Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of Labor 

Statistics.

Using Call Records to Study Sources of Nonresponse Bias in Consumer Expenditure Surveys.

John Dixon and Eric Figueroa, Bureau of Labor Statistics, Room 1950, 2 Massachusetts Ave., NE, 

Washington, DC 20212-0001

Dixon_J@bls.gov

Keywords: “Survey nonresponse, Gross Flows, Expenditure, Compositional Analysis”

Introduction

 The Consumer Expenditure Surveys 

(Quarterly and Diary) are household surveys 

which provide part of the “market basket” of 

consumer expenditures which are the basis of the 

CPI as well as other indices. Selected housing 

units of the Quarterly remain in sample during a 

5 quarter period. The households are 

interviewed for 5 consecutive quarters. These 

interviews are referred to as “time-in-sample” 

(TIS) 1 to 5. The Diary households are 

interviewed for each of two consecutive weeks.

 Matching households between times allows 

an analysis of the relationship between 

nonresponse and estimates of the proportions of 

expenditures. Since change in expenditures may 

be related to the household’s participation, the 

estimates of the “market basket” may be 

affected. Recent studies of a different survey by 

Tucker and Kojetin (1997) and Dixon (2001) 

showed that unemployment rates were related to 

nonresponse in the CPS. “Converts” 

(households that do not participate in the prior 

month) do not completely make up for the 

number of “Attriters” (households that do not 

participate in the following month), so their 

relative effect may not be offset. Moreover, they 

may differ on important characteristics, e.g.; 

race, age, or gender. A related study (Dixon, 

2002), was extended for the current study. The 

current study examines the nature of this 

relationship through an analysis of call records 

and, demographics and nonresponse and their 

resulting effect on estimates of the proportions of 

expenditures in the Consumer Expenditure 

Surveys. 

Gross Flows

 In this study “gross flows” uses the 

availability of information on one time to 

contrast the estimates from another time. For 

example, expenditure estimates in quarter 2 are 

contrasted based on whether a household 

responded in quarter 3, and similarly, quarter 3 

estimates are contrasted based on whether a 

household responded in quarter 2. This allows 

an examination of the effect of “attrition” and 

“conversion”. For example; if the expenditure 

pattern for quarter 2 is different for households 

who continued to respond in quarter 3 compared 

to those who did not respond, and this was not 

balanced by a difference in the other direction 

for estimates from quarter 3 who did not respond 

in quarter 2, then some the estimates would be 

biased due to nonresponse.

Design

 The CEQ is a household expenditure survey 

for the United States conducted by the U.S. 

Census Bureau for the U.S. Bureau of Labor 

Statistics. Approximately 8,910 eligible 

addresses are sampled each quarter, with 6,160 

completed interviews typical. Households (or 

more correctly: consumer units) were matched 

for the years 1997 through 1999. The response 

rate is usually in the range of 80-83 percent. In 

this study 5112 households were matched across 

the 5 quarters, although only times 2 and 3 are 

used here.

 The measures of consumer expenditures for 

the Quarterly are divided up into 12 categories: 

Housing, Food, Transportation, Personal 

Insurance, Entertainment, Apparel, Education, 

Tobacco, Personal Care, Miscellaneous, 

Alcoholic Beverages, and Reading. Medical 

expenditures were left out of the analysis.

 The Diary consists of two panels recording 

expenditures for each of two consecutive weeks. 

The sample is typically 6300 households. 

Compared to the Quarterly, the Diary has shorter 

memory demand and is easier to use receipts. 

The Diary emphasizes small items: Clothes, 

Meals eaten out, Food (grocery items), other. 

The nonresponse rate (type A) is often over 20%. 

The measures of consumer expenditures for the 

Diary are divided up into Food, Apparel, 

Miscellaneous, and Other.

Analysis

 The estimates used are based on the 

proportions of expenditures for a household. 

Since the proportions add up to 100%, the data is 

of a “compositional” nature (Aitchison, 1986). 

