Total Survey Error in the American Time Use Survey 

John Dixon and Brian Meekins, Bureau of Labor Statistics, Room 4675, 2 Massachusetts Ave, NE, Washington, DC 

20212-0001 

Dixon.john@bls.gov 

Keywords: Nonresponse, time use, bias 

Abstract 

The American Time Use Survey (ATUS) is the first continuous, Federally-funded survey designed to measure 

how people spend their time. The ATUS sample is drawn from households completing their final month of 

interviews for the Current Population Survey (CPS). Because the CPS records contain a wealth of demographic 

information about respondents, this design enables us to look directly at nonresponse without having to rely on 

techniques such as data matching or the use of reluctant respondents to model nonrespondents. The Contact History 

Instrument (CHI) from the CPS describes concerns about responding to the CPS, which may relate to responding in 

the ATUS. Our paper focuses on nonresponse bias and measurement error. First, we describe nonresponse rates by 

demographic characteristics, and then we use logistic analysis to examine correlates of nonresponse, including 

demographic and contact history characteristics. A propensity score model is utilized to examine differences in timeuse patterns and to assess the extent of nonresponse bias. Measurement error is assessed with indicators based on 

item nonresponse and interviewer judgement. 

Introduction 

The American Time Use Survey (ATUS) is the first continuous, Federally-funded survey designed to measure 

people’s daily activities, including where they spend their time, what they spend their time doing, and with whom 

they spend their time. The ATUS is a one-time telephone interview with three main components: (1) questions 

updating the designated person’s (DP)1

 employment status, industry and occupation, and earnings information from 

the CPS, (2) a 24-hour time diary, and (3) additional information on secondary childcare, paid work, volunteering, 

and travel away from home. The ATUS sample is drawn from households that have completed the entire CPS 

interview rotation of eight interviews over a 16-month period. Once a CPS household is selected, one household 

member is randomly selected to participate in the ATUS interview. Substitution or proxy response is not allowed. 

The selected DP must be 15 years old or older and may or may not have been the CPS reference person. Each DP is 

also required to report on a pre-assigned reporting day of the week—such as Tuesday, reporting about Monday. The 

specific day of the week assigned to each DP does not change, and there is no substitution of this day. The 

interviewing period for a case is up to eight weeks on the assigned day to secure an ATUS interview. 

Design 

The ATUS is a computer assisted telephone survey conducted by the U.S. Census Bureau for the U.S. Bureau of 

Labor Statistics. Production began in January 2003. In 2010, approximately 2,000 participants were selected each 

month, and the average ATUS response rate was 57 percent.2 

Key estimates of interest are the time-use patterns of the general population. All activities are classified into a threetiered, hierarchical system, with 17 major, or first-tier, categories, each having two additional sub-levels of detail. 

The 17 first-tier categories include: personal care; household activities; caring for and helping household members; 

caring for and helping non-household members; work and work-related activities; education; consumer purchases; 

professional and personal care services; household services; government services and civic obligations; eating and 

drinking; socializing, relaxing, and leisure; sports, exercise, and recreation; religious activities; volunteering; 

telephone calls; and travel. 

Analysis 

The difference between respondents and nonrespondents on key estimates of interest is usually unknown. 

Therefore, nonresponse bias typically must be examined using indirect measures that assume certain types of 

respondents can serve as accurate proxies for nonrespondents. However, since the ATUS draws its sample from 

respondents who have completed their final CPS interview, a direct comparison between respondents and 

nonrespondents on the ATUS is possible.3

 This paper focuses on nonresponse bias and measurement error. First, we 

investigate nonresponse bias from CPS nonresponse, then nonresponse bias due to ATUS nonresponse. 

1

 A designated person is the household member selected for ATUS. 

2

 The response rate was calculated using the AAPOR Response Rate #2. 

3

 In 2010, the CPS response rate was 92 percent. 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

We use logistic analysis (SAS PROC SURVEYLOGISTIC) to examine correlates of nonresponse, including 

both refusals and noncontacts. The ATUS nonresponse model was based on the characteristics of both refusals and 

noncontacts in the CPS and contains nine predictor variables and five interaction variables based on models 

developed by Dixon and Tucker (2000) as well as summary variables from the CHI. In the Dixon and Tucker study, 

variables were selected based on a theory of nonresponse (Groves and Couper, 1998) and effects found in previous 

studies. The independent predictors include: age, race, ethnicity, presence of children under 6 years old in the 

household, marital status, household size, number of relatives in the household other than spouse, school enrollment, 

and number of attempted interview contacts. The interactions include age by relative present in the household, 

ethnicity by household size, presence of child under 6 by household size, household size by school enrollment, and 

presence of relatives by race. 

