Integrating Nonmarket 

Consumption into the 

Bureau of Labor Statistics 

Consumer Expenditure 

Survey

FEBRUARY 2024

Ajit Zacharias, Fernando Rios-Avila, Nancy Folbre, and Thomas Masterson

Prepared under contract with the Bureau of Labor Statistics by the Levy Economics Institute 

of Bard College 

(9/30/2021–2/27/2023, Order No. 1605C5-21-P-00021)

2/22/2024
Integrating Nonmarket Consumption into the BLS Consumer Expenditure Survey

by Ajit Zacharias, Fernando Rios-Avila, Nancy Folbre, and Thomas Masterson

A report for the Bureau of Labor Statistics, US Department of Labor

Prepared by the Levy Economics Institute of Bard College (Order No. 1605C5-21-P-00021)

February 22, 2024 

Acknowledgements: We are grateful to the US Bureau of Labor Statistics for assigning us this project 

that enhances our research in the Institute's programs on Distribution of Income and Wealth and Gender 

Equality and the Economy. We also appreciate the valuable and insightful feedback we received from the 

BLS economists. In particular, we wish to thank Thesia I. Garner, Jake Schild, and Frazis Harley. We also 

wish to thank Lindsey Carter for her excellent editing assistance and Michael Stephens for managing the 

administrative aspects of the project.
2

Contents

List of Tables.................................................................................................................................................4

List of Figures................................................................................................................................................6

Executive Summary.......................................................................................................................................8

Research Goals ............................................................................................................................................15

1 Introduction..........................................................................................................................................17

2 Scope and Valuation of Household Production...................................................................................20

2.1 Introduction..................................................................................................................................20

2.2 Definitions ...................................................................................................................................20

2.3 Valuation......................................................................................................................................24

2.4 Summary......................................................................................................................................31

3 Imputing Home Production by Members of the Consumer Unit ........................................................32

3.1 Background..................................................................................................................................32

3.2 Imputation methods.....................................................................................................................36

3.3 Imputation results ........................................................................................................................40

3.3.1 Household production for members of own household ................................................40

3.4 Aggregation to consumer units....................................................................................................71

3.5 Summary......................................................................................................................................73

4 Imputing Care Received ......................................................................................................................76

4.1 Care received by children ............................................................................................................76

4.1.1 Background ...................................................................................................................76

4.1.2 Comparison of ECPP and CE samples..........................................................................79

4.1.3 Models and results.........................................................................................................82

4.2 Care received by older adults ......................................................................................................91

4.2.1 Background ...................................................................................................................91

4.2.2 Data ...............................................................................................................................92

4.2.3 Models and results.........................................................................................................95
3

4.3 Summary....................................................................................................................................103

5 Consumption Expenditures and Household Production....................................................................105

5.1 Introduction................................................................................................................................105

5.2 The overall impact of the inclusion of household production ...................................................105

5.3 Subgroup disparities ..................................................................................................................109

5.3.1 Gender.........................................................................................................................109

5.3.2 Size and composition of consumer units.....................................................................110

5.3.3 Race and ethnicity.......................................................................................................113

5.4 Distribution of household production........................................................................................117

5.5 Applications to food and childcare ............................................................................................122

5.5.1 Food.............................................................................................................................123

5.5.2 Childcare .....................................................................................................................127

5.6 Summary....................................................................................................................................131

6 Conclusion and Recommendations....................................................................................................134

6.1 Scope of home production.........................................................................................................134

6.2 Valuation of home production...................................................................................................135

6.3 Imputations from the ATUS ......................................................................................................135

6.4 Imputations of care received from outside the consumer unit...................................................137

6.5 Recommendations for the use of synthetic CE public-use files................................................137

Appendix A Validation exercise using the PSID ......................................................................................140

Background............................................................................................................................................140

Data........................................................................................................................................................140

Methodology..........................................................................................................................................141

Results....................................................................................................................................................144

Appendix B Comparison of the results from two algorithms of multiple imputation...............................149

References..................................................................................................................................................151
4

List of Tables

Table 2-1 Categories of household production included in the study .........................................................22

Table 2-2 Percent of parental supervisory (in-your-care) time overlaps with leisure and unpaid housework 

and procurement activities in households with children, no other adults present (ATUS 2004-2019).......28

Table 3-1 Imputation methods used by the component of household production.......................................37

Table 3-2 Weighted vs. actual observations across surveys (individuals aged 15 years and older) ...........42

Table 3-3 Composition of the sample by sex, employment status, and presence of children in the 

household (percent) .....................................................................................................................................43

Table 3-4 Variables used in matching and propensity score estimation .....................................................45

Table 3-5 Share of the total number of observations matched in each round (percent) in the matching of 

the CE Interview sample with ATUS ..........................................................................................................47

Table 3-6 Classification of characteristics based on whether the relationship between the characteristic 

and time is reflected in the matching imputation (“Yes”) or not (“No”), by type of statistic, sample, and 

sex................................................................................................................................................................54

Table 3-7 Average shares of daily supervisory childcare time spent alone (solo) by gender and number of 

caregivers (percent) .....................................................................................................................................73

Table 4-1 Demographic composition of the population under six years of age by survey, 2019 ...............80

Table 4-2 Estimates of the binary logit model (dependent variable is the recipiency of care by a child 

under six years)............................................................................................................................................84

Table 4-3 Observed and imputed recipiency of care (percent) among children under six years ................86

Table 4-4 Estimates of the Poisson model (dependent variable is the weekly hours of care received by a 

child under six years)...................................................................................................................................88

Table 4-5 Observed and imputed weekly hours of care received by children under six years ...................90

Table 4-6 Demographic composition of the population over 50 years of age by survey............................94

Table 4-7 Estimates of the binary logit model (dependent variable is the recipiency of care by persons 

over 50 years of age)....................................................................................................................................96

Table 4-8 Observed and imputed recipiency of care (percent) among persons over 50 years of age .........98

Table 4-9 Estimates of the Poisson model (dependent variable is the weekly hours of care received by a 

person over 50 years of age)......................................................................................................................100

Table 4-10 Observed and imputed weekly hours of care received by adults 50 years and older..............102

Table 5-1 Average monthly values of expenditures and household production per consumer unit, 2019 

(third quarter).............................................................................................................................................107
5

Table 5-2 Racial differences in monthly average values of household production and their potential 

determinants...............................................................................................................................................114

Table 5-3 Subgroup decomposition of Gini coefficients for expenditures and augmented expenditures by 

the presence of children in the household and measure of home production, 2019 (Gini points) ............120

Table 5-4 Decomposition of the Gini coefficient by components of consumption (Gini points) by measure 

and presence of children, 2019 ..................................................................................................................121

Table 5-5 OLS estimates of a simple model of household hours of cooking............................................126

Table 5-6 Employed married-couple families with children and two adults: Average monthly childcare 

expenditures and average monthly hours of childcare provided by the family, 2019...............................129

Table 5-7 Families with children under 13 years and a single, employed reference person: average 

monthly childcare expenditures and average monthly hours of childcare provided by the family, 2019.130

Appendix Table A-1 Residual correlation.................................................................................................147

Appendix Table A-2 Conditional correlation coefficient of log(hh Exp) .................................................147
6

List of Figures

Figure 2-1 Hourly wage rates used in valuing household production by category (2019 dollars)..............30

Figure 3-1 Density of the first principal component ...................................................................................44

Figure 3-2 Matching quality by round in the matching of the CE Interview and CE Diary samples with 

ATUS (based on the shares of the matching distance in each round) .........................................................48

Figure 3-3 Box Plots: Distribution of daily hours of household production on weekdays and weekends, 

imputed vs. observed, statistical matching (double matching method without small cells)........................51

Figure 3-4 Actual (ATUS) values compared to imputed values, by sex and race/ethnicity (daily hours)..52

Figure 3-5 ATUS weekday standard deviation compared to standard deviation in the matched weekday 

samples, by sex, subgroup, and sample .......................................................................................................56

Figure 3-6 Box Plots: Distribution of daily hours of household production on weekdays and weekends, 

imputed vs. observed, imputation by prediction (regression prediction) ....................................................59

Figure 3-7 Percentage deviation of the imputed CE Interview sample statistic from the ATUS statistic: 

comparison of statistical matching (SM) and regression-prediction (RP) for subgroups of women, by 

statistic and diary day ..................................................................................................................................61

Figure 3-8 Percentage deviation of the imputed CE Interview sample statistic from the ATUS statistic: 

comparison of statistical matching (SM) and regression-prediction (RP) for subgroups of men, by statistic 

and diary day................................................................................................................................................63

Figure 3-9 ATUS weekday standard deviation compared to imputed weekday standard deviation in the 

CE samples using regression-prediction (RP method), by sex, subgroup, and sample...............................64

Figure 3-10 Box Plots: Distribution of daily hours of household production on weekdays and weekends, 

imputed vs. observed, imputation by prediction (MI method)....................................................................66

Figure 3-11 Percentage deviation of the imputed CE Interview sample statistic from the ATUS statistic: 

comparison of statistical matching (SM) and multiple imputation (MI) for subgroups of women, by 

statistic and diary day ..................................................................................................................................68

Figure 3-12 Percentage deviation of the imputed CE Interview sample statistic from the ATUS statistic: 

comparison of statistical matching (SM) and multiple imputation (MI) for subgroups of men, by statistic 

and diary day................................................................................................................................................69

Figure 3-13 ATUS weekday standard deviation compared to imputed weekday standard deviation in the 

CE samples using multiple imputation (MI method), by sex, subgroup and sample ..................................70

Figure 3-14 Share of daily supervisory childcare time spent alone (solo) by gender and number of 

caregivers in the household (percent)..........................................................................................................72
7

Figure 5-1 Average monthly hours of home production and supervisory care by persons 15 years and over 

by gender, 2019 .........................................................................................................................................110

Figure 5-2 Relative average expenditures by the number of adults and children in the household, standard 

vs. augmented expenditure definition, 2019..............................................................................................112

Figure 5-3 Relative average expenditures by race/ethnicity of the household, standard vs. augmented 

expenditure definition, 2019......................................................................................................................116

Figure 5-4 Average monthly values of expenditures and home production by expenditure decile, 2019 117

Figure 5-5 Average monthly values of the major components of home production by expenditure decile, 

2019 ...........................................................................................................................................................118

Figure 5-6 Average monthly value of home production by expenditure decile and presence of children 

(under 18) in the household, 2019 .............................................................................................................119

Figure 5-7 Factors affecting the contribution of home production to consumption inequality by alternative 

measures, 2019 ..........................................................................................................................................122

Figure 5-8 Average monthly food expenditures and hours of cooking by number of adults and children in 

the household, 2019 ...................................................................................................................................124

Figure 5-9 Average monthly food expenditures, average monthly household hours of cooking, and share 

of expenditures on food away from home in total food expenditures by size and composition of 

households, 2019 .......................................................................................................................................125

Appendix Figure A-1 Conditional correlation: total household production..............................................145

Appendix Figure A-2 Conditional correlation: components of household production .............................145

Appendix Figure B-1 Comparison of SAS and Stata methods of multiple imputation.............................150
8

Executive Summary

Consumption expenditures are a powerful indicator of standard of living because most families acquire 

key requirements for their daily lives—food, clothing, cellphone services, etc.—as commodities, i.e., 

goods and services exchanged for money. Economists continue to devote considerable effort to 

understanding how the acquisition of commodities translates into actual consumption and how living 

standards also depend on products that are not commodities. An important category of such products that 

are crucial to sustaining living standards results from household production activities and consist 

primarily of nonmarket and nongovernmental services such as do-it-yourself home repairs and childcare. 