The centered log transform is used with these 

data to make the assumptions of linear models 

more realistic. The analysis begins with a 

multivariate analysis of variance contrasting two 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics.

time periods; households which consistently 

responded to those which had nonresponse in the 

prior or subsequent time. Univariate analyses 

examine which consumer categories contributed 

to the overall test. The nonresponse is further 

broken into refusal and noncontact, attrition and 

conversion, and the analyses repeated. The type 

of nonresponse is indicated by “II” for 

respondents in both times, “IR” for respondents 

who subsequently refused (attrition), “IN” 

(noncontact attrition), “RI” (refusal conversion), 

and “NI” (noncontact conversion). Covariates 

and interactions are added to the model to see 

what household characteristics may be related to 

bias effects. Call records are the covariates of 

principle interest. 

Results

Quarterly survey

 Table 1 in Appendix A shows the mean 

proportions for the CEQ by those who were 

interviewed compared to those who were 

converted from the 2nd quarter and those who 

dropped out in the 3rd quarter. The overall

MANOVA was significant (p<.0001) indicating 

that the pattern of expenditures was different for 

the nonresponders compared to the responders. 

The MANOVA and the univariate ANOVAs 

were based on the centered logs, but the table 

shows the mean proportions for ease of 

interpretation. The “housing” category showed 

the largest effect, with nonresponders having 

higher expenditures. Other categories which had 

higher expenditures were “food” and “apparel”. 

This was counterbalanced by lower expenditures 

in “personal insurance”, and “miscellaneous”. 

Diary Survey

 Table 12 in Appendix B shows the mean 

proportions for the CED by those who were 

interviewed compared to those who were not 

responders for the first or second diary. The 

overall MANOVA was significant (p<.0001). 

The nonresponders spent proportionately more 

on clothing. While not significant, they also 

spent more on meals and food and less on other 

expenses.

Household_and Interview Characteristics

Quarterly survey

 Consumer unit size, respondent age, 

expenditure amount, tenure, number of children, 

respondent gender, race, population density, 

respondent education, and multi-unit structure 

were examined in a series of 12 MANOVAs. 

The interview characteristics (mostly from call 

records were; interview length, number of phone 

calls, number of trips, total number of contacts, 

travel time, type of call records, phone or visit to 

collect data, phone for visit to schedule data 

collection, and phone or visit for other purposes. 

These were analyzed with an additional 12 

MANOVAs. The results for nonresponse can be 

seen in Tables 3 and 4. All of the covariates 

were related to expenditure patterns (the 

covariate effect in Tables 3 and 4, but only those 

which affected interview status either through an 

interaction or by making the interview status 

non-significant (suppressor effect) will be 

discussed in this paper. 

 Total expenditures interacted with interview 

status. Nonresponding households with higher 

total expenditures had relatively higher food and 

tobacco expenditures, while alcohol, education 

and personal insurance were relatively lower 

(Figures 1 through 5). NOTE: figures and tables 

are available in the complete paper.

 Education level of the respondent interacted 

with interview status. Higher educated 

respondents spent relatively less on personal care 

and relatively more on personal insurance than 

nonrespondents (figure 6 and 7).

 The age of the respondent interacted with 

interview status in terms of higher age 

nonresponders spending less on apparel, reading 

material and relatively more on education and 

personal insurance (figures 8-11).

 Family income interacted with interview 

status in terms of higher income families who 

didn't respond spent less on education, 

miscellaneous and more on tobacco and personal 

insurance (figures 12-14).

 Urban/Rural interacted with interview status 

for nonresponding rural households having 

relatively higher alcohol and personal care 

expenditures, with lower apparel expenditures. 

Overall rural had lower expenditures (Table 5).

 Multiple unit structures (such as apartments) 

interacted with interview status for 

nonresponding households in multiunit structures 

having relatively higher expenditures for apparel 

and reading, and relatively lower for housing. 

Overall, multiunit households also had lower 

housing expenditures (Table 6).

 Homeowners spent more on entertainment 

and miscellaneous. Ownership interacted with 

interview status such that for nonresponding 

households which owned their home spent 

relatively less on apparel and miscellaneous, but 

more on food, housing, and entertainment (Table 

7) compared to responders.

 The length of interview interacted with 

nonresponse with longer interviews for 

responding households associated with relatively 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics.

lower expenditures on apparel (figure 16). 

Households which needed more visits to 

schedule data collection and responded spent 

relatively more on transportation and less on 

personal insurance (figures 17 and 18). 