Using the predicted values from the model described above and in Table 2, a propensity score model is then used to 

examine differences in time-use patterns and to assess the extent of nonresponse bias. A propensity score model 

allows us to predict the probability of group membership. In this case, we use CPS variables to predict the 

probability of respondents versus nonrespondents for the CPS and the ATUS separately. The propensity scores were 

categorized as refusals and noncontacts in the same proportions as the real refusals and noncontacts in the surveys. 

While we cannot know the nonrespondents’ true time-use estimates, we can estimate the bias due to nonresponse by 

contrasting the time-use estimates for those responders who were most similar to the nonrespondents based on 

propensity scores (Dixon 2004). A similar analysis was repeated for noncontacts. Measurement error may have an 

impact on data quality as well. Bose and Sharp (2005) and Meekins, Downey, and Fricker (2010) studied a number 

of sources of error in reporting for the ATUS. For this study, we will use non-reporting of income in the CPS as an 

indicator of privacy concerns, and the quality concerns reported by the interviewer for likely response error due to 

memory failure or willingness to provide quality data. 

Results 

Refusal 

Table 1 shows the results of a logistic regression; the refusal model of the ATUS includes ten independent 

variables as predictors and five interaction terms and has a rescaled R-square of 0.3077. The statistically significant 

predictors are; Hispanic origin, children, marital status, household size, relatives present, in school, and the 

interaction of age by relatives present, children by family size, family size by “in school”, and race(White) by 

relatives present in the household. Specifically, White respondents are less likely to refuse, while married and older 

respondents are more likely to refuse. The significant interaction shows that White respondents with relatives 

present are more likely to refuse. The concerns expressed in the CPS CHI and the number of attempted contacts 

were also significant. 

Each DP, (respondents only), is assigned a “refusal propensity score,” which is then used to create the same 

proportion of "refusers" that existed in the sample. Then, those respondents most like the refusers are compared to 

those who are most unlike the refusers. This was done with the logistic model (Table 1) by using the predicted 

values to classify those who responded to the ATUS into two groups, those most like refusers and those least like 

refusers. This provided an indication of the direction and magnitude of bias in the time-use estimates. Similar 

models were used to predict ATUS noncontact (Table 2) as well as CPS refusal and CPS noncontact. Groups 

proportionate to their respective sizes from the CPS and ATUS were created from those propensity scores. 

Noncontact 

The noncontact model uses the same variables as the refusal model to permit easier comparison between models 

(Table 2.) If different variables are used, the relative importance of the different variables would not be seen. This is 

because the variables left out of the model would get a nonsignificant weight in the model and would not affect the 

propensity scores. The noncontact model has a rescaled R-square of 0.1858. The best predictors of noncontact 

propensity are marital status, age and the interaction of age and relatives present, and number of attempted contacts. 

Specifically, white respondents and older respondents were less likely to be noncontacts (since their logistic model 

estimates are negative), and households with more attempted contacts are more likely to be noncontacts (which is 

obvious, but helps estimate the likelihood of contact for those who were eventually contacted). The relative bias for 

each ATUS measure was calculated as the difference between the estimate for the total sample and those who were 

most like those who responded, divided by the total sample (Groves and Peytcheva,2008). 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

                     

 Nonresponse bias and Measurement error 

Table 3 shows the standardized estimates of nonresponse bias and measurement error, using different indicators. 

This is the difference between the survey estimates and what the estimates would be with those like the 

nonresponders (or measurement errors) removed, divided by the estimates standard error (from SAS PROC 

SURVEYMEANS). This gives the estimate of bias relative to its’ precision of estimation. The table is sorted by the 

average length of the activity. For the total activities measure, the CPS refusal is low (-0.00634) standard errors 

relative to the other sources. The CPS nonresponse sources are negative, while the ATUS sources are positive, 

which may counterbalance the effect. The measurement error indicators are also in opposite directions, although 

those missing income had a stronger effect. 