Consumer Expenditure Surveys (CE) conducted by the BLS do not collect information on home 

production activities. The lack of data is the chief obstacle to developing household-level consumption 

measures that include household production. To fill this gap, the Bureau of Labor Statistics (BLS) 

awarded a contract to the Levy Institute in late 2021. This report contributes to the efforts of the BLS by 

creating a methodology to integrate information on household production. The quantities of the services 

resulting from household production are proxied by the labor inputs into their provision, i.e., the time 

allocated to providing the services. We include in the latter the time spent by household members for their 

own household. We also include the time allocated to services received by household members by people 

outside their household, e.g., care for older household adults with frailties provided by relatives or friends. 

Household production is defined as encompassing both sets of activities. The activities comprising 

household production contribute to household consumption; hence, some measure of its monetary value 

should be included in a broad measure of consumption.

We propose data sources and methods to build home production estimates in the CE Interview and Diary 

samples at the consumer-unit level. Our procedures are deployed on the quarterly CE public-use files that 

contain expenditure information for the calendar year 2019. The dataset we construct allows joint 

examination of consumption expenditures and household production by adding variables associated with 

the latter to each CE sample (Interview and Diary).

The 2019 round of the American Time Use Survey (ATUS), also carried out by the BLS, is our data 

source for the services provided by household members for their own households. Using 24-hour time 

diaries, the ATUS captures how one respondent (15 years or older) in a sample household spent a day and 

classifies the activities into detailed groups. Given our schema (described below) of constructing 

monetary values of household production, we categorize the activities into five groups: cooking, 

including clean-up (e.g., preparation of meals, washing dishes); housework other than cooking (e.g., 

doing laundry, cleaning the living premises, shopping for groceries, etc.); caring for and helping 

household adults (e.g., giving medicine, helping with a cellphone, etc.); active childcare; and supervisory 
9

child care. Standard definitions of childcare include only active care (e.g., reading to a child, bathing a 

child, etc.). However, we also account for passive or supervisory care of children (e.g., keeping an eye on 

a child while watching TV). The latter is not an activity. Instead, it measures the time household children 

were in the respondent's care. We differentiate between supervisory childcare that overlaps with cooking, 

other housework, or adult care and supervisory childcare that does not coincide with those activities. 

Supervisory care is intrinsically vital as care. Further, it can often constrain individuals (especially 

women) from engaging in other activities, particularly employment. In light of these considerations, we 

recommend that supervisory care be included in the definition of household production.

We use two sources of data to measure care services received by households from people outside their 

households. The people outside the household providing nonmarket care services may be family members 

who live outside the household (e.g., siblings, step or biological parents or children), members of the 

extended family (e.g., grandmothers or nieces), or nonrelatives (e.g., friends or neighbors). One of our 

data sources—the 2019 round of the Early Childhood Program Participation Survey (ECPP) module of 

the National Household Education Survey (NHES)—provides information to estimate weekly hours of 

nonmarket and nongovernmental care received by children under six. The other data source was the 2016 

round of the Health and Retirement Survey (HRS) conducted by the University of Michigan. The HRS 

collects information on hours of assistance on certain activities received from people outside the 

household by older adults (51+ years) who report some health difficulty or disability. 

Both sources would understate the care received. Several children older than six are also likely to be care 

recipients for childcare. Similarly, we ignore the external care received by people younger than 50 years 

of age (especially the younger disabled population) and care related to activities other than those captured 

in the HRS. Despite the limitations, we used these data sources because they appear to be the best 

currently available data for our purposes. We recommend that efforts be enhanced to gather information 

on nonmarket care received from nonhousehold members by older children (5 to 13 years) and disabled or 

sick individuals under 50 years of age living in households.

Estimates of home production that we build in the CE samples are based on imputation techniques widely 

used in the literature. A central statistical assumption involved in imputation is the Conditional 

Independence Assumption (CIA). Simply put, the premise is that, once we control for a set of relevant 

characteristics, the distribution of variables that cannot be observed jointly (in our case, consumption 

expenditures and household production) and require imputation is as good as independent. We tested this

assumption using the 2019 data on time use and expenditures from the Panel Study of Income Dynamics 

(PSID) conducted by the Institute for Social Research, University of Michigan. The results suggest that 

the CIA is valid, which is a reasonable source of confidence in the imputations we carried out. At the 
10

same time, we should also point out that the definitions and measurement of consumption expenditures 

and time use are quite different between the PSID on the one hand, and the CE and ATUS on the other. 

Further, other assumptions (the validities of which are harder to assess than the CIA using available data, 

or even impossible) are involved in the estimates of home production we construct. On balance, our 

stance is that unknowable biases from assumptions that are difficult to assess empirically should not deter 

us from imputations necessary for understanding critical economic phenomena.

We implemented three methods of imputation from the ATUS. The simplest method we employed may 

be the “regression-prediction” (RP) method. Multivariate models were first estimated separately for 

individuals in households with and without children in the ATUS. The dependent variables in each model 

were the time allocated (weekly hours) to the five categories of household production in the ATUS, and 

independent variables consisted of a set of relevant individual and household characteristics. In the next 

step, we used the estimated coefficients to predict the hours of each person (15 or older) in the CE 

samples.

Our second imputation method was the technique known as multiple imputation (MI). Unlike the RP 

method, the MI procedure produces several imputed values of the same variable for each record (e.g., five 

different values are generated of the time allocated to cooking for each person). Admittedly, imputation 

errors can be accounted for coherently when using the imputed data for statistical inference (e.g., 

constructing confidence intervals for the average hours allocated to cooking). The multiple values of the

same variable for each person are generated via a simulation using the variance–covariance matrix of the 

residuals of the estimated multivariate model (similar to that used in the RP method described above) in 

the ATUS. In light of the current software capabilities, we tested two models for this exercise—OLS and 

interval regression—and found both to produce similar results, although the latter is methodologically 

superior.

The third method utilized in the ATUS data was statistical matching (SM). Our matching algorithm has 

two crucial features: constrained matching based on propensity scores and stratification. The constraint is 

that matching is done without replacement (i.e., all the records in the donor file are used) to equalize the 

weighted number of observations in the ATUS (“donor file”) and each CE sample (“recipient file”). Once 

the transformation required to satisfy the constraint is implemented (along with a few other steps), the 

following procedure is undertaken separately for each recipient file (i.e., CE Interview and Diary 

samples). We stack the donor and recipient files to form a single file. Then, we estimate propensity scores 

via a logit model with the probability of a record belonging to either the recipient or donor file as a 

dependent variable and a set of individual and family characteristics as independent variables. To further 

improve matching quality, stratification is carried out to form relatively homogenous groups in both files. 
11

Small subgroups (e.g., subgroups formed by gender and the presence of children) are created and 

identified using principal component analysis (PCA) and cluster analysis. We iteratively match 

observations based on rank similarity within subgroups, modifying the subgroups as needed in the process 

to ensure that one of all the possible candidates from the donor file is selected as the effective donor for 

each record in the recipient file.

For imputations based on the ECPP and HRS data of services for children and older adults received from 

people outside the home, we utilized a two-stage imputation procedure. First, the likelihood of receiving 

care is modeled via a binomial logit model. This model predicted the propensity score of recipiency in the 

CE samples. A stochastic component was added to the prediction by assigning recipiency based on a draw 

from a Bernoulli distribution—using the predicted score for each individual as a parameter for the draw. 

In the next step, we estimated a Poisson model of the hours of care received using only the subsample that 

received care. The model parameters were then employed to estimate hours of care received by those 

assigned recipiency of care in the CE samples. We assessed the quality of imputations by assessing how 

close the incidence of the recipiency of care and the average hours of care—computed from the imputed 

data—were to those estimated from the original data. In general, we found that the estimates were 

satisfactorily close. A more detailed examination of the imputation quality was not conducted, nor were 

alternative imputed values created because a preliminary perusal of the data showed that the incidence 

and quantum of external care was small relative to “internal” household production, i.e., services received 

by the household from members of the same household.

In contrast, our final data product provides imputed values produced by the three alternative methods 

from the ATUS. They were also subjected to much closer scrutiny. We recognize that methodological 

preferences and analysis goals would differ among the potential users of the imputed data. To provide 

flexibility and room for sensitivity tests, we recommend that imputed values generated by all methods be 

made available for public use. We found that all three methods performed equally well in replicating the 

average values of time allocated by individuals in various demographic subgroups, except for some male 

subgroups where the RP and MI methods did not perform as well as SM. The advantage of SM over the 

other two methods was clearly evident in replicating the median values and other percentile points in the 

distribution of time allocated (e.g., the 75th percentile value of the time given to cooking by employed 

women). On the other hand, advanced users interested in multivariate analysis of imputed data may favor 

the MI method because of the well-established statistical inference techniques associated with the method 

available in statistical software such as SAS®.

Once the imputations described above are carried out for each individual in the CE samples, we construct 

the aggregate for the consumer unit by adding up the imputed amounts for individuals in the consumer 
12

unit. For example, the time allocated to active childcare by the consumer unit is obtained by summing the 

time allotted by each individual in the consumer unit to active childcare. To avoid double counting in 

calculating the total time spent on household production by each individual, the time during which 

supervisory care coincides with other household production was included only in one of the following 

categories: cooking, housework other than cooking (including shopping), or adult care. Supervisory care

that does not overlap with household production was obtained by subtracting overlapping supervisory 

care time from the total time allocated to supervisory care. 