Diary Survey

 The MANOVAs for the diary were the same 

as for the quarterly survey. Total expenditures 

interacted with interview status with 

nonrespondents spending proportionately more 

on food as total expenditures increased (Figure 

37) and less on other (Figure 39). 

Nonrespondents with lower education spent 

relatively more on clothing (Figure 41) but less 

on food (Figure 43). 

 Nonresponding households which took more 

phone calls to collect data spent proportionately 

more on meals (Figure 45). 

Type of Nonresponse

Quarterly survey

Table 2 shows the same effects separated by type 

of nonresponse. The higher proportions for 

“housing” came from refusals(IR and RI vs. II) 

and “food”(RI, IR, and NI vs. II), while for 

“apparel” it was limited to converted refusals(IR 

vs. II, with IR lowest). The lower proportions 

came from converted refusals for “personal 

insurance” (RI vs. II), and from refusals(RI and 

IR) and attrition noncontact(IN) for 

"miscellaneous".

 The higher proportions of nonresponse for 

Urban dwellers came from noncontact and 

refusal conversion, slightly offset by lower rates 

due to refusal attrition (Table 8). Urban refusers 

spent relatively more on personal care which was 

offset by noncontacts spending less.

 Higher nonresponse for multiple unit 

structures came from noncontact and attrition 

refusal (Table 9). Attrition was associated with 

lower relative housing expenditures. 

 Ownership was associated with lower 

nonresponse except for refusal conversion. 

Owners spent less on housing, but noncontacted 

attrition owners spent more while refuser owners 

spent less. Owners spend proportionately less on 

apparel, but refusal conversions spent notably 

less on apparel (Table 10).

Diary Survey

 Total expenditures interacted with interview 

status with nonrespondents spending 

proportionately more on food as total 

expenditures increased (Figure 38) for refusal 

attrition and noncontact conversion, but the 

opposite for the other nonresponse types. The 

lower spending on other was due to noncontact 

conversion, all the other types of nonresponse 

were higher(Figure 40). Nonrespondents with 

lower education spent relatively more on 

clothing (Figure 42) except for refusal 

conversion. Lower spending on food was due to 

noncontact attrition and to a lesser extent both 

types of conversion (Figure 44).

 The effect of nonresponding households 

which took more phone calls to collect data spent 

proportionately more on meals was due to all 

forms of nonresponse, particularly refusal 

conversion (Figure 46). 

Discussion

 The most striking finding of this study was 

the lack of effects due to call record data. An 

optimistic interpretation would be that patterns 

of interviewer behavior captured by call record 

data have no biasing impact on the data 

collected.

 The nonrespondents on the quarterly survey 

had higher relative expenditure estimates for 

housing, food, and apparel, offset by lower 

expenditures for personal insurance and 

miscellaneous expenditures. The nonresponders 

in the diary survey also spent more on apparel

and food. This suggests there may be a format 

effect. The magnitude of the bias depends on 

how similar the attrition and conversion groups 

are to those who never responded. Swanson 

(2002) found that “the nonresponses of the 

intermittent responders appear to have a 

relatively small effect on the CEQ’s published 

expenditure estimates.” The methods of this 

study differed from Swanson’s in that the 

relative expenditure between categories was 

examined, rather than the expenditure amount.

 This study found the age of the reference 

person was related to slight bias due to 

nonresponse on the Quarterly, but not the Diary. 

Swanson (2002) found “the average age of the 

reference person in complete responder CU’s is 

greater (50.6 versus 40.9)”. This agreed with

Groves and Couper (1998) for refusal, but older 

households had greater noncontact. Since the 

CEQ has proportionately more refusal this is 

consistent. Similarly, Tucker (1992) found 

younger respondents had more item nonresponse 

in the Consumer Expenditure Diary survey. This 

study also had younger nonresponders.

 Swanson also found for complete responders: 

“the average quarterly expenditure per CU on all 

items is greater ($8981 versus $7,504), and the 

average expenditure per person is greater ($3,442

versus $3,212) than for intermittent responders”. 