Table 4 shows the relative error for nonresponse bias and measurement error. The difference between survey 

estimates and the estimates without those like the nonresponders (or measurement errors) removed, divided by the 

estimates. This gives the bias relative to the scale of the estimate. For example, the bias for sleep could be 

compared to the bias for food preparation. Table 5 shows the relationship between nonresponse and measurement 

error. There was only weak correlation, with the strongest from “no income” with the nonresponse indicators, but in 

opposite directions for CPS and ATUS. 

Discussion 

The estimates of bias were very small from all sources. For overall error, ATUS noncontact had the largest 

effect; 0.34706 of a standard error, or .01 percent relative error (about 1 and a half minutes out of 1434). Of the top 

three categories, only work showed much bias (approximately 10 minutes out to 205). The direction of bias was 

opposite for the CPS indicators and the ATUS indicators, with CPS underestimating work time and ATUS 

overestimating it. The ATUS indicators were three times as large as the CPS indicators. The measurement error 

indicators were in the same direction as the ATUS nonresponse, so the overall effect would be to overestimate work 

time. Socializing had overall overestimation for all sources of nonresponse, and underestimation from measurement 

error sources. Sleep was in the opposite direction from “work” for nonresponse bias. Abraham et.al. discussed 

“volunteering” in terms of overestimating, since those who spend more time volunteering may be more likely to 

participate in the survey. This study found the same effect from all the nonresponse sources. The largest effect was 

due to “education”, underestimating the time by as 8 or 9 minutes based on ATUS nonresponse. This may be due to 

those in school less likely to refuse, and so reducing the total education time (relative to those who had already 

completed school). The largest effects for CPS refusal are “traveling” and “personal” underestimation. Those who 

are like those who refuse the CPS spend more time on those activities. They spent slightly more time on “sports”. 

Some of the smallest time use categories had large estimates of bias, but they were so small they are likely to be 

unreliable. The category “household health care- other” was consistent in underestimating for refusal and 

overestimating for non-contact, so there might be some concern there. Child care showed small effects for CPS 

nonresponse, but larger effects for ATUS nonresponse with overestimation. This may be due to child care providers 

being more available and willing to participate, as seen in the propensity models. ATUS nonresponse had higher 

estimates of bias, particularly non-contact. The biases were usually overestimation for most categories, but 

underestimation for “sleep”, “education”, “sports”, and “telephone calls”. For measurement error, many of the 

estimates were as large as for nonresponse, but the effect on the estimates is much harder to justify. In this study we 

assume the effects are proportionate for privacy concerns and quality concerns from the interviewer, but there 

haven’t been any studies to calibrate the effect the way there has been with nonresponse. 

Limitations and Future Work 

There are several limitations to the current research. First, the assumption that the propensity model represents 

nonresponse needs to be examined with other models. Second, many of the time-use categories had non-normal 

distributions and would have been better analyzed using a model that did not assume a normal distribution. Next, 

due to the wide confidence intervals associated with small proportions, the bias model for time categories with small 

time amounts, like volunteering, did not provide as good a fit as in categories with large time values. Future studies 

should focus on better evaluations for categories with smaller time amounts. The current study adjusted for the fit 

of the nonresponse propensity models to provide some measure of the bias adjusting for nonresponse. If the models 

fit more poorly, the intervals would have been wider. Other estimates adjusting for the variability of each of the 

time use categories would also give wider confidence intervals. 

While the differences due to nonresponse in mean times for activities are of interest, the trade-offs between 

activities is also of interest. To better understand bias, subgroups of interest, such as those living in households with 

young children, should also be modeled separately. More indicators of measurement error should be used, 

particularly comparisons with CPS estimates. More link functions need to be explored to better match the
     

 

 

 

 

 

 

 

distributions, particularly the zeros. Multivariate methods (seamingly unrelated regressions or structural equation 

models) may help describe the patterns of bias more clearly. 

References: 

Abraham, K., Maitland, A., and Bianchi, S., “Nonresponse in the American Time Use Survey: Who is missing from 

the data and how much does it matter?”, Public Opinion Quarterly, 70(5), 2006. 

Dixon, J., Using Census Match Data to Evaluate Models of Survey Nonresponse, A paper presented at the Joint 

Statistical Meetings, Toronto, 2004. 

