS, using a method similar to that used in Halpern-Manners and Warren (2012). For example, from 

Rotation Group 1 in the May 2006 TUS-CPS, only those matched to Rotation Group 2 in the June 2006 CPS were 

selected; from Rotation Group 2 in the May 2006 TUS-CPS, only those matched to Rotation Group 1 in the April 

2006 CPS were selected. In the end, the resulting sample contains only those individuals from rotation groups 1 and 

2 in the May 2006 TUS-CPS who were interviewed in two consecutive months and, as a result, any significant 

differences between these two groups of people would be evidence of panel conditioning.8 Ideally, to remove the 

effect of panel attrition completely, one needs to match rotation groups from one TUS-CPS to another TUS-CPS. 

However, there are no TUS-CPS data that are 1 month apart. I used the Basic CPS in lieu of the TUS-CPS. If the 

attrition rate by smoking status between one set of TUS-CPS data and one set of CPS data is similar to that between 

two TUS-CPS data, this method is not going to be problematic in detecting evidence of panel conditioning. 

However, if the attrition rate for smokers is higher in matching to one set of TUS-CPS data than in matching to one 

set of CPS data (as shown in the second row of Table 5), Rotation Group 1 in a TUS-CPS, whose second interview 

is in the CPS, will have a higher proportion of smokers than Rotation Group 2, whose second interview is in the 

TUS-CPS, in the matched data. Therefore, a higher proportion of smokers among Rotation Group 1 than among 

Rotation Group 2 in this matched data would be evidence of panel conditioning or panel attrition. 

I applied this procedure to each and every month of the TUS-CPS. Panel A of Table 6 shows the marginal effects of 

Rotation Group 1, relative to Rotation Group 2, in probit regression on current smoking with no other control 

variable in the matched data. Of the eight TUS-CPS data sets, six have positive and significant marginal effects for 

Rotation Group 1, suggesting that, as a result of panel conditioning (or panel attrition), those interviewed for the 

second time in the TUS-CPS may have lower rates of smoking prevalence than those interviewed for the first time. 

However, when the differences in other characteristics between Rotation Groups 1 and 2 are controlled for in Panel 

B of Table 6 by including the same set of control variables used in Panel B of Table 2, only the marginal effect of 

Rotation Group 1 in the 2003 TUS-CPS is positive and statistically significant.9 In sum, the results in Panel B of 

Table 6 provide evidence of panel conditioning (or panel attrition) for 2003. And rotation group bias observed for 

2003in Panel B of Table 2 is likely to be due to panel conditioning (or panel attrition). 

7. Conclusions 

The main goal of this paper is to examine if the sampling rotation scheme used in the CPS results in an 

underestimation of smoking prevalence in the TUS-CPS. The analysis reported in this paper showed that although 

panel attrition is prevalent in all years, for the six waves of TUS-CPS before 2003 there is no evidence that smoking 

prevalence estimates were significantly affected by the rotation scheme of the CPS. For the three waves of TUSCPS since 2003, however, the results showed that smoking prevalence has been underestimated due to panel 

attrition and panel conditioning. It appears that rotation group bias in these waves was caused by the substantially 

increased number of additional questions smokers have to answer. Therefore, one way to reduce the effect of 

rotation group bias in the future waves of TUS-CPS would be to alleviate the burden on respondents by reducing the 

number of questions for smokers. 

 

8

Another assumption in my analysis is that panel conditioning may arise from participating in the CPS and TUSCPS in consecutive months. By participating in one basic CPS, respondents may learn that their responses may 

affect the number of additional questions they need to answer and in the end, the length of the interview. So in their 

second interview in the TUS-CPS, they may manipulate a survey instrument in an effort to minimize the length of 

the survey and their burden.

9

 In their analysis of panel conditioning, Halpern-Manners and Warren (2012) restricted their samples to individuals 

who were household heads, self-respondents, and matched across their first and second months in the sample. 

However, they did not control for differences in other characteristics, including the survey mode. Considering the 

changes between Panels A and B of Table 6, their results need to be reexamined with more control variables. 
8

 

Finally, the problem of underestimating smoking prevalence due to the higher attrition of smokers in longitudinal 

data may not be limited to the TUS-CPS. It also broadly applies to any longitudinal survey data that have some 

measures of smoking, such as the Panel Study of Income Dynamics, National Longitudinal Study of Adolescent 

Health, Monitoring the Future, National Longitudinal Survey of Youth, International Tobacco Control Policy 

Evaluation Project, Health and Retirement Study, and the National Youth Smoking Cessation Survey. Therefore, 

along with survey mode and respondent type, researchers will need to consider the effect of different attrition by 

smoking status in estimating the prevalence of smoking using these longitudinal data. 