This study found the relative expenditure for 

alcohol, education, and personal insurance were 

higher for complete responders with higher 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics.

incomes, offset by relatively lower food and 

tobacco expenditures on the Quarterly. On the 

Diary complete responders with higher income 

spent relatively less for food. Further bias 

research would be useful to determine if the 

measures of the sources of income, their overall 

amounts, household composition, and age 

interact. A much larger sample size would be 

necessary.

 “Complete responder CU’s are also more 

likely to have both husbands and wives present 

in the household (57.2% versus 39.8%), less 

likely to be single consumers (25.3% versus 

37.5%)” (Swanson, 2002). Groves and Couper 

(1998) and Tucker and Dixon (2000) found 

larger households were more likely to be 

nonresponders (due to noncontact), which would 

seem to disagree with the finding for single 

consumer units. Since much of the nonresponse 

in the CEQ is due to refusal rather than 

noncontact, the difference in household 

characteristics may not be so different. This 

study found size of household may be nonsignificantly related to nonresponse bias for the 

Quarterly survey, with nonresponding 

households being smaller. This may be related 

to the age effect. No effect was found for the 

Diary.

 Swanson found complete responders were 

“more likely to be homeowners (73.2% verus 

41.0%), and more likely to have only one CU 

living in the household (98.3% versus 87.3%).” 

Similar to household size, Groves and Couper 

found homeowners less likely to have 

noncontact, but more likely to have refusal. This 

study found that homeowners spent more on 

entertainment and miscellaneous. Ownership 

interacted with interview status so that 

responders spent relatively more on apparel and 

miscellaneous, but less on housing, food, and 

entertainment. This may be related to income.

 Children present was related to complete 

responders in Swansons’ (2002) study, and lower 

refusal and noncontact in Groves and Couper 

(1998, p.92) The effect may disappear or reverse 

if adjusted for other variable ( p.113). Tucker 

and Dixon (2000) found lower probability of 

noncontact even adjusting for other variables 

(although the model was different). This study 

found no interaction with interview status.

 The gender of the respondent didn’t relate to 

nonresponse bias in this study. Although there 

seemed to be a difference in expenditures ( Table 

3 ) there wasn’t either a suppressor effect or an 

interaction with nonresponse. Tucker and Dixon 

(2000) found males more difficult to contact and 

more likely to refuse.

 Multi-unit structures had been associated with 

higher refusal and nonresponse by both Groves 

and Couper (1998) and Tucker and Dixon 

(2000). In this study multi-unit households spent 

more on housing, but nonrespondents from those 

households spent relatively more on apparel and 

reading, making up for it by spending relatively 

less on housing. With a larger sample size it 

would be interesting to see if there is an 

interaction with age. There wasn't an effect 

found in the Diary survey.

 The type of nonresponse seemed to make a 

difference. Attrition noncontact showed little 

biasing effects except for "miscellaneous". The 

effects of refusal were strongest in housing and 

food. The other effects tended to counterbalance 

one another (for example: alcohol had higher 

expenditures for refusal attrition but lower for 

refusal conversion). Nonresponse on the Diary 

was associated with higher spending except for 

the "other" category. The effect was consistent 

for different types of nonresponse, except for 

conversion noncontact for clothes and attrition 

noncontact for food, but the effects were slight.

 The call record variables on the Quarterly 

survey showed little bias effects. Longer 

interviews associated with nonresponse showed 

lower expenditures on apparel. Since 

nonresponse is associated with age, home 

ownership, multiunit dwellers, and urban areas it 

would be interesting to see the profile of apparel 

spending broken down by these variables in 

terms of length of interview. This would be best 

done with a larger sample size. The effect of 

more visits for nonresponding households 

assiciated with higher transportation spending 

may relate to demographic characteristics 

associated with contactability (possibly such as 

younger renters not being at home). The effect 

in the Diary of more phone calls for 

nonresponding households associated with 

higher spending on meals eaten out may 

similarly be associated with some demographics 

(possibly such as younger, apartment dwellers). 

Limitations and Future Research 

 There are two methodological issues future 

research should address. A larger sample size 

would allow more study of interactions. The 

distributions of several of the expenditures 

(tobacco and medical in particular) and 

covariates need to be further explored. While the 

“compositional analysis” method was 

interesting, it didn’t adjust for all the features of 

the data.
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics.