Further, supervisory childcare time in consumer units with multiple adults was adjusted to avoid the 

potential double-counting of such care (e.g., both parents may keep an eye on their toddler while 

watching TV during a given time of the day). The adjustments were made on parameters differentiated by 

gender and the number of likely caregivers estimated from the ATUS. Better information is required to 

understand the dynamics of supervisory care provision and other domestic services in households with 

multiple providers and receivers. Unfortunately, this cannot be obtained under the current ATUS 

sampling strategy of collecting time diaries from only one respondent per household.

The final step in our estimation endeavor was valuation, i.e., the conversion of hours of household 

production into monetary values. Given the alternative perspectives on the valuation principle, we believe 

that at least two sets of monetary values should be provided in the public-use file. We produced one set of 

values using the so-called generalist approach that applies a single hourly wage rate to various tasks of 

household production. Perhaps the most well-known estimate embodying this principle is the value of 

household production calculated by the US Bureau of Economic Analysis as a supplement to national 

accounts. Our preferred method is often described as the specialist-wage approach because it uses 

different wage rates to value various tasks of household production. Specifically, we used the average 

hourly wage for cooks, workers in the “private household” industry, preschool and kindergarten teachers, 

and nursing assistants to value the time allocated to cooking, housework other than cooking, active

childcare, and adult care. Because supervisory childcare is less costly to replace than active childcare, we 

used half of the average wage of preschool and kindergarten teachers to assign a monetary value to 

supervisory childcare. For childcare from people outside the household, we apply the same rate as active 

childcare rendered by a household member. Similarly, we value care for adults given by people outside 

the household at the same rate as members of the home. After completing the valuation of the various 

categories according to each principle, the resulting monetary values were aggregated to form the value of 

household production for each consumer unit in the CE samples.

While the central goal of our research was to develop a methodology for integrating household production 

into the CE samples, we conducted a preliminary descriptive analysis of the imputed values. Some 
13

tentative conclusions emerged. Irrespective of the valuation principle, the impact of adding the value of 

household production to consumption expenditures1 is enormous: the average value of expenditures that 

includes household production (“augmented expenditures”) is higher than a standard definition by 50 and 

63 percent for the generalist and specialist principles of valuation, respectively. The provision of these 

services—which adds to potential consumption but is excluded in the standard measures—is primarily 

taken on by women, reflecting the patriarchal allocation of household production responsibilities in our 

society. We estimate that women accounted for an overwhelming 78 percent of the total value and hours 

of household production in 2019.

The measured disparities in consumption expenditures between population subgroups are affected by the 

inclusion of household production. Crucial demographic variables at work are the number of adults and 

children in the household. Ignoring home production leads us to conclude that the highest average 

expenditures are among households with two adults and two children. However, including home 

production puts the group with three or more adults and three or more children at the top position. 

Incorporation of household production also causes a re-ranking among households differentiated by the 

race or ethnicity of the consumer unit’s reference person. We found that the change in racial disparities 

was primarily linked to differentials among groups in the incidence of households with children. The 

most striking finding in this regard was the reversal of rank between Hispanic and White families: 

Hispanics ranked slightly above Whites regarding average expenditures when we added household 

production to spending.

Consistent with the spirit of the earlier studies, we also found that household production has an 

inequality-reducing effect on the distribution of consumption expenditures. Our estimates show that for 

consumer units in the bottom half of the standard expenditure distribution, the average value of household 

production is approximately equal to average expenditures. For those in the top half, the average value of 

household production shows only a small variation across expenditure deciles compared to the sharp 

gradient of average expenditures. Thus, the relatively large size of household production and its 

somewhat equal distribution across the expenditure distribution ensures that its addition will increase the 

expenditure shares (e.g., the share of the bottom decile in the aggregate value of the new expenditures that 

1 Our starting point in estimating consumption expenditures is the total consumer expenditures available in the 

public use CE data. The latter consists of expenditures in 14 major categories: food, alcohol, housing, clothing, 

transportation, healthcare, entertainment, personal care, reading, education, tobacco, miscellaneous, cash 

contributions, and personal insurance and pensions. We subtract from the BLS definition of consumer expenditures, 

mortgage interest, property taxes, maintenance, repairs, insurance, and other expenditures on owner-occupied 

primary home. We also subtract expenses classified as miscellaneous, cash contributions, and personal insurance 

and pensions. Finally, we add the rental equivalent for owned homes.
14

includes the value of household production) of those in the lower rungs of the distribution. This shift will 

be reflected in the new distribution, displaying a lower inequality than the prior distribution.

Two opposing forces affect the change in inequality. First, since the mean value of household production 

is notably higher for those with children than for those without, adding home production to expenditures 

widens the intergroup inequality in the augmented measure compared to the standard estimate of 

expenditures. However, because of the lesser inequality in the distribution of household production than 

in that of spending, the inequality in the augmented measure within both groups will be lower than in the 

standard measure. We found, via a decomposition analysis, that the reduction in within-group inequality 

overwhelms the increase in between-group inequality. Additionally, the inclusion of supervisory care in 

the definition of childcare has an inequality-enhancing effect compared to a measure of childcare that 

includes only direct childcare. As noted above, the value of supervisory care is roughly equivalent to that 

of direct childcare. Hence, including supervisory care widens the between-group inequality and increases 

overall inequality.

In sum, our research has provided an empirical methodology and has identified data sources to extend the 

consumer expenditure data collected by the US Bureau of Labor Statistics by incorporating household 

production. We applied and tested our approach by generating estimates for 2019. Our recognition of the 

diversity of views and analytical strategies among researchers studying household production and its 

relationships with living standards has led us to implement estimation of various categories and 

imputation techniques to accommodate at least some of the diversity. Instead of a single homogeneous 

measure of household production, we also generate estimates of the major components of household 

production, such as childcare and cooking. This feature permits, for example, the analysis of the 

relationship between expenditures on prepared food and time allocated to cooking. We also provide 

imputed values for the principal categories of household production (except care services received from 

persons outside the household) based on three alternative imputation methods. Therefore, researchers can 

examine the sensitivity of their findings to alternative methods of imputation and use a particular set of 

imputed values based on their analytical strategy and goals. In a similar vein, we also generate monetary 

values of household production and its subcategories generated according to two principles of valuation. 

We hope that our efforts contribute to closer scrutiny of household production and aid in the formulation 

of policies to reduce gender inequality and economic inequality. 
15

Research Goals

The research conducted by the Levy Institute for the US Bureau of Labor Statistics (BLS) under Order 

No. 1605C5-21-P-00021 aimed to develop methods and identify data sources to build home production 

estimates in the CE Interview and Diary samples. Our research also involved applying the empirical 

methodology to the quarterly CE public-use files that contain expenditure information for the calendar 

year 2019. Specifically, we undertook the following activities, which are discussed in detail in the 

remainder of this report:

• Identify an appropriate household production definition to integrate with consumer expenditure 

data in light of existing research and practices.

• After reviewing alternative approaches, identify two alternative valuation methods for household 

production and the best available data to estimate the valuation rates (i.e., the rates at which 

household production hours of various types are to be converted into dollars).

• Provide a literature review of imputation of household production and assessment of imputation 

methods.

• Conduct a validation exercise examining the critical assumption of conditional independence 

using the Panel Study of Income Dynamics that contains joint information on household 

production and consumption expenditures.

• Implement imputation using statistical matching via matching records in the 2019 ATUS 

weekend and weekday diaries separately to individual records in each quarterly CE public-use 

file for each Interview and Diary sample.

• Perform using the method of regression-prediction, imputation of six categories of time allocated 

to household production from the 2019 ATUS weekend and weekday diaries separately to 

individual records in each quarterly CE public-use file for each Interview and Diary sample.

• Implement employing the method of multiple imputation, imputation of six categories of time 

allocated to household production from the 2019 ATUS weekend diaries and weekday diaries 

separately to individual records in each quarterly CE public-use file for each Interview and Diary 

sample.

• Impute hours of childcare received from people outside the household based on the 2019 round of 

the Early Childhood Program Participation Survey (ECPP) to each person under six years in each 

quarterly CE public-use file for each Interview and Diary sample.

• Impute hours of care of older adults received from people outside the household based on the 

2019 round of the Health and Retirement Survey (HRS) to each person over 50 years in each 

quarterly CE public-use file for each Interview and Diary sample.
16

• Conduct an extensive quality assessment of the imputations.

• Combine the results of imputations at the individual-level to generate consumer-unit level 

estimates of household production hours in the CE samples.

• Use the consumer-unit level estimates of household production hours to generate the monetary 

values of household production according to two valuation principles.

• Provide a preliminary analysis of the joint distribution of household production and consumption 

expenditures using descriptive statistics and decomposition analysis of the third-quarter CE 

Interview file.

• Explore (tentatively) the relationship between expenditures of time and money for food and 

childcare separately using the third-quarter CE Interview file.

• Provide recommendations to the BLS to aid their planned work on home production. The 

recommendations pertain to the scope and valuation of household production, imputations of 

various categories of household production, and use of CE data with imputed home production 

variables.
17

1 Introduction

The United States is among the handful of industrialized nations to publish official statistics on 

nonmarket production as a part of its national accounts. It has also been at the forefront of collecting 

annual data on time use for the last two decades via the American Time Use Survey (ATUS) of the US 

Bureau of Labor Statistics (BLS)—the vital input required for the estimates on nonmarket services in the 

national accounts. Recently, the BLS launched a laudable initiative to explore the integration of home 

production into the Consumer Expenditure Survey (CE) as part of an effort to develop a broad measure of 

household economic well-being (Armstrong et al. 2022). As a part of the initiative, the Bureau of Labor 

Statistics (BLS) awarded a contract to the Levy Institute in late 2021. Accordingly, we propose methods 

and data sources to build home production estimates in the CE Interview and Diary samples at the 

consumer-unit level. The dataset we construct allows joint analysis of consumption expenditures and 

household production by adding variables associated with the latter to the CE samples.

Our starting point details a set of considerations in defining the scope of home production suitable for 

integration with a measure of consumption (Chapter 2). We argue that the definition of childcare should 

be expanded to include supervisory childcare—which is not an activity per se when performed on a 

nonmarket basis but a constraint, especially on women. In addition, we should consider nonmarket care 

received by members of the consumer unit from people outside their household (e.g., a grandmother 

caring for her grandchild while the parents of the child are at their jobs) because such services expand 

consumption possibilities. In contrast to the US national accounts approach of using a single hourly rate 

to convert the time spent on diverse home production activities into monetary units, we propose a set of 

specialist hourly rates. In particular, we assign a higher value to the active care of dependents than to 

routine housework. Our valuation proposal is motivated by the observation that market substitutes for 

direct care work are generally costlier than substitutes for routine housework under the existing 

occupational wage structure. Hence, they may reflect relevant market replacement costs better than the 

single hourly rate anchored to one of the occupations with relatively low wages, i.e., workers in private 

households.