Dixon, J. and Tucker, C., Modeling Household and Interviewer Nonresponse Rates from Household and Regional 

Characteristics. A paper presented at the International Workshop on Household Survey Nonresponse, Budapest, 

September, 2000. http://www.fcsm.gov/committees/ihsng/buda5.pdf 

Fricker, S. and Tourangeau, R., “Examining the Relationship Between Nonresponse Propensity and Data Quality in 

Two National Household Surveys”, Public Opinion Quarterly. Volume 74, No. 5/December 2010. 

Groves, R. and Couper, M., Nonresponse in Household Interview Surveys, 1998, New York: Wiley. 

Groves, R. and Peytcheva, E., “The impact of Nonresponse Rates on Nonresponse Bias: A Meta-Analysis”, Public 

Opinion Quarterly, 72 (2): 167-189, 2008. 

Meekins, B., Downey, K., and Fricker, S., “Errors in Reporting of Activities on the American Time Use Survey”, 

paper presented at the International Total Survey Error Workshop, June, 2010. 

O’Neill, G. and Dixon, J., “Nonresponse bias in the American Time Use Survey”, paper presented at the Joint 

Statistical Meetings, 2005. 
Table 1: ATUS Refusal model

 Model Fit Statistics 

Intercept

Intercept and 

Criterion Only Covariates 

AIC 33730.232 26817.033 

SC 33738.526 26949.734 

-2 Log L 33728.232 26785.033 

R-Square 0.2094 Max-rescaled R-Square 0.3077 

Testing Global Null Hypothesis: BETA=0 

Test Chi-Square DF Pr > ChiSq 

Likelihood Ratio 6943.1989 15 <.0001 

Score 6275.1312 15 <.0001 

Wald 4995.0898 15 <.0001 

Analysis of Maximum Likelihood Estimates 

Standard Wald 

Parameter DF Estimate Error Chi-Square Pr > ChiSq 

Intercept

nrfact 

1 

1 

-1.1060 

1.9181 

0.1258 

0.2323 

77.3448 

68.1942 

<.0001 

<.0001 

modage

modhsp

modkid 

1 

1 

1 

-0.00223 

-0.2417 

-1.6281 

0.00192 

0.0931 

0.0947 

1.3498 

6.7358 

295.7850 

0.2453 

0.0094 

<.0001 

modmar 1 1.2431 0.0410 917.4167 <.0001 

modnum 1 -0.0548 0.0178 9.4163 0.0022 

modrel 1 3.3415 0.1364 600.3804 <.0001 

modsch 1 -0.8294 0.1467 31.9656 <.0001 

modwht 1 -0.0652 0.0773 0.7114 0.3990 

agerel

hspnum

kidnum 

1 

1 

1 

-0.0914 

0.0220 

0.1510 

0.00234 

0.0277 

0.0252 

1529.0785 

0.6314 

35.9776 

<.0001 

0.4268 

<.0001 

numsch 1 0.2550 0.0448 32.4241 <.0001 

relwht 1 0.0184 0.0886 0.0432 0.8354 

cnt 1 0.1491 0.0243 37.7901 <.0001 
Table          2: ATUS Noncontact model

 Model Fit Statistics 

Intercept

Intercept and 

Criterion Only Covariates 

AIC 20842.095 17874.903 

SC 20850.491 18009.236 

-2 Log L 20840.095 17842.903 

R-Square 0.0875 Max-rescaled R-Square 0.1858 

Testing Global Null Hypothesis: BETA=0 

Test Chi-Square DF Pr > ChiSq 

Likelihood Ratio 2997.1923 15 <.0001 

Score 2867.3138 15 <.0001 

Wald 2412.6254 15 <.0001 

Analysis of Maximum Likelihood Estimates 

Standard Wald 

Parameter DF Estimate Error Chi-Square Pr > ChiSq 

Intercept

nnfact 

1 

1 

-0.7601 

1.0481 

0.1711 

0.3092 

19.7312 

11.4896 

<.0001 

0.0007 

modage

modhsp

modkid 

1 

1 

1 

-0.0327 

0.1940 

-0.7189 

0.00303 

0.1129 

0.1094 

115.8767 

2.9502 

43.2072 

<.0001 

0.0859 

<.0001 

modmar 1 1.0250 0.0590 302.2942 <.0001 

modnum 1 -0.00926 0.0223 0.1722 0.6781 

modrel 1 0.8804 0.1775 24.6070 <.0001 

modsch 1 -0.1773 0.1653 1.1505 0.2834 

modwht 1 -0.3745 0.1117 11.2394 0.0008 

agerel

hspnum

kidnum 

1 

1 

1 

-0.0408 

-0.0279 

0.0550 

0.00346 

0.0334 

0.0297 

139.2758 

0.6983 

3.4381 

<.0001 

0.4033 

0.0637 

numsch 1 0.0663 0.0496 1.7886 0.1811 

relwht 1 -0.0650 0.1221 0.2835 0.5944 

cnt 1 0.2401 0.0268 80.5744 <.0001 
                      

                       