 The effect of time in sample should be 

examined. Since attrition and conversion occur 

at relatively high rates there is some rotation of 

the sample between interview periods. The bias 

doesn’t seem to change overall, but refusals 

seem to have a varied pattern. 
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics.

 References:

• Atchison, J., The Statistical Analysis of Compositional Data, Chapman and Hall, New York, 1986.

• Dixon, J., “Relationship Between Household Nonresponse, Demographics, and Unemployment Rate in 

the Current Population Survey”, Paper presented at the Joint Statistical Meetings, Atlanta, Georgia, 

2001.

• Dolton, P., Lindeboom, M., and Van den Berg, G.J., “Survey attrition: A taxonomy and the search for 

valid instruments to correct for biases”, in Statistical Policy Working Paper 30, 1999 Federal 

Committee on Statistical Methodology Research Conference.

• Fletcher, J., and Schmidt, D., “Measuring Response Bias in Survey Research: An Analysis of Age 

Characteristics of Early Respondents and Resistors”, Paper presented at AAPOR, 2001.

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

• Swanson, D., “Characteristics of Complete and Intermittent Responders in the Consumer Expenditure 

Quarterly Interview Survey”, presented at the Nonresponse Summit organized by the Bureau of the 

Census, February 21, 2002.

• Tucker, C., “The Estimation of Insturment Effects on Data Quality in the Consumer Expenditure Diary 

Survey”, Journal of Official Statistics, Vol. 8.1, 1992, pages 41-61.

• Tucker, C., and Dixon, J., “Predicting Interviewer Nonresponse Rates from Household and Regional 

Characterstics”, Paper presented at AAPOR, 2000.

• Tucker, C., and Kojetin, B., “The Impact of Nonresponse on the Unemployment Rate in the Current 

Population Survey”, Paper presented at the International Workshop on Household Survey 

Nonresponse, 1997.
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics.

Appendix_A Quarterly Consumer Expenditure Survey 

Table 1 Gross Flows due to nonresponse for the CEQ.