Ideally, consumer-unit–level nonmarket consumption estimates should be built from information 

collected directly from the respondents in the CE samples. However, the CE does not collect such 

information. Consequently, reasonable guesses—“imputations”—have to be made regarding nonmarket 

provisioning undertaken by members of a consumer unit for their household (“self-provisioning”) or 

nonmarket services received by the consumer unit from people outside their homes. Admittedly, no 

known imputation method will faithfully replicate the information that the individual respondent would 

have given if queried during the administration of the survey. Researchers have used various statistical 
18

methods for imputation, and we provide a brief overview in Chapter 3. In general, the best hope is to 

faithfully replicate the standard measures of location (mean and median values) and dispersion (standard 

deviations or measures of inequality such as the Gini coefficient) for a reasonable number of population 

subgroups (e.g., households with children and employed, female reference persons). In practice, the 

extent to which various methods would fulfill that hope differs.

Further complicating the issue is that there are no clear-cut scientific rules on which imputation performs 

best. Therefore, judgment calls and rules of thumb are required in this exercise. The main focus of 

Chapter 3 is to discuss how we used three different strategies to impute, using the 2019 ATUS file, selfprovisioning undertaken by individuals aged 15 and over in the consumer units covered in the Interview 

and Diary samples for 2019. We also present results on the quality of the imputations. Additionally, 

Appendix A of the chapter reports results from a validation exercise using data from the Panel Study of 

Income Dynamics. The purpose of the exercise was to ascertain the validity of the conditional 

independence assumption—an assumption that is made in most methods of imputation. The results lend 

strong support to the assumption with regard to the relationship between total consumption expenditures 

and categories of household production. However, further research would be required to assess whether 

the same conclusion would apply to the relationship between subcategories of expenditures (e.g., food 

expenditures) and household production.

We also continue on the theme of imputations in the next chapter (Chapter 4). Here, the focus is on 

nonmarket care rendered by people outside the household to the youngest (under 6 years) and older (over 

50 years) members of the consumer unit. In principle, we should account for the care received by 

members of all age groups. Unfortunately, reasonably reliable microdata is not available to facilitate such 

imputations. For the youngest group, we use the 2019 Early Childhood Program Participation (ECPP) 

survey that contains detailed information on the incidence and weekly hours of care received from 

relatives and nonrelatives, along with the demographic characteristics of the children and their families. 

Our source of information for the care received by older adults is the Health and Retirement Survey 

(HRS) of 2016. The HRS provides information on help received by adults with physical or mental 

frailties. We can identify the amount of care (in number of hours) received from people outside the 

household. When combined with data on the characteristics of the recipients and their households, the 

HRS can provide the ground for making reasonable imputations in the CE samples.

The imputations described in Chapters 3 and 4 allow us to accomplish the goal of producing estimates of 

home production (perhaps better described as nonmarket consumption in the current context) at the 

consumer-unit level in the CE Interview and Diary samples. Our final synthetic CE data files have 

estimates of home production by members of the consumer unit using three imputation methods. 
19

Estimates in the files of help received from outside the household were generated with a single imputation 

method. Both categories of home production are valued by our preferred method (“specialist wages”) as 

well as what we describe as the BEA (US Bureau of Economic Analysis) method, using the generalist 

wage. Results from alternative imputation methods are provided to facilitate the use of the data for 

various research purposes, from descriptive analysis to multivariate modeling. We also include results 

from two valuation methods for users to judge the sensitivity of their findings to the valuation method.

We use the synthetic Interview file for the third quarter of 2019 to present some salient features of the 

relationship between consumption expenditures and home production (Chapter 5). Irrespective of whether 

one takes supervisory childcare or care received from outside the household, and under both valuation 

methods, the inclusion of home production has a big impact. For the entire sample, average consumption 

that includes home production (“augmented expenditures”) is higher than standard consumption 

expenditures by at least 50 percent. The effect on families with children is even larger. We also briefly 

discuss the impact of including home production on consumption inequality. Echoing the findings from 

similar studies that incorporate home production in an income measure, we find that the measured level of 

inequality falls significantly, regardless of the definition or valuation of household production. Once 

again, substantial differences emerge between the impacts on households with and without children. 

Further, these differences are notably affected by whether we include supervisory childcare. We hope our 

initial foray into using the synthetic files leads to further scrutiny of these and related issues.

In the final chapter (Chapter 6), we outline the study's recommendations for integrating home production 

into the CE. These recommendations are derived from the conceptual and practical considerations 

outlined in Chapters 2–4. We were also guided by the data analysis that we report here. In addition, we 

took into account the findings from several other explorations of alternative assumptions and techniques, 

which are not reported here because of space limitations. Finally, we provide a set of guidelines that may 

be helpful to users of the data.
20

2 Scope and Valuation of Household Production

2.1 Introduction

Economists have long recognized that household production is crucial in sustaining living standards by 

providing goods and services (see, e.g., Antonopoulos 2008; Folbre 2009; and Wolff and Zacharias 

2007). The activities included in the household production orbit can generally be delegated by the 

household member performing that activity to someone outside the household for pay—the so-called 

“third party principle,” devised by Margaret Reid (1934). Hence, leisure (or, for that matter, those 

activities related to formal learning) and personal care are excluded from the scope of household 

production. Discussions of various methods of putting a monetary value on household production in the 

US date back to Kneeland (1929) and Reid (1934). In his work on national income accounts, Kuznets 

(1946) acknowledged the principle of using a “housekeeper wage” method, usually described today as the 

“generalist wage” method. Countless generations of students have since heard about how GDP would 

decrease when the economist marries his or her housekeeper, illustrating the omissions in national 

accounts. There are several excellent overviews of valuation methods (e.g., Goldschmidt-Clermont 1993; 

National Research Council 2005). In light of the existing literature, our focus in this chapter is to outline 

our methodological and empirical choices in defining and valuing household production. Since official 

US economic statistics, usually dubbed “satellite accounts,” already include aggregate estimates of the 

value of household production, we compare and contrast our procedure with the existing official 

methodology. We feel that such an exercise is particularly appropriate because the present research is 

expected to contribute to an expanded measure of consumption that another government agency, the 

Bureau of Labor Statistics, plans to assemble.

2.2 Definitions

We chose a household production definition suitable for a broad consumption measure at the household 

or consumer-unit level. Accordingly, our measurement excludes nonmarket services for people outside 

their household (e.g., friends or relatives) rendered by household members. By the same logic, we include 

nonmarket services received by members of the consumer unit from people outside their household 

because those services expand the consumption possibilities of the consumer unit—our object of 

measurement. In contrast, who receives the services is not relevant for aggregate measures of household 

production. Hence, the satellite account of household production developed by the US Bureau of 
21

Economic Analysis (BEA) includes the time spent on providing nonmarket services to nonhousehold 

members in its scope (Bridgman, Craig, and Kanal 2022).

Household production can include the production of both goods and services. Historically, in the United 

States, home production of various food items and clothing was a substantial contribution made by 

women to sustain family living standards (Kneeland 1929).2 In farm families, some of the output is 

consumed on the farm, not just for intermediate but also for final uses. The aggregate personal 

consumption expenditures in the official US national accounts include the imputed value of farm products 

destined for final consumption of the farm families (Line 202, Table 7.12).3 In 2021, it amounted to 

roughly $200 million, a tiny percentage of personal consumption expenditures. Increasing 

commodification of everyday life, i.e., growth in the share of business enterprises in the supply of 

consumer goods, has seen the category of goods gradually disappear from production for own use by 

households. No systematic data exists regarding the output of goods for own use, such as nonfarm 

production of fruits and vegetables.4

A lacuna exists on the input side as well. However, the ATUS includes several categories of time-use, at 

least some of which may be dedicated to producing goods for own use. These categories are the following 

(with examples indicating an activity that leads to the production of a good provided in the parentheses 

after each type): “Sewing, repairing and maintaining textiles” (knitting sweaters), “Food and drink 

preparation” (brewing beer), “Building and repairing furniture” (building a table), “Heating and cooling”

(chopping firewood), and “Lawn, garden and houseplant care” (planting herbs or vegetables). 

Additionally, the ATUS includes two categories that may include the time spent on producing tangible 

assets that provide consumption flows for several years: “Exterior repair, improvements, and decoration”

(building a garage) and “Ponds, pools, and hot tubs” (e.g., putting in a pool). While we cannot explicitly 

identify the time spent on producing goods or tangible assets per se, we include these broader categories 

of time use in our definition of household production.

Turning to the self-provisioning of services, we can categorize the activities into housework (e.g., 

cleaning the home) and direct care of household members in need of assistance. Our preferred definition 

of childcare is broader than that employed in the BEA definition. Folbre et al. (2005) have argued that the 

standard childcare measure focuses on activities alone and can be considered active childcare. However, 

2 For a critical historical interrogation of the notion of the homemaker, see Coleman (1998); Shapiro (2009) provides 

a social history of the transformation of production of food at home.

3 Table 7.12 “Imputations in the National Income and Product Accounts” (Last revised on September 30, 2022).

4 The ATUS does collect information on the time spent on “lawn, garden and houseplant care” (020501), which 

should include the time inputs into growing fruits, vegetables, and herbs, and travel related to this activity. We have 

included this category in our definition of housework described below.
22

active childcare underestimates the time demands of childcare because it ignores the on-call aspect of care 

responsibilities. Suh and Folbre (2015) describe the latter as the time spent on supervisory care and 

propose a methodology to measure it in the ATUS, which we follow. Purchases of childcare services 

outside the home tend to reduce supervisory constraints far more than active childcare time, a finding 

supported by analysis of data from the Panel Study of Income Dynamics as well as analysis of the impact 

of maternal employment hours on active and supervisory care in the ATUS (Gautham and Folbre, 

forthcoming, Suh and Folbre 2023).

Definitions of time devoted to household production categories by members of the household for the 

household are operationalized using the codes from the ATUS activity lexicon pertaining to the 2019 

round and, for supervisory care, are based on the measure of “in your care” time that the ATUS labels 

supervisory care, adjusted by some other variables (such as overlaps with other adults) available in the 

public-use data file (Table 2-1). Household production by household members for their household (selfprovisioning) consists of five broad categories: cooking, housework (except cooking), shopping, care of 

household adults, and care of household children. Further, we list two categories of nonmarket care the 

household receives from people outside the home: care of children and adults. They draw on data sources 

other than the ATUS and are discussed in detail later (Chapter 5).