Acknowledgements 

I would like to thank Anne Hartman and Todd Gibson at the National Cancer Institute for their help in analyzing the 

TUS-CPS data. 
9

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12

Table 1 Current Smokers: Percentage Estimates and Rotation Group Indices, U.S. Household Population, 18 

Years and Over, 1992–2011 TUS-CPS 

1 

Month in sample Sample 

2 3 4 5 6 7 8 Size 

1992–93 TUS-CPS 

 Percentage estimate 

 Rotation group index 

25.03 

(0.38) 

105.39 

(1.60) 

23.63 

(0.4) 

99.49 

(1.68) 

23.31 

(0.39) 

98.15 

(1.64) 

23.37 

(0.33) 

98.40 

(1.39) 

24.35 

(0.3) 

102.53 

(1.26) 

23.11 

(0.37) 

97.31 

(1.56) 

23.84 

(0.34) 

100.38 

(1.43) 

23.32 

(0.35) 

98.19 

(1.47) 

277,703 

1995–96 TUS-CPS 

 Percentage estimate 

 Rotation group index 

24.03 

(0.36) 

104.12 

(1.56) 

23.17 

(0.36) 

100.39 

(1.56) 

22.65 

(0.35) 

98.14 

(1.52) 

23.13 

(0.35) 

100.22 

(1.52) 

23.53 

(0.36) 

101.95 

(1.56) 

23.31 

(0.36) 

101.00 

(1.56) 

22.15 

(0.34) 

95.97 

(1.47) 

22.69 

(0.36) 

98.31 

(1.56) 

233,737 

1998–99 TUS-CPS 

 Percentage estimate 

 Rotation group index 

22.71 

(0.34) 

105.97 

(1.59) 

21.64 

(0.32) 

100.98 

(1.49) 

20.73 

(0.34) 

96.73 

(1.59) 

21.43 

(0.34) 

100.00 

(1.59) 

21.97 

(0.35) 

102.52 

(1.63) 

21.10 

(0.32) 

98.46 

(1.49) 

20.73 

(0.35) 

96.73 

(1.63) 

21.17 

(0.37) 

98.79 

(1.73) 

224,902 

2000 TUS-CPS 

 Percentage estimate 

 Rotation group index 

22.09 

(0.40) 

103.08 

(1.87) 

20.96 

(0.45) 

97.81 

(2.10) 

21.37 

(0.41) 

99.72 

(1.91) 

21.30 

(0.38) 

99.39 

(1.77) 

21.91 

(0.41) 

102.24 

(1.91) 

20.82 

(0.34) 

97.15 

(1.59) 

20.89 

(0.41) 

97.48 

(1.91) 

21.32 

(0.43) 

99.49 

(2.01) 

156,764 

2001–02 TUS-CPS 

 Percentage estimate 

 Rotation group index 

21.74 

(0.35) 

106.26 

(1.71) 

20.73 

(0.34) 

101.32 

(1.66) 

19.98 

(0.29) 

97.65 

(1.42) 

20.19 

(0.4) 

98.68 

(1.96) 

21.08 

(0.31) 

103.03 

(1.52) 

20.04 

(0.35) 

97.95 

(1.71) 

19.57 

(0.33) 

95.65 

(1.61) 

20.1 

(0.43) 

98.24 

(2.10) 

234,227 

2003 TUS-CPS 

 Percentage estimate 

 Rotation group index 

 

20.17 

(0.29) 

109.74 

(1.58) 

18.59 

(0.34) 

101.14 

(1.85) 

18.16 

(0.3) 

98.80 

(1.63) 

18.05 

(0.38) 

98.20 

(2.07) 

18.39 

(0.3) 

100.05 

(1.63) 

17.84 

(0.28) 

97.06 

(1.52) 

17.79 

(0.34) 

96.79 

(1.85) 

17.5 

(0.4) 

95.21 

(2.18) 

234,274 

2006–07 TUS-CPS 

 Percentage estimate 

 Rotation group index 

19.54 

(0.31) 

110.27 

(1.75) 

17.67 

(0.29) 

99.72 

(1.64) 

17.89 

(0.32) 

100.96 

(1.81) 

17.79 

(0.38) 

100.40 

(2.14) 

17.84 

(0.33) 

100.68 

(1.86) 

17.31 

(0.32) 

97.69 

(1.81) 

16.68 

(0.31) 

94.13 

(1.75) 

16.49 

(0.34) 

93.06 

(1.92) 

227,428 

2010–11 TUS-CPS 

 Percentage estimate 

 Rotation group index 

17

(0.31) 