 II NR All p-value

Food Mean 0.196 0.212 0.198 <.0001

 StdErr 0.001 0.004 0.001

Alcohol Mean 0.009 0.009 0.009 0.7263

StdErr 0.000 0.001 0.000

Housing Mean 0.379 0.397 0.381 <.0001

StdErr 0.002 0.006 0.002

Apparel Mean 0.041 0.042 0.041 <.0001

 StdErr 0.001 0.002 0.000

Transport Mean 0.180 0.175 0.179 0.3826

 StdErr 0.002 0.006 0.002

Entertain Mean 0.051 0.047 0.051 0.1264

 StdErr 0.001 0.002 0.001

Personal Mean 0.012 0.013 0.012 0.0740

 StdErr 0.000 0.001 0.000

Reading Mean 0.005 0.005 0.005 0.0787

 StdErr 0.000 0.000 0.000

Education Mean 0.014 0.013 0.014 0.0737

 StdErr 0.001 0.002 0.001

Tobacco Mean 0.014 0.011 0.013 0.9153

 StdErr 0.000 0.001 0.000

Misc Mean 0.011 0.010 0.011 0.0003

 StdErr 0.001 0.002 0.000

Insurance Mean 0.088 0.068 0.086 <.0001

 StdErr 0.001 0.003 0.001

All N 6801 920.0 7721 <.0001

Table 2: CEQ Type of Nonresponse

. . II IN IR NI RI All

Food Mean 0.196 0.184 0.207 0.205 0.230 0.198

. StdErr 0.001 0.010 0.007 0.009 0.007 0.001

Alcohol Mean 0.009 0.010 0.008 0.008 0.011 0.009

. StdErr 0.000 0.002 0.001 0.001 0.002 0.000

Housing Mean 0.379 0.388 0.402 0.354 0.412 0.381

. StdErr 0.002 0.017 0.010 0.014 0.009 0.002

Apparel Mean 0.041 0.049 0.037 0.044 0.043 0.041

. StdErr 0.001 0.006 0.003 0.005 0.003 0.000

Transport Mean 0.180 0.198 0.174 0.196 0.158 0.179

. StdErr 0.002 0.020 0.009 0.018 0.008 0.002

Entertain Mean 0.051 0.048 0.045 0.062 0.042 0.051

. StdErr 0.001 0.005 0.003 0.007 0.002 0.001

Personal Mean 0.012 0.009 0.015 0.010 0.014 0.012

. StdErr 0.000 0.001 0.001 0.001 0.001 0.000

Reading Mean 0.005 0.006 0.005 0.005 0.004 0.005

. StdErr 0.000 0.001 0.001 0.001 0.000 0.000

Education Mean 0.014 0.013 0.012 0.017 0.012 0.014

. StdErr 0.001 0.005 0.003 0.007 0.003 0.001

Tobacco Mean 0.014 0.012 0.010 0.011 0.011 0.013

. StdErr 0.000 0.002 0.001 0.003 0.001 0.000

Pmisc1_m Mean 0.011 0.005 0.009 0.015 0.009 0.011

. StdErr 0.001 0.002 0.002 0.006 0.003 0.000

Insurance Mean 0.088 0.079 0.076 0.073 0.053 0.086
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics.

. StdErr 0.001 0.007 0.005 0.008 0.005 0.001

All N 6801 116.0 331.0 137.0 336.0 7721
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Labor Statistics.

Table 3 CEQ – MANOVA p-values for nonresponse and covariates

Covariate Name Interview Status Covariate Interaction 

Total expenditures <.0001 <.0001 <.0001

Place size <.0001 <.0001 0.1411

Education 0.0171 <.0001 0.0152

Age <.0001 <.0001 0.0067

Household size <.0001 <.0001 0.2903

Household income <.0001 <.0001 <.0001

Number of Children <.0001 <.0001 0.1339

Multiple Unit structure <.0001 <.0001 <.0001

Own home <.0001 <.0001 <.0001

Urban <.0001 <.0001 0.0087

Sex <.0001 <.0001 0.6026

Race 0.0004 <.0001 0.0778

Interview length <.0001 <.0001 0.0055

Number of phone calls <.0001 <.0001 0.2967

Number of contacts <.0001 <.0001 0.1318

Number of trips <.0001 <.0001 0.0755

Phone collect data <.0001 <.0001 0.8315

Phone other <.0001 <.0001 0.1212

Phone schedule <.0001 <.0001 0.3819

Travel time <.0001 <.0001 0.1815

Type of call records <.0001 <.0001 0.0761

Visit to collect data <.0001 0.0003 0.5699

Visit other <.0001 <.0001 0.2529

Visit schedule <.0001 <.0001 0.0008

Table 4 

. II NR All

Total spent 8691(81.17) 8859(284.6) 8710(79.02)

size 11.23(0.095) 11.84(0.264) 11.30(0.089)

Education 12.96(0.023) 12.99(0.064) 12.96(0.022)

Age 49.44(0.211) 48.32(0.558) 49.31(0.198)

Cusize 2.594(0.018) 2.448(0.046) 2.577(0.017)

Family income 35180(548.6) 25822(1429) 34108(513.5)

Kids 0.804(0.013) 0.697(0.035) 0.792(0.013)

Multiple unit 0.267(0.005) 0.304(0.015) 0.271(0.005)

Own 0.698(0.006) 0.670(0.016) 0.695(0.005)

Ncalls 2.472(0.033) 2.596(0.105) 2.487(0.031)

Ncontacts 4.493(0.045) 4.837(0.143) 4.532(0.043)

Ntravel 2.067(0.027) 2.307(0.085) 2.095(0.026)

Nvisits 2.020(0.027) 2.241(0.086) 2.045(0.026)

Pcollect 0.472(0.012) 0.541(0.036) 0.480(0.012)

Pother 0.715(0.022) 0.848(0.071) 0.730(0.021)

Pschedule 1.286(0.023) 1.207(0.075) 1.277(0.022)

travel time 91.59(1.144) 116.4(3.827) 94.30(1.107)

vcollect 0.815(0.012) 0.731(0.035) 0.806(0.012)

Vother 0.956(0.021) 1.086(0.064) 0.971(0.020)

Vschedule 0.249(0.010) 0.424(0.042) 0.269(0.010)
Any opinions expressed in this paper are those of the author and do not constitute policy of the Bureau of 

Labor Statistics.