Table 2-1 Categories of household production included in the study

Number Time category ATUS codes, ATUS variable name, or 

Description of data source

1 Cooking

Food and drink preparation; presentation, and 

clean-up

0202

Related travel 180202

2 Housework (except cooking)

Housework (except cooking) 02 (except 0202)

Related travel 1802, excluding 180202

3 Shopping

Using financial services 0802

Using legal services 0803

Using household services 0901

Using home maint/repair/décor/construction

services

0902

Using pet services 0903

Using lawn and garden services 0904

Using vehicle maintenance or repair services 0905

Using government services 1001

Telephone calls to/from salespeople 160104
23

Number Time category ATUS codes, ATUS variable name, or 

Description of data source

Telephone calls to/from household services 

providers

160106

Travel related to consumer purchases 1807

Travel related to using financial services 180802

Travel related to using legal services 180803

Travel related to using household services 180901

Travel related to using home 

maint/repair/décor/construction services

180902

Travel related to using pet services 180903

Travel related to using lawn and garden services 180904

Travel related to using vehicle maintenance or 

repair services

180905

Travel related to using government services 181001

4 Care of household adults

Activities related to the care of household adults 0304, 0305, 0399

Related travel 180304, 180305

5 Childcare

Active childcare

Activities related to the care of household 

children

0301, 0303, 

Using paid childcare services 0801

Related travel 180302, 180303, 180801

Supervisory childcare (in-your-care) TRTHH_LN*

Overlapping with other household production Supervisory childcare while engaged in 

activities numbered 1,2, 3, or 4 above

Non-overlapping with other household 

production

Supervisory childcare less overlapping 

supervisory care

6 Care received from people outside the 

household

Care received by children Early childhood program participation survey

Care received by adults Health and retirement survey

*Total time spent on secondary childcare for household children under 13 years. The variable is available in the 

ATUS public-use data files.

As described above, we have included the category of supervisory childcare or “in-your-care” time. We 

measure it as the total time spent on providing secondary childcare for household children under 13 years

of age. The ATUS considers the respondent as engaged in secondary childcare based on several questions 

regarding whether the person had a child under 13 in their care during the activity, among other 

considerations. In particular, if the activity involves sleeping, providing active childcare, or takes place 

when the children are asleep, the time for that activity is excluded from the measure of secondary care. 

We measure overlapping care time as time when a household child under 13 years was in the care of the 
24

respondent while the latter was engaged in activities we define as cooking, housework (except cooking), 

shopping, or care of household adults. For example, if a 5-year-old was in the respondent’s care during 

the 30 minutes the respondent spent on cooking, we will count those 30 minutes as 30 minutes of 

overlapping care time. The non-overlapping amount is then calculated as a residual by subtracting the 

overlapping time from the total time spent providing secondary care.

The ATUS collects information on one respondent aged 15 or older from each sample household. We use 

this information to impute time spent on home production by each individual above 14 years of age in the 

CE Interview and Diary samples. We define the total time spent on household production by a household 

member as the sum of the time spent on cooking, other housework, shopping, care of household adults, 

active care of household children, and non-overlapping supervisory care of household children. To avoid 

double counting, we exclude the time spent on overlapping supervisory care from the calculation of total 

time spent on childcare and household production.5

As noted above, we also impute the hours spent by people outside the household to provide care to 

individuals in the CE samples. To obtain the consumer-unit level estimates of hours spent by household 

members for self-provisioning and spent on household members by those outside the household, we add 

up the hours of each person in the unit. An exception is made, however, for time spent on supervisory 

care. The adjustment is required because it is not uncommon for another adult to be present in the same 

room when an adult reports that a child under 13 is in their care (see Chapter 3 for the adjustment). We 

regard only the presence of one adult as socially necessary to provide supervisory care.

2.3 Valuation

Most literature on converting household production time into monetary values uses generalist, specialist, 

or own wages. The BEA, for example, uses the average hourly compensation of private household 

employees. The BEA method reflects the principle behind the generalist wage—using a single hourly rate 

to value the diverse tasks included in household production. On the other hand, advocates of the specialist 

wage argue that different broad categories of household production require the application of different 

hourly rates. For example, the National Research Council (2005) proposed using a specialist wage method 

whereby the specialist hourly rate is multiplied by a factor (a number ranging from zero to one). The 

5 Suppose that a person spends an hour cooking that does not overlap with supervisory care and another hour on 

cooking that overlaps with supervisory care. Also, assume that she or he does not spend time on any other 

housework, active childcare, or adult care. We will calculate the total time spent on household production by the 

person as the total time spent cooking, i.e., two hours. We avoid double counting by not considering the hour she or 

he spent cooking while engaged in supervisory care as an hour of cooking and an hour of supervisory care.
25

factor is supposed to reflect the relative productivity of the household member who performs the task 

(National Research Council 2005, 70). The third method of own wage is derived from the notion of 

opportunity costs and uses an hourly rate that corresponds to the household member’s actual or potential 

wage. 

The generalist and specialist wage methods are attempting to answer the question of market replacement:

i.e., how much would it cost to procure market substitutes for home production? Advocates of generalist 

wages argue that the appropriate replacement is hiring household help to perform all housework and care 

for dependents. In contrast, the assumption behind the specialist wage is that replacement would entail 

hiring workers to perform different tasks that would otherwise have to be carried out by household 

members. The own wage approach does not address the same question of replacement. Instead, the 

strategy focuses on the wage the household member could earn if devoting the same time to paid 

employment.

We are in favor of the specialist wage method. However, given that our valuation exercise uses imputed 

data, we propose a lower level of disaggregation of activities than in some previous estimates of the value 

of nonmarket household services (Suh and Folbre 2015). Of the time categories included in our definition 

of household production, we consider “Cooking” as a separate category for valuation because previous 

research reveals considerable substitutability between purchased and home-produced meals. Another 

rationale for this distinction is that cooking is a more regular daily activity than many other categories of 

unpaid work. Our hourly rate for cooking is the average wage of cooks in various occupational groups we 

calculated from the 2019 Occupational Employment and Wages Survey (OEWS), a survey of 

establishments conducted by the BLS.6

Next, we combine the time categories “Housework (except cooking)” and “Shopping” into a single 

category for valuation. A generalist wage is assigned as the hourly rate for this category. Finally, we use 

the same source of data as in the BEA estimates, viz., the Merged Outgoing Rotations Group (MORG) 

earnings data compiled from the monthly CPS (2017–19 pooled sample) and calculate the hourly earnings 

of workers employed in the industry “Private households” (industry code 9290).

Methodological considerations regarding the use of imputed data also prompt us to consider active 

childcare as a single category rather than distinguishing among physical, developmental, and managerial 

6 The occupational groups are: Cooks, fast food; Cooks, institutions and cafeteria; Cooks, restaurant; Cooks, short 

order; Cooks, all other; and Cooks, private household. OEWS occupational codes for the groups are in the listed 

order: 35-2011, 35-2012, 35-2013, 35-2014, 35-2015, and 35-2019.
26

care.7 We believe a separate specialist wage should be used for the valuation of unpaid childcare, based 

not on the occupational wage of childcare workers but on that of early childhood educators. Specifically, 

we use the mean combined hourly wage for the occupational groups “Preschool teachers, except special 

education” (OEWS code 25-2011) and “Kindergarten teachers, except special education” (OEWS code 

25-2012). Much concern has been expressed about the underpayment of childcare workers in the US. For 

instance, the Build Back Better initiative recently passed by the US Congress envisages early childhood 

educators to be paid on par with K–12 educators. In some countries, including France, childcare workers 

are paid approximately the same as elementary school teachers.

Some previous research has valued developmental childcare at a higher rate than other childcare activities 

(Suh and Folbre 2015). As mentioned, we cannot meaningfully disaggregate childcare activities in this 

imputation. Most parents incorporate developmental efforts into their interactions with small children, and 

they develop child-specific skills that enhance their productivity in many respects. Some basic level of 

parental care is critical to successful child socialization and capability development. Many parents have 

less education than is characteristic of preschool and kindergarten teachers, and the home environment 

lacks the extra stimulation provided by children of similar age. On the other hand, the primarily one-onone interactions of childcare in a home environment are typically high quality. Small children often enjoy 

the undivided attention of a parent or other family member. 

The cost of out-of-home childcare is a more relevant replacement cost for most families than hiring 

household help, and it is undoubtedly utilized by a larger number of families with children. However, 

most estimates of the cost of out-of-home childcare services are based on weekly rather than hourly rates 

and utilization hours are heterogeneous. An additional consideration is that the cost of out-of-home 

childcare understates the value of childcare because it is strongly influenced by the relatively low pay of 

childcare workers, as we mentioned earlier. For these reasons, the input-based specialist replacement cost 

estimate for early childhood educators probably remains the best choice.8

7 While these disaggregated categories will not be imputed on the household level, it will be possible to apply a set 

of ATUS averages for the composition of active childcare to households in the merged data set to provide an 

approximation.

8 A recent study on Austria argues that the wage information from digital platforms is superior to that derived from 

labor force surveys in some respects; the platforms report wages specific to tasks that would be performed in 

individual households and are often geographically detailed (Jokubauskaitė and Schneebaum 2021). Several digital 

platforms operating in the US provide data on wages advertised by job applicants. However, aggregate data does not 

appear to be available (or scrapable) at this time. More importantly, the characteristics of the self-selected samples 

are unclear, and almost certainly more upscale. A methodological issue that emerges from the consideration of data 

from digital platforms is the economies of scale in childcare: the differential in hourly cost between in-home nanny 

care and center-based care shrink with the number of children under care. This has implications for differences 

between an input-based valuation based on replacement cost and an output valuation based on purchase of services 

outside the home (for a detailed discussion of this issue, see Mullan 2010).