110.68 

(2.02) 

15.09 

(0.32) 

98.24 

(2.08) 

15.09 

(0.27) 

98.24 

(1.76) 

15.61 

(0.34) 

101.63 

(2.21) 

15.28 

(0.28) 

99.48 

(1.82) 

14.82 

(0.3) 

96.48 

(1.95) 

14.72 

(0.28) 

95.83 

(1.82) 

15.37 

(0.35) 

100.07 

(2.28) 

227,722 

Note: Standard errors are in parentheses. The results are weighted using the Tobacco Use Supplement to the Current 

Population Survey nonresponse weight (PWNRWGT) and standard errors have been estimated using the balanced 

repeated replication method. 
13

Figure 1 Rotation Group Indices for Current Smokers, 1992–2011 TUS-CPS 

80

85

90

95

100

105

110

115

1992-93 1995-96 1998-99 2000 2001-02 2003 2006-07 2010-11

Rotation group index 

MIS 1 MIS 2 MIS 3 MIS 4 MIS 5 MIS 6 MIS 7 MIS 8 

 

Note: MIS stands for Month In Sample. 
14

Table 2 Marginal Effects of Rotation Group 1 in Probit Regression on Current Smokers, U.S. Household Population, 18 Years and Over, 1992–2011 

TUS-CPS 

(1) (2) (3) (4) (5) (6) (7) (8) 

1992–93 1995–96 1998–99 2000 2001–02 2003 2006–07 2010–11

Rotation group 1 0.015*** 0.011*** 0.014*** 0.009* 0.015*** 0.020*** 0.021*** 0.019*** 

(0.004) (0.004) (0.003) (0.004) (0.004) (0.003) (0.003) (0.003) 

Observations 277,703 233,737 224,902 156,764 234,227 234,274 227,428 227,722 

Mean of the dependent variable 0.237 0.231 0.214 0.213 0.205 0.184 0.177 0.154 

Dependent variable: Current Smoker 

Panel A No Control 

Panel B Full Control 

(1) (2) (3) (4) (5) (6) (7) (8) 

1992–93 1995–96 1998–99 2000 2001–02 2003 2006–07 2010–11

Rotation group 1 -0.001 -0.001 -0.000 -0.002 0.004 0.016*** 0.011*** 0.009*** 

(0.004) (0.004) (0.004) (0.004) (0.003) (0.003) (0.003) (0.003) 

Observations 277,703 233,737 224,902 156,764 234,227 234,274 227,428 227,722 

Mean of the dependent variable 0.237 0.231 0.214 0.213 0.205 0.184 0.177 0.154 

Mean of the dependent variable 

for rotation group 1 0.250 0.240 0.227 0.221 0.217 0.202 0.195 0.170 

Difference -0.013 -0.009 -0.013 -0.008 -0.012 -0.018 -0.018 -0.016 

Note: Standard errors in parentheses. The results are weighted using the Tobacco Use Supplement to the Current Population Survey nonresponse weight 

(PWNRWGT) and standard errors have been estimated using the balanced repeated replication method. *** p<0.01, ** p<0.05, * p<0.1. 

The following control variables are included in the results presented in Panel B: three dummies for age categories (18 to 24, 25 to 44, 45 to 64; the reference 

group is age 65 or older); a female dummy; five dummies for race/ethnicity (Black, Hispanic, American Indian/Alaskan Native, Asian/Pacific Islander, and other; 

the reference group is White); three dummies for education (high school, some college, and college or more; the reference group is less than high school); two 

dummies for employment status (unemployed and not in labor force; the reference group is employed); three dummies for marital status (married, widowed, and 

divorced/separated; the reference group is never married); dummies for family income levels, including family income missing; a dummy for home owner; two 

dummies for metropolitan areas (non metropolitan area and not identified; the reference group is metropolitan area); three region dummies; dummies for TUSCPS month in each period; a dummy for self-response (the reference group is proxy response); and a dummy for personal interview (the reference group is 

telephone interview). 

 
15

Table 3 Number of Variables and Changes in the TUS-CPS Over Time 

Year

Number of 

variables Changes 

Person 

nonresponse rate 

1992–93 86 N/A 

1995–96 69 14.8; 15.6; 13.1 

1998–99 73 13.0; 15.4; 18.0 

2000 27 An abbreviated version of the TUS-CPS and consisted of several questions measuring basic tobacco use 

prevalence (cigarettes, cigars, pipes, chewing tobacco, and snuff) 

12.0; 12.9 

2001–02 75 18.4; 17.7; 16.3 

2003 240 The 2003 Tobacco Use Special Cessation Supplement (TUSCS) had several unique topics covering: 

type of cigarette usually smoked (menthol, lights); 

switching to lighter cigarettes; 

level of nicotine dependence; 

products, treatments and methods used to quit cigarette and/or other tobacco product use; 

cessation behavior for "other" non-cigarette tobacco products (cigars, pipes, chewing tobacco and 

snuff); 

use of new harm reduction products; 

specific guidance from health professionals; and 

cost of last pack/carton of cigarettes purchased and in which state purchased. 