           

               

           

           

                   

       

                     

                 

           

                   

           

       

           

       

               

           

                       

           

           

           

        

                 

         

                   

                 

         

                 

                 

               

                   

                 

         

         

                 

             

                 

             

             

                 

                 

                 

         

              

Table 3. Standardized error based on propensity models and measurement error indicators.

VarName mean(se) CPS ref CPS nc ATUS ref ATUS nc Income quality

Total 1434.349373(0.429733)  ‐0.00634  ‐0.31017 0.20610 0.34706  ‐0.27024 0.06808

Sleep 513.261979(1.749497)  ‐0.28334  ‐0.10668 1.27051 1.20539  ‐0.11525 0.09101

Socializing 269.590682(2.866224)  ‐0.62015  ‐0.38357  ‐0.13482  ‐0.14722 0.97496 0.18441

Work 205.073491(3.590434) 0.36012 0.41445  ‐1.24162  ‐1.36072  ‐0.28730  ‐0.25724

Traveling 72.379727(1.027571) 0.73809 0.11839 0.17373 0.29382  ‐0.46285  ‐0.16278

Eating 67.587840(0.730269)  ‐0.39656  ‐0.20350  ‐1.00318  ‐1.29477 0.80975  ‐0.08807

Personal 46.331073(1.333924) 0.40965 0.02093  ‐0.00373 0.12341 0.01762 0.08313

Activities 42.529503(1.055025)  ‐0.39201  ‐0.18261 0.96375 1.28989 0.23030 0.02180

Hhtasks 41.829792(1.257063)  ‐0.07690  ‐0.21840  ‐0.90910  ‐1.46067 1.21300 0.03169

Groom 39.835070(0.474536) 0.40836 0.24648 1.01235 1.56389  ‐0.50777  ‐0.05444

Housework 36.068538(1.029151) 0.14042 0.08188  ‐1.39424  ‐1.29502  ‐0.40938  ‐0.05101

Food 35.496786(0.764786)  ‐0.05992  ‐0.04040  ‐1.88065  ‐2.13662 0.00803  ‐0.03047

Hhchild 32.782657(0.910129) 0.16845 0.09169  ‐1.45998  ‐1.44397  ‐2.33673  ‐0.17759

hhcare_child 29.288916(0.834368) 0.21721  ‐0.00582  ‐1.46947  ‐1.37716  ‐2.08323  ‐0.14853

buyservice 28.800984(0.738385) 0.15148 0.05526  ‐0.99988  ‐1.10156 1.15518  ‐0.15511

purchase 22.528956(0.620087) 0.11538  ‐0.05581  ‐0.82688  ‐0.91969 1.01960  ‐0.12798

education 18.776018(1.314530) 0.08536 0.20614 6.44868 7.09043  ‐1.38272 0.23704

uncoded 18.737085(0.813104)  ‐0.04300  ‐0.28104  ‐0.08324 0.14770  ‐0.62886 0.45087

sports 18.242611(0.651214)  ‐0.57810  ‐0.09716 2.06266 2.09597  ‐0.64388  ‐0.06333

lawn 12.996173(0.763881)  ‐0.03792  ‐0.25048  ‐0.73285  ‐0.95579 1.17388 0.19315

hh_management 12.074965(0.503313)  ‐0.02086  ‐0.07129  ‐1.15933  ‐1.05185 0.54568  ‐0.04071

religious 9.701637(0.542893) 0.23845  ‐0.15174  ‐0.46905  ‐0.19056 0.66749 0.26640

volunteer 8.639446(0.524938)  ‐0.10874  ‐0.09742  ‐0.67198  ‐0.76503 0.41417  ‐0.09287