28

Table 2-2 Percent of parental supervisory (in-your-care) time overlaps with leisure and unpaid housework 

and procurement activities in households with children, no other adults present (ATUS 2004–2019)

One might argue that the marginal cost of supervision is small if the respondent plans to perform 

housework. Still, the causality often runs the other way, i.e., a parent who must be at home to supervise 

children is more likely to perform housework or shop because engaging in other productive activities is 

impractical.10 Given the existing structure of wage rates that we use in our valuation, the implication is 

that combined housework–supervisory care time would be valued more highly than active childcare and 

adult care. It is difficult to say whether this form of multitasking delivers the same quality as solo tasking 

or whether it lowers the quality and should therefore be somewhat discounted. From a consumption-value 

perspective, it may be argued that the combined hour should be valued, if we assume no significant 

10 This has changed since 2019 in the wake of increased adoption of technologies that facilitate telecommuting.

Any child under 

13

Children 0-4, 

none older

Children 5-12, 

none younger

Fathers

In-your-care time overlapping 

with unpaid housework and 

shopping

25% 25% 24%

In-your-care time, 

overlapping with leisure

51% 50% 52%

In-your-care time, 

overlapping with either 

leisure or unpaid housework 

and shopping

75% 75% 76%

Mothers 

In-your-care time, 

overlapping with unpaid 

housework and shopping

38% 37% 36%

In-your-care time, 

overlapping with leisure

38% 38% 40%

In-your-care time, 

overlapping with leisure and 

unpaid housework and

shopping

76% 75% 76%
29

reduction in quality, at an even higher rate than we propose. Like other decisions regarding replacement 

wage rates, this one is based more on intuition than evidence and deserves further consideration.

Finally, adult care often entails responsibilities—including medical supervision of frail elderly or others 

experiencing long-term disabilities—that require commitment and management skills. Like childcare 

workers, elder care workers are generally considered underpaid in the US, especially when employed in 

home- and community-based care where labor standards are poorly enforced. As with childcare, we have 

good reason to believe that unpaid care provided by family members entails person-specific skills, oneon-one attention, and social-emotional benefits, all of which should be considered in its valuation. For 

these reasons, we chose an occupational category that, like that chosen for specialist valuation of 

childcare, is a step up from the most poorly paid, high-turnover occupation of “Home health and personal 

care aides” (OEWS code 31-1131). The best candidate may be “Nursing assistants” (OEWS code 31-

1120). These workers “perform duties such as monitoring health status, feeding, bathing, dressing, 

grooming, toileting, or ambulation of patients ... medication administration and other health-related 

tasks.”11 National estimates for 2021 from OEWS data show that the average hourly wage was $14.07 and 

$15.99, respectively, for home health aides and nursing assistants. We chose the latter for our valuation of 

adult care.

We also include care received from people outside the household in our definition of household 

production (Table 2-1). We apply the same rate for childcare for active childcare rendered by a household 

member. Similarly, we value care for adults given by people outside the household at the same rate as 

members of the home.

We constructed an alternative set of estimates to assess the sensitivity of our preferred definitions. They 

exclude supervisory care and care received from people outside the household. Further, the valuation of 

the remaining activities of household production shown in Table 2-1 was performed using the generalist 

wage. Because the scope of household production and valuation method in these alternative estimates are

similar to the BEA methodology, we refer to them as the “BEA method” in our comparisons (Chapter 5).

As with many occupations, there is considerable geographical variation in the wages of the occupational 

groups we discussed (Figure 2-1). Therefore, we recommend using at least state-level wages in the CE 

internal data. In the public-use data, the Census division is the lowest level of geography available for all 

records. Consequently, our valuation exercise used the average wage for each occupation, differentiated 

by division.

11 This description is from Occupational Employment and Wages, May 2021. Nursing assistant definition is 

available at: https://www.bls.gov/oes/current/oes311131.htm
30

Figure 2-1 Hourly wage rates used in valuing household production by category (2019 dollars)

As expected, wages for all categories are generally higher in the New England, Mid-Atlantic, and Pacific 

divisions, compared to other areas. In all divisions, the average wage for active childcare is the highest, 

followed by that for adult care. We found the gap between the two wage rates to be rather small in three 

of the nine divisions: East North Central, Mountain, and Pacific. Two wage categories that correspond to 

housework—cooking and generalist—do not show a clear ranking across divisions. In the New England 

and Pacific divisions, cooking has a clear lead over generalist, while in East South Central, the reverse 

holds. Across the other six divisions, a notable difference between the two is absent. An anomaly is 

presented by the East South division, where the adult care wage is below that of the generalist. The lowest 

wage in all divisions is that assigned to supervisory care, which is half of that for active childcare. Hence, 

the variation across regions is the same for both categories of childcare.

Notwithstanding the regional diversity, the national pattern of our chosen wage structure ranks active care 

work in households higher than household chores, and supervisory care ranks the lowest. Examining the 

range of wages across categories gives us a fuller picture regarding valuation. The wage for cooking falls 

between $11–$16 across regions, while the generalist wage has the tightest band with a range of $12–$14. 

The ranges for the two care categories are $13–$17 and $15–$19 for adult care and active childcare, 

respectively. Given the closeness of the wage band for active childcare to that of other categories, and our 

method of valuing supervisory childcare at half of that for active childcare, we end up valuing an hour of 

any class of household production (cooking, other housework, and adult care) that overlaps with 

0.0

5.0

10.0

15.0

20.0

25.0

New

England

Mid

Atlantic

EN Central WN

Central

South

Atlantic

ES Central WS

Central

Mountain Pacific

Cooking Generalist Adult care Active childcare Supervisory care
31

supervisory care at a higher rate than an hour of active childcare. Since active childcare has the highest 

wage, the combined effective wage of supervisory care that overlaps with household production will also 

be higher than for any other category of household production.

2.4 Summary

We have outlined the list of nonmarket activities that need to be brought into the picture in a broad 

measure of consumption. Our scope of activities differs from the standard definition used in most 

previous work (e.g., by the BEA), in that we account for supervisory care or in-your-care time with 

children and care received by household members from people outside the household. These extensions 

are warranted because they contribute to households’ potential consumption. We also specified a 

valuation procedure to convert household production hours into monetary units. In contrast to the current 

BEA practice of using the oldest and most widely deployed generalist wage method, we favor the 

specialist wage approach, which, in fact, was used by earlier research on incorporating household 

production in US national accounts (Landefeld, Fraumeni, and Vojtech 2009).12 Our specialist wage 

structure assigns a greater hourly value to active care for dependents than to housework.

12 However, still earlier work by Landefeld and McCulla (2000) used the generalist wage.
32

3 Imputing Home Production by Members of the Consumer Unit

We begin this chapter with an overview of the methods previously used in the literature on imputation. 

Our focus is on studies attempting to impute the time spent on household production, especially on efforts 

to combine time use and consumption expenditures. The subsequent section outlines the methods of 

imputation we use in the project in a more or less formal manner. We do not aim to provide all the 

technical details since our techniques are relatively well known, and excellent treatments of the various 

methods are readily available elsewhere. Instead, our exposition is biased toward those who may not be 

immersed in the literature on imputation. The following section is the longest. We present details on the 

implementation of imputation methods using the datasets for this project. Alternative methods are also 

assessed for the quality of imputations that they generate, in other words, how “closely” they can mimic 

the features of the observed, i.e., time-use survey, data. We put the term “closely” in quotes because 

defining closeness involves ingredients of judgment and emphasis rather than formulaic definitions, as we 

shall see below. A validation exercise using data from the Panel Study of Income Dynamics is discussed 

in a companion appendix to the chapter (Appendix A). As discussed there, all imputation methods rely on 

an assumption known as the conditional independence assumption (CIA). A direct test of this assumption 

is generally impossible; our validation study offers the next-best alternative, given the currently available 

data.

3.1 Background

Previous research has combined microdata from time-use surveys and income or expenditure surveys for 

various research goals. Perhaps the earliest instance for the United States is that of Fuchs (1986), who 

attempted to estimate total hours of work, leisure, and income for men and women separately using the 

1975–76 time-use survey (TUS)13 and the public-use samples from the decennial censuses of 1960, 1970,

and 1980. The method used by Fuchs for his 1980 estimates consisted of two steps: (1) estimating an 

ordinary least square regression (OLS) of hours of nonmarket work or household production on a set of 

explanatory variables in the TUS, and then (2) fitting the estimated model to the Census data. Fuchs 

obtained the OLS estimates of the core subgroup of interest to him—the working-age population (ages 25 

to 64)—separately for men and women. The explanatory variables were the dummies for ages 45–64, 

Black, married, full-time employment, part-time employment, and the presence of a child under age 5 in 

the household. For those ages 18–24 and 65 and over, the OLS estimates were obtained with age, 

13 This survey was conducted by the Institute for Social Research at the University of Michigan. See Juster et al. 

(1979) for documentation. 
33

dummies for sex, marital status, and employment status as independent variables (Fuchs 1986: S270–

S271).14

A recent application of the same method is worthy of mention in our context because it involved 

imputation of time use in the CE. The study sought to estimate the joint distribution of family-level per 

capita leisure and consumption using time-use and CE for four years between 1975 and 2016 (Han, 

Meyer, and Sullivan 2020). Family-level leisure and consumption are defined as weekly leisure per adult 

and yearly amount per equivalent adult, respectively. In addition to the 1975–76 TUS mentioned above, 

the authors use the 1985 unofficial TUS as well as the 2003 and 2016 rounds of the ATUS. Leisure time 

was imputed for individuals in the CE in the corresponding years (except that the 1975–76 TUS is paired 

with the 1972–73 CE). Family-level leisure is computed as the average for adults in the household. OLS 

estimates used in the imputation are the coefficients for the independent variables by year obtained by 

pooling the various time-use surveys. The independent variables used in the analysis included individual 

characteristics (dummies for age group, sex, educational attainment, and marital status; and usual weekly 

hours of employment) and family characteristics (number of children under five, number of children 

under 18, number of adults). An important difference from Fuchs (1986) is that Han et al. (2020) attempt 

to control for within-year variation (“transitory variation”) in leisure by including interview-month 

dummies and interactions of the year with interview day of the week as regressors.15

A modification of the “fit and predict” method of imputing household production was introduced by 

Jenkins and O’Leary (1996) in their study of the distribution of extended family income (standard money 

income plus the value of household production) in the UK (see Frazis and Stewart [2011] for the US). 

The modification consisted of adding an individual-specific random number drawn from a normal 

distribution with mean zero and variance set equal to the estimated variance of the error term in the model 

fitted in the TUS, viz., the Time Budget Extension of the 1987 ESRC Social Change and Economic Life 

14 Schor (1992) employed the same method for imputing hours of household production in the March CPS 

supplements of 1970, 1974, 1980, and 1988. She used the TUS used by Fuchs (1986) and the 1981 wave of the same 

TUS. OLS estimates were obtained for women and men separately. The regressors included dummies for age group, 

marital status, housewife, head of household or spouse of the head of household, Black, and other race, as well as 

family income, hours of employment, square root of the number of children under 3 years, and square root of the 

number of children above 3 years (Schor 1992, 171–74).