16.4; 18.3; 16.8 

2006–07 168 A core TUS was fielded that combined some of the new features of the 2003 TUSCS-CPS with the general 

2001–02 TUS. Topics that had not previously been fielded included: 

cost and purchase of "single" cigarettes; 

smoking consumption about 12 months ago; 

awareness and use of "quitlines" and advising family and friends to quit smoking; 

use of "Marlboro Ultrasmooth", a new test-marketed tobacco product; 

asking about other non-cigarette tobacco products separately rather than combined; and 

asking about attitudes toward clean indoor air policies for children's outdoor sports fields and 

playgrounds, and indoor concert venues. 

19.3; 18.3; 14.8* 

2010–11 323 The TUS-CPS included more detailed questions than previous survey cycles on: 

menthol cigarette use; 

recent quit attempts; 

recent quitting; 

treatment and other methods used to quit; 

emerging products (dissolvables and E-cigarettes); and

attitudes toward clean indoor air policies for casinos and cars. 

17.8; 17.9; 18.4* 

Source: Author’s counting, codebooks for each of the Tobacco Use Supplement to the Current Population Survey and 

http://appliedresearch.cancer.gov/studies/tus-cps/questionnaire.html. The person nonreponse rates are listed for each month of the TUS-CPS. * The rates for 

2006–07 and 2010–11 are for those at least 18 years old. 
16

Table 4 Marginal Effects of Smoking in Probit Regression of Matching between the First Rotation Group in the TUS-CPS and the Subsequent Basic 

CPS 

Dependent variable: Successful matching 

(1) (2) (3) (4) (5) (6) (7) 

Years MIS 2 MIS 3 MIS 4 MIS 5 MIS 6 MIS 7 MIS 8 Sample size 

1992–93 -0.000 -0.009* -0.014** -0.027*** -0.021*** -0.028*** -0.025*** 34,325 

(0.005) (0.005) (0.006) (0.007) (0.007) (0.007) (0.007) 

1995–96 -0.002 -0.007 -0.008 -0.036*** -0.037*** -0.036*** -0.037*** 29,754 

(0.004) (0.005) (0.005) (0.006) (0.006) (0.005) (0.006) 

1998–99 -0.004 -0.014*** -0.010** -0.038*** -0.033*** -0.033*** -0.034*** 28,823 

(0.004) (0.004) (0.005) (0.007) (0.008) (0.008) (0.008) 

2000 0.002 -0.007 -0.002 -0.010 -0.011 -0.023** -0.021** 19,423 

(0.005) (0.006) (0.006) (0.010) (0.009) (0.009) (0.009) 

2001–02 -0.004 -0.012** -0.018*** -0.032*** -0.035*** -0.035*** -0.036*** 33,308 

(0.004) (0.005) (0.006) (0.007) (0.008) (0.008) (0.008) 

2003 0.002 -0.000 -0.009* -0.031*** -0.029*** -0.030*** -0.030*** 32,733 

(0.004) (0.005) (0.005) (0.007) (0.008) (0.008) (0.007) 

2006–07 -0.010** -0.010* -0.011** -0.026*** -0.022*** -0.023*** -0.026*** 32,293 

(0.004) (0.005) (0.005) (0.008) (0.008) (0.007) (0.008) 

2010–11 -0.008* -0.013** -0.019*** -0.037*** -0.040*** -0.038*** -0.044*** 31,874 

(0.004) (0.006) (0.005) (0.008) (0.008) (0.008) (0.008) 

Note: Standard errors in parentheses. The results are weighted using the Tobacco Use Supplement to the Current Population Survey nonresponse weight 

(PWNRWGT) and standard errors have been estimated using the balanced repeated replication method. *** p<0.01, ** p<0.05, * p<0.1. 