telcall 5.945809(0.311095)  ‐0.35194 0.01327 0.90307 1.61040 0.26516  ‐0.10167

health 5.815100(1.297034) 0.19855  ‐0.04893  ‐0.34023  ‐0.42024 0.20247 0.03448

pets 5.539110(0.412736)  ‐0.21236  ‐0.08813  ‐0.57986  ‐0.24342 0.45665  ‐0.11621

professional 4.990976(0.339517) 0.06809 0.09020  ‐0.51396  ‐0.65206 0.50365  ‐0.12911

tnhhadult 4.572612(0.404680) 0.00720 0.31212  ‐0.09287  ‐0.22371 0.56313  ‐0.06238

tnhhchild 4.292052(0.512466) 0.12035  ‐0.22113  ‐0.00994 0.06654 0.70496  ‐0.03114

nhhcare_child 4.075828(0.504031) 0.12060  ‐0.21433 0.00811 0.10124 0.75016  ‐0.02700

int_repair 4.049268(0.410666)  ‐0.14610 0.05980  ‐0.23791  ‐0.77576 0.47087 0.04252

ext_repair 3.354132(0.313338)  ‐0.18478  ‐0.12719  ‐0.73464  ‐0.86202 0.40359  ‐0.05876

hheduc_child 2.904238(0.202429)  ‐0.12935 0.42121  ‐0.53405  ‐0.81623  ‐1.28292  ‐0.15569

vehicle 2.498088(0.504304)  ‐0.05273 0.00413 1.30844 0.44854  ‐0.40013  ‐0.04760

thhadult 1.839136(0.257434)  ‐0.12611 0.12682  ‐0.45071  ‐0.51383  ‐0.20560 0.03557

appliance 1.303314(0.234260) 0.74413 0.00227  ‐0.38689  ‐0.47776 0.21455  ‐0.06038

hhcare_adult 0.963780(0.221390)  ‐0.10056  ‐0.04049  ‐0.32277  ‐0.39540 0.17394 0.04955

hh_services 0.757084(0.111084)  ‐0.10429  ‐0.05574  ‐0.06382  ‐0.12739 0.27610 0.12918

personal 0.680903(0.147162) 0.64646  ‐0.17388  ‐0.29957  ‐0.22035 0.01254 0.62515

hhhlth_child 0.589504(0.110366)  ‐0.01575 0.02758 0.04909 0.00077  ‐1.16741  ‐0.05603

gvment 0.523967(0.116575) 0.24681 0.43732  ‐0.37719  ‐0.06476 0.16345  ‐0.04878

hhcare_other 0.063610(0.058618)  ‐0.12253 0.47493  ‐0.10911  ‐0.13015 0.28718  ‐0.01178

hh_other 0.014740(0.010877) 0.05695 0.15915  ‐0.13625  ‐0.16253  ‐0.30454  ‐0.01471
                   

                        

               

                   

                     

               

               

                   

                     

                     

                     

               

               

                   

               

                

                   

                   

                   

                   

               

                     

                    

                       

                     

                     

                       

                     

                     

                     

                     

                     

                       

                     

                 

                 

                     

                     

                       

                       

                       

                 

                     

                     

                 

Table 4. Relative error based on propensity models and measurement error indicators.