15Apps and Rees (1996), while deploying the same method of imputation in the Australian Bureau of Statistics 

(ABS) 1985/86 Income Distribution Survey with the OLS coefficients estimated from the 1987 ABS Time Use Pilot 

Survey, expanded the list of regressors in a notable fashion. In addition to household characteristics and own 

characteristics of the respondent, they incorporated characteristics of other household members, specifically, the 

married or cohabitating partner, such as their hours of employment and dummies for their occupational group (see 

Apps and Rees 1996, 217–18). The inclusion was prompted by the authors’ goal to compare the parameter estimates 

from two variants of the collective labor supply models: the “transfer model” which neglects the intrahousehold 

division of housework (by treating housework and leisure identically) and the “exchange model” that explicitly 

incorporates the gender division of housework.
34

(SCEL) survey. OLS estimates were obtained from the TUS separately for four groups of adults 

differentiated by sex and marital status. The regressors comprised respondents’ age group, employment 

status, and household composition. These estimates were then used to impute household production hours 

in the 1986 Family Expenditure Survey. However, the authors note that the modification of the method 

did not alter their most important conclusion that adding household production had an equalizing effect 

on income distribution (Jenkins and O’Leary 1996, 407n15). They also point out that the crucial 

assumption required to justify the imputation—the conditional independence assumption (CIA)—is hard 

to test in practice. As a substitute, they calculated the correlations between the total time spent by the 

household on domestic work and the poor-quality data on household money income for each value of the 

regressor variables used in the TUS regressions. Most of the correlations were not statistically different 

from zero, and they concluded that the assumption “may be a satisfactory approximation in the current 

context” (Jenkins and O’Leary 1996, 408). 

Kolodinsky and Goldstein (2011) represent a nonparametric approach to examining the relationship 

between obesity, time use, and food expenditure patterns in the US. Since no one survey contains all three 

variables, the authors turn to imputation to overcome the information gap. Specifically, they pool the data 

from the Eating and Health Module of the ATUS for 2006 and 2007. This module collects information on 

the height and weight of individuals. From this information, the authors compute the body mass index 

(BMI), which they use to measure obesity. Kolodinsky and Goldstein (2011) also utilize the demographic 

information on the respondents that is directly linked to their CPS record and available as a part of the 

ATUS. Their analysis focused on single-female household heads ages 31–50 and without disabilities. 

They use a nonparametric conditional-mean imputation approach to obtain the food expenditure pattern 

for the selected households. Specifically, they assign average values of food expenditures by detailed 

categories from the CE 2007 to the ATUS, using cells that are defined by gender, income, and family 

composition. As part of their statistical analysis, the authors model the likelihood of being obese as a 

function of demographic characteristics, time-use activities (related to activities or eating), and specific 

categories of food expenditures. The effects of (imputed) expenditure variables are identified in their 

statistical model only because imputed data was transferred nonparametrically (cell means), and no 

corrections were applied to standard errors in their analysis. 

Recently, researchers in the economics of transportation have also realized the importance of data that 

contains information on time spent on various activities and expenditures on associated categories of 

commodities that may complement, substitute, or serve as inputs for such activities. For example, 

Konduri et al. (2011) developed an a-theoretical statistical model that estimates the willingness to pay for 

leisure activities and a reduced-form linear expenditure system (the so-called Stone-Geary model) to 
35

ascertain the (opportunity-cost) value of leisure. Their empirical analysis uses the sub-samples of 

households with only one person who is also employed from the 2008 rounds of the ATUS and CE 

(Interview sample). The authors argue that a week is the appropriate period of analysis because 

expenditures of time and money follow weekly cycles (Konduri et al. 2011, 56). However, the ATUS 

collected the time diary for a single day from each individual in the sample. To overcome this limitation, 

the authors impute entire time diaries for each respondent for days other than their interview day. The 

imputation method is to take random draws with replacements from the sample for all days of the week 

(except the day of the interview), using a pool of donors based on matching demographics (such as 

gender, age, employment status, race, college status, family income, and employment category). 

Similarly, they also imputed expenditures to the ATUS respondents by conducting random draws with 

replacements from the CE sample, with the only difference being that the employment category was 

dropped from the list of matching characteristics. Weekly expenditures in the CE sample were created by 

converting quarterly expenditures via “naïve scaling.” While the authors describe their method of 

matching as simplistic, they do not adjust their statistical analysis to account for the imputation and 

consider the imputed variables on the same footing as the original variables in the ATUS.

Another example of combining expenditures and time use in the literature on transportation economics is 

Hawkins and Nurul Habib (2022). They attempt to create a synthetic data file by combining householdlevel expenditure data with individual time-use patterns, which should be statistically valid for urban 

simulation models. The authors argue that the main limitation of standard data fusion/statistical matching 

approaches is that they obfuscate spatial information, linking observations from different locations. The 

authors apply their proposed framework for the Greater Toronto Area in Canada, fusing data from the 

Canadian Census of Population (CCP 2016) for general demographics, the Survey of Household 

Spending (SHS for years 2012–16) for expenditures on goods and services, and the General Social 

Survey: Time Use Survey (TUS 2010 and 2015) for time spent on activities. In contrast with other 

methods, which use one of the surveys as baseline/recipient, transferring the required data from other 

surveys after matching, their approach uses the different surveys to identify the joint distribution of all 

variables of interest and create a fully synthetic dataset based on the information from the reference 

surveys. To do this, the authors utilize generative modeling algorithms borrowing from machine learning 

and Bayesian literature, specifically the algorithm proposed by Ye at al. (2017). A peculiarity of this 

approach is that there is, in effect, no “recipient” data file. They discuss the potential limitations and care 

required to consolidate the different time frames for data collection across surveys. Hawkins and Nurul 

Habib (2022) also acknowledge that not having access to time use for every day of the week, their 

methodology may ignore the correlation across substitutable day-to-day activities. The authors compare
36

the distributions of the analysis variables in the synthetic dataset using—for time use—data from the 

Transportation Tomorrow Survey (TTS-2016) and the CCP for sociodemographic patterns.

Kum and Masterson (2010) provide a review of statistical matching, describing different methodological 

choices that would link statistically similar individuals across surveys, thus allowing for the creation of 

synthetic datasets with information from both surveys. They discuss the matching algorithm used to 

construct the Levy Institute Measure of Economic Well-Being. The authors match records from the 

Survey of Consumer Finances (donor file) with records from the March supplement of the CPS using a 

rank of predicted propensity scores in a constrained matching procedure with strict strata variables to 

improve the quality of imputation. In contrast to other methods described above, the procedure adjusts 

survey weights to achieve almost perfect alignment and transfer the full wealth distribution from the SCF 

to the CPS. They also advocate for the use of sample segmentation or stratification. This stratification 

helps improve the match quality by restricting the potential donors and recipients across both files.

Distinct from other methods, the segmentations are dynamic, so the definition of matching groups is 

expanded after all possible observations within the more detailed segment definition have been utilized. 

The authors’ evidence suggests that statistical matching can transfer overall distributions almost perfectly 

to the donor survey, with minor losses when using some of the same variables used for the sample 

segmentation. They warn, however, of potential problems associated with statistical matching when the 

survey structures between donor and recipient are too dissimilar, as it may produce attenuation of 

inequality in the donor dataset.

As described below, our preferred method of imputation for the current project is a variant of the Kum 

and Masterson (2010) procedure, linking information from the time-use survey (donor) to the Consumer 

Expenditure Survey (recipient). The modifications include splitting survey weights to integrate the two 

files fully. We also deploy principal-component analysis and cluster analysis to pre-identify similar 

groups in the two files and improve the imputation quality.

3.2 Imputation methods

As described in Chapter 2, our definition of household production encompasses unpaid services rendered 

by household members and nonhousehold people for household members. Regarding time expenditures, 

services by own-household members far outweigh those by nonhousehold members. In our judgment, the 

data available for imputing the latter is also of lower quality—a point we elaborate on in the next 

chapter—than that available for the former, namely, the ATUS. Consequently, we produced imputations 

with three methods using the ATUS to allow choices for the data users depending on their research goals. 



40

principal component analysis (PCA) (as described in the later section on the matching process). Still, they 

rely on propensity scores estimated at the highest level of aggregation. We employ PCA as a data 

reduction technique to obtain a few principal components representing a more extensive set of observed 

characteristics. These principal components are then utilized to identify different groups of 

observationally similar individuals by applying a modified partitioning cluster algorithm. In turn, these 

subgroups are deployed as sub-strata in the statistical matching procedure.

We match observations based on rank similarity within subgroups. Thus, we sort the observations in each 

file by subgroup based on the estimated propensity scores. A record with a larger weight in the donor (or 

recipient) file will be split up or duplicated and matched with multiple records in the recipient (or donor) 

file until all of its weight is used. In practice, however, subgroups will not be of the same weighted size, 

and the weight-splitting will not completely match the records in both files using the most detailed 

subgroups. Thus, additional steps (i.e., using higher levels of aggregation for subgroups) will be required 

to accomplish this goal. Because multiple “donors” could be linked to a single “recipient” observation, 

and vice versa, we choose only one of all the possible candidates as the effective donor. We select the 

donor observation matched in the earliest round, representing the largest “share” of the weighted recipient 

observation.18

3.3 Imputation results

3.3.1 Household production for members of own household

3.3.1.1 Introduction

The main category of household production we impute is time spent on household production by 

members of the household for their household. Our source data or donor file is the 2019 American Time 

Use Survey (ATUS), which collected 24-hour time diaries from a sample of individuals aged 15 years or 

older. We imputed hours for each individual 15 years or older in the Consumer Expenditure Survey (CE). 

Imputations were carried out for the 2019 CE Interview and CE Diary samples, using each quarter 

separately.19 Separate imputation is warranted because the Interview and Diary samples are independent, 

18 For example, if recipient r1 is matched with donors d1 and d2 in rounds 3 and 5, donor d1 is selected as the 

effective donor. If recipient r1 is matched with donors d1 and d2 in the same round, the donor with the largest split 

weight is chosen as the effective donor.

19 For the Interview sample, we separately matched memi191x, memi192, memi193, memi194, and memi201 data 

files. Separate matches were also conducted for the Diary sample files, memd191, memd192, memd193 and 

memd194. We used information from the corresponding fmli files to construct household-level variables (such as 

family income) to be used in the matching. For each of the CE recipient files, the full-year ATUS 2019 was the 

donor file.
41

and each quarter is considered independent. It is also desirable for several analytical purposes of the 

matched data because users may want to consider only one of the samples. For example, relatively more 

frequent expenditures, such as food expenditures, are captured better in the Diary sample. Users may want 

to examine the correlation between those expenditures and time spent on housework or shopping. 