The following control variables are included: three dummies for age categories (18 to 24, 25 to 44, 45 to 64; the reference group is age 65 or older); a female 

dummy; five dummies for race/ethnicity (Black, Hispanic, American Indian/Alaskan Native, Asian/Pacific Islander, and other; the reference group is White); 

three dummies for education (high school, some college, and college or more; the reference group is less than high school); two dummies for employment status 

(unemployed and not in labor force; the reference group is employed); three dummies for marital status (married, widowed, and divorced/separated; the reference 

group is never married); dummies for family income levels, including family income missing; a dummy for home owner; two dummies for metropolitan areas 

(non metropolitan area and not identified; the reference group is metropolitan area); three region dummies; dummies for TUS-CPS month in each period; a 

dummy for self-response (the reference group is proxy response); a dummy for personal interview (the reference group is telephone interview); and a dummy for 

availability of a telephone in the house. 

 
17

Table 5 Marginal Effects of Smoking in Probit Regression of Matching between the First Rotation Groups in the January and May 1999 and February 

2002 TUS-CPS and Fifth Rotation Groups in the January and May 2000 and February 2003 Basic CPS and TUS-CPS 

Dependent variable: Successful matching 

(1) 

Basic CPS 

(2) 

TUS-CPS 

Sample size 

January and May 1999 -0.0309*** 

(0.0100) 

-0.0312*** 

(0.0118) 

19,058 

February 2002 -0.0547*** 

(0.0115) 

-0.0684*** 

(0.0130) 

11,027 

Note: Standard errors in parentheses. The results are weighted using the Tobacco Use Supplement to the Current Population Survey nonresponse weight 

(PWNRWGT) and standard errors have been estimated using the balanced repeated replication method. *** p<0.01, ** p<0.05, * p<0.1. 

The following control variables are included: three dummies for age categories (18 to 24, 25 to 44, 45 to 64; the reference group is age 65 or older); a female 

dummy; five dummies for race/ethnicity (Black, Hispanic, American Indian/Alaskan Native, Asian/Pacific Islander, and other; the reference group is White); 

three dummies for education (high school, some college, and college or more; the reference group is less than high school); two dummies for employment status 

(unemployed and not in labor force; the reference group is employed); three dummies for marital status (married, widowed, and divorced/separated; the reference 

group is never married); dummies for family income levels, including family income missing; a dummy for home owner; two dummies for metropolitan areas 

(non metropolitan area and not identified; the reference group is metropolitan area); three region dummies; dummies for TUS-CPS month in each period; a 

dummy for self-response (the reference group is proxy response); a dummy for personal interview (the reference group is telephone interview); and a dummy for 

availability of a telephone in the house. 

 
18

Table 6 Panel Conditioning: Marginal Effects of Rotation Group 1 in Probit Regression on Smoking, TUS-CPS Respondents Matched Across Their 

First and Second Months in Sample 

Dependent variable: Current Smoker 

Panel A No Control 

 (1) (2) (3) (4) (5) (6) (7) (8) 

1992–93 1995–96 1998–99 2000 2001–02 2003 2006–07 2010–11

Rotation group 1 0.014** 0.007 0.010** 0.014** 0.006 0.017*** 0.017*** 0.019*** 

 (0.005) (0.007) (0.005) (0.006) (0.005) (0.005) (0.004) (0.005) 

Observations 69,760 35,927 54,065 35,969 59,384 60,294 58,766 58,055 

Panel B Full Control 

 (1) (2) (3) (4) (5) (6) (7) (8) 

1992–93 1995–96 1998–99 2000 2001–02 2003 2006–07 2010–11

Rotation group 1 0.003 0.004 -0.001 0.006 -0.002 0.014** 0.006 0.010* 

 (0.006) (0.008) (0.006) (0.008) (0.006) (0.006) (0.005) (0.005) 

Observations 69,760 35,927 54,065 35,969 59,384 60,294 58,766 58,055 

Note: Standard errors in parentheses. The results are weighted using the Tobacco Use Supplement to the Current Population Survey nonresponse weight 

(PWNRWGT) and standard errors have been estimated using the balanced repeated replication method. *** p<0.01, ** p<0.05, * p<0.1. 

The following control variables are included in the results presented in Panel B: three dummies for age categories (18 to 24, 25 to 44, 45 to 64; the reference 

group is age 65 or older); a female dummy; five dummies for race/ethnicity (Black, Hispanic, American Indian/Alaskan Native, Asian/Pacific Islander, and other; 

the reference group is White); three dummies for education (high school, some college, and college or more; the reference group is less than high school); two 

dummies for employment status (unemployed and not in labor force; the reference group is employed); three dummies for marital status (married, widowed, and 

divorced/separated; the reference group is never married); dummies for family income levels, including family income missing; a dummy for home owner; two 

dummies for metropolitan areas (non metropolitan area and not identified; the reference group is metropolitan area); three region dummies; dummies for TUSCPS month in each period; a dummy for self-response (the reference group is proxy response); and a dummy for personal interview (the reference group is 

telephone interview).