VarName total(se) CPS_ref CPS_nc ATUS_ref ATUS_nc No income Quality

Total 1434.349(0.430) ‐0.000 ‐0.009 00.006 00.010  ‐0.008 00.002

Sleep 513.262(1.749) ‐0.097 ‐0.036 00.433 00.411  ‐0.039 00.031

Socializing 269.591(2.866) ‐0.659 ‐0.408  ‐0.143 ‐0.157 01.037 00.196

Work 205.073(3.590) 00.631 00.726  ‐2.174 ‐2.382  ‐0.503  ‐0.450

Traveling 72.380(1.028) 01.048 00.168 00.247 00.417  ‐0.657  ‐0.231

Eating 67.588(0.730) ‐0.428 ‐0.220  ‐1.084 ‐1.399 00.875  ‐0.095

Personal 46.331(1.334) 01.179 00.060  ‐0.011 00.355 00.051 00.239

Activities 42.530(1.055) ‐0.972 ‐0.453 02.391 03.200 00.571 00.054

Hhtasks 41.830(1.257) ‐0.231 ‐0.656  ‐2.732 ‐4.390 03.645 00.095

Groom 39.835(0.475) 00.486 00.294 01.206 01.863  ‐0.605  ‐0.065

Housework 36.069(1.029) 00.401 00.234  ‐3.978 ‐3.695  ‐1.168  ‐0.146

Food 35.497(0.765) ‐0.129 ‐0.087  ‐4.052 ‐4.603 00.017  ‐0.066

Hhchild 32.783(0.910) 00.468 00.255  ‐4.053 ‐4.009  ‐6.487  ‐0.493

Hhcare_child 29.289(0.834) 00.619 ‐0.017  ‐4.186 ‐3.923  ‐5.935  ‐0.423

Buyservice 28.801(0.738) 00.388 00.142  ‐2.563 ‐2.824 02.962  ‐0.398

purchase 22.529(0.620) 00.318 ‐0.154  ‐2.276 ‐2.531 02.806  ‐0.352

education 18.776(1.315) 00.598 01.443 45.148 49.641  ‐9.681 01.660

uncoded 18.737(0.813) ‐0.187 ‐1.220  ‐0.361 00.641  ‐2.729 01.957

sports 18.243(0.651) ‐2.064 ‐0.347 07.363 07.482  ‐2.298  ‐0.226

lawn 12.996(0.764) ‐0.223 ‐1.472  ‐4.308 ‐5.618 06.900 01.135

hh_management 12.075(0.503) ‐0.087 ‐0.297  ‐4.832 ‐4.384 02.275  ‐0.170

religious 9.702(0.543) 01.334 ‐0.849  ‐2.625 ‐1.066 03.735 01.491

volunteer 8.639(0.525) ‐0.661 ‐0.592  ‐4.083 ‐4.648 02.517  ‐0.564

telcall 5.946(0.311) ‐1.841 00.069 04.725 08.426 01.387  ‐0.532

health 5.815(1.297) 04.429 ‐1.091  ‐7.589 ‐9.373 04.516 00.769

pets 5.539(0.413) ‐1.582 ‐0.657  ‐4.321 ‐1.814 03.403  ‐0.866

professional 4.991(0.340) 00.463 00.614  ‐3.496 ‐4.436 03.426  ‐0.878

nhhadult 4.573(0.405) 00.064 02.762  ‐0.822 ‐1.980 04.984  ‐0.552

nhhchild 4.292(0.512) 01.437 ‐2.640  ‐0.119 00.794 08.417  ‐0.372

nhhcare_child 4.076(0.504) 01.491 ‐2.650 00.100 01.252 09.277  ‐0.334

int_repair 4.049(0.411) ‐1.482 00.606  ‐2.413 ‐7.868 04.775 00.431

ext_repair 3.354(0.313) ‐1.726 ‐1.188  ‐6.863 ‐8.053 03.770  ‐0.549

hheduc_child 2.904(0.202) ‐0.902 02.936  ‐3.722 ‐5.689  ‐8.942  ‐1.085

vehicle 2.498(0.504) ‐1.064 00.083 26.414 09.055  ‐8.078  ‐0.961

hhadult 1.839(0.257) ‐1.765 01.775  ‐6.309 ‐7.192  ‐2.878 00.498

appliance 1.303(0.234) 13.375 00.041  ‐6.954 ‐8.587 03.856  ‐1.085

hhcare_adult 0.964(0.221) ‐2.310 ‐0.930  ‐7.414 ‐9.083 03.996 01.138

hh_services 0.757(0.111) ‐1.530 ‐0.818  ‐0.936 ‐1.869 04.051 01.895

personal 0.681(0.147) 13.972 ‐3.758  ‐6.475 ‐4.762 00.271 13.511

hhhlth_child 0.590(0.110) ‐0.295 00.516 00.919 00.014  ‐21.86  ‐1.049

gvment 0.524(0.117) 05.491 09.730  ‐8.392 ‐1.441 03.637  ‐1.085

hhcare_other 0.064(0.059) ‐11.29 43.766  ‐10.05 ‐11.99 26.464  ‐1.085

hh_other 0.015(0.011) 04.202 11.744  ‐10.05 ‐11.99  ‐22.47  ‐1.085
Table 5. Nonresponse Propensity and Measurement Error. 

Variable No Income Interviewer 

CPS refusal 0.041 -0.0077 

CPS noncontact 0.067 0.0038 

ATUS refusal -0.0936 -0.0100 

ATUS noncontact -0.0750 -0.0138