We explored two options for combining the information from the two surveys. One procedure involves 

treating all the records in the ATUS simultaneously as donor records, while conducting separate 

imputations with the Interview and Diary samples. A potential drawback of this procedure may be seen 

by considering that the ultimate objective of the exercise is to develop household-level estimates of the 

value of household production. The procedure has no explicit way of preventing, e.g., in the context of the 

statistical matching, all or a majority of individuals in a CE consumer unit from being assigned only 

weekend or weekday diaries. Such a result would mean we do not have a reliable picture of household 

production during the average day in the week of the household. Further, the mix of weekday and 

weekend records in consumer units can unpredictably vary across subgroups of households, thus making 

intergroup comparisons problematic.

The alternative procedure entails treating weekday and weekend diaries as separate donor files. Each 

record in each CE sample is then imputed with weekday hours and weekend hours separately from the 

respective donor files. We can then calculate the amount of time spent on household production during 

the average day of the week by the individual in a given CE sample as a weighted average of the time 

imputed from the weekday and weekend matches, respectively. The averages thus calculated for 

individuals in the household can then be added up to obtain the household-level total of time spent on 

household production, avoiding the potential bias from treating the weekday and weekend diaries as a 

single donor file. We consider this second procedure to be the most appropriate, as it would better capture 

the potential heterogeneity in time allocation between weekdays and weekends. 20

A crucial assumption underpinning all imputation methods is that the donor and recipient files represent 

the same underlying population. Therefore, we compared the demographic picture conveyed by the 

ATUS and CE samples to assess their mutual correspondence. Given the differences in sample design, 

survey implementation, and sampling variance, we do not expect the picture to be identical, yet, a close 

20 In the ideal scenario, one would prefer using each day of the week as a separate matching task, so that we could 

better reconstruct the time-use heterogeneity across a typical week, month, or year. However, a potential drawback 

of treating the weekday and weekend diaries as separate donor files is that the size of the donor pool is cut in half 

because the time diaries in the ATUS are split equally between weekdays and weekends. The smaller donor pool can 

affect the quality of the imputation, because each ATUS subsample may be less representative of weekend or 

weekday time-use activities. Using weekend and weekday time–use data separately is a good compromise, given the 

time-use heterogeneity, although we do not recommend using a lower-level subsampling (for example, using quarter 

or month).
42

resemblance is desirable and necessary for effective imputation. Table 3-2 provides the total number of 

observations across each survey and the total number of weighted observations.

Table 3-2 Weighted vs. actual observations across surveys (individuals aged 15 years and older)

Sample Weighted Obs. N

CE Interview

memi191x 262,286,293 10,861

memi192 261,946,747 10,630

memi193 263,038,925 10,357

memi194 263,864,691 10,158

memi195 263,070,970 10,124

CE Diary

memd191 262,018,321 5,156

memd192 262,356,969 5,353

memd193 263,622,588 5,380

memd194 263,390,712 4,952

ATUS

ATUS Weekday 263,653,004 4,642

ATUS Weekend 262,635,719 4,793

Roughly speaking, all samples represent approximately the same number of individuals (263 million). 

The actual sample sizes are different, and the ATUS sample is considerably smaller. In the context of 

statistical matching, the differences in size imply that each ATUS (weekend or weekday sample) record

may be used as a donor record, on average, for up to two observations in the CE Interview sample. 

Consequently, the variances of the imputed conditional distribution we may observe in the CE data may 

be a slight understatement of the “true” but unknown conditional distribution.

21

As described below, gender, presence of children in the household, and employment status are crucial in 

our imputation exercise. They are also widely recognized in the literature as driving factors behind 

variations among individuals in the time spent on household production. A simple comparison of the 

gender-wise distribution of employment status and children’s presence in the household across all 

samples is shown in Table 3-3. We report average characteristics across all quarters for the CE rather than 

consider each quarter’s statistics separately. The statistics suggest a reasonable balance across all samples

regarding this particular classification of individuals. Additional tables comparing the distribution across 

a more extensive set of variables used in the analysis can be found in the online data appendix.

21 In this case, conditional distribution refers to the distribution in time use we would expect to see among households 

with exactly the same characteristics.
43

Table 3-3 Composition of the sample by sex, employment status, and presence of children in the 

household (percent) 

Note: CE Interview and CE Diary correspond to pooled data across all available quarters.

To summarize the findings from the detailed tables provided in the online appendix: there is a close 

correspondence of the critical characteristics across all surveys, with a few cases where the 

correspondence is less than desirable. We describe a few of these cases:

- In the CE Interview and Diary samples, there are about 3 percentage points fewer individuals 

with a GED and 2 percentage points fewer individuals with an advanced degree (Master, Ph.D.,

or similar) compared to the ATUS, but a greater presence of individuals with only some college 

education (3–4 percentage points higher)

- A slightly larger proportion of individuals in households with children in the CE than in the 

ATUS.

- A higher share of homeowners (4.5 percentage points more) in the ATUS than the CE.

- There are some differences in the distribution of individuals across annual income levels, 

especially for those in the $100K–150K bracket.

While these differences may seem notable individually, the overall differences in sample composition are 

not as pronounced. For instance, Figure 3-1 plots the kernel density of the first principal component built 

on all selected characteristics, which is used later for matching. Since a principal component is a linear 

CE 

Interview

CE 

Diary

ATUS 

Weekday

ATUS 

Weekend

Men

Not employed; no children 20.58 20.46 20.61 22.11

Not employed; with children 8.39 9.08 6.83 7.80

Employed; no children 42.09 42.51 44.77 43.93

Employed; with children 28.94 27.95 27.79 26.16

All 100.00 100.00 100.00 100.00

Women

Not employed; no children 25.61 26.83 27.68 25.88

Not employed; with children 14.03 12.97 11.64 12.59

Employed; no children 34.76 34.50 35.65 36.95

Employed; with children 25.59 25.70 25.03 24.58

All 100.00 100.00 100.00 100.00
44

combination of characteristics that explain most of the variation of the data, it can be used as a simple 

approach to compare the balance across two datasets. This use of principal component analysis is similar 

to applying propensity scores for matching and balancing assessments in the policy evaluation literature. 

Although some differences in the densities exist, they follow each other closely, suggesting good balance 

across all surveys.

Figure 3-1 Density of the first principal component

Note: For the CE Interview, and CE Diary, the plot corresponds to all quarters’ pooled information

3.3.1.2 Imputation by matching

3.3.1.2.1 Variables and cell construction

We describe the variables used to implement the matching algorithm and propensity score estimation in 

Table 3-4. Propensity score estimation was conducted by interacting all variables with gender and the 

presence of children. We can classify the variables used in the process into three groups: principal strata, 

secondary strata, and others. A principal strata variable is kept in all rounds of matching. In our 

application, all statistical twins in the match are defined within the cells “men with children,” “men 

without children,” “women with children,” and “women without children.” We retained secondary strata 

variables in the first 12 rounds of matches, which, as shown below, encompass most observations. We 

chose these strata variables because the differences in household production among these subgroups are 

known to be sizeable. Finally, variables classified as “Other” are used more flexibly in the matching 

process to define subgroups.
45

Table 3-4 Variables used in matching and propensity score estimation

Variable Description

Principal Strata

Gender Dummy for male/female

Co-residence with children Dummy for any child 0–17 in the household

Secondary Strata 

Employment status Dummy for whether a person is currently employed or not

Number of children (0,1,2,3+) Dummies for the number of children in the household

Other Variables

Own Characteristics Interviewed person

Age Age in years

Education level Level of education

Self-employment status Dummy for self-employment

Race White, Black, Hispanic, and other

Householder 1’s characteristics Husband in a couple-household, older male or female in a household with 

a single reference person, and male or female in a same-sex household

Gender Dummy for male/female

Age Age in years

Education level Level of education

Employment status Dummy for whether a person is currently employed or not

Self-employment status Dummy for self-employment

Race or ethnicity White, Black, Hispanic, and other

Householder 2’s characteristics Wife in a couple-household, younger male or female in a same-sex 

household, in a household with a single reference person

Gender Dummy for male/female

Age Age in years

Education level Level of education

Employment status Dummy for whether a person is currently employed or not

Self-employment status Dummy for self-employment

Race or ethnicity White, Black, Hispanic, and other

Household Characteristics

Couple/single household Type of household (Single headed or couple head)

House tenure Owner or renter

# Children 0–5 Number of children in the household between 0 and 5 years of age

# Children 6–12 Number of children in the household between 6 and 12 years of age

# Children 13–17 Number of children in the household between 13 and 17 years of age

# Male Adults 18–64 Number of male adults in the household

# Female Adults 18–64 Number of female adults in the household

# Older Adults 65+ Number of older (65+ years) adults in the household

# Employed Adults 18–74 Number of employed adults in the household

Family income group Family pre-tax income group (based on ATUS classification)

47

data is matched in the first round when small cells are used, compared to 47 percent when small cells are 

not allowed. While this may suggest that imposing the cell-size constraints affects the match quality

negatively, one should also consider that using those micro-cells may be equivalent to overfitting, which 

can negatively affect the overall match quality. However, as shown in Figure 2, there is little loss in terms 

of matching quality when imposing the cell-size restriction.

Table 3-5 Share of the total number of observations matched in each round (percent) in the matching of the 

CE Interview sample with ATUS

No small cells With small cells

Round

Number 

of cells

CE Interview CE Diary CE Interview CE Diary

1 1502 47.16 46.11 74.52 70.31

2 1381 7.88 8.49 5.09 6.06

3 1238 3.94 4.07 2.81 3.38

4 1084 4.23 4.30 1.77 2.05

5 930 3.30 3.18 1.50 1.79

6 791 4.01 4.07 1.26 1.79

7 635 3.84 3.74 1.11 1.30

8 476 3.94 3.58 1.31 1.62

9 318 6.28 5.97 1.82 2.12

10 160 6.20 6.16 2.35 2.34

11 400 2.00 2.16 1.38 1.57

12 360 0.92 0.77 0.26 0.32

13 320 0.42 0.52 0.14 0.19

14 280 0.36 0.38 0.15 0.15

15 240 0.25 0.35 0.07 0.14

16 200 0.32 0.38 0.10 0.12

17 160 0.17 0.27 0.12 0.13

18 120 0.33 0.40 0.16 0.20

19 80 0.25 0.49 0.13 0.29

20 40 0.52 0.59 0.39 0.37

21 140 1.72 1.89 1.60 1.74

22 20 0.70 0.91 0.70 0.70

23 4 1.28 1.23 1.28 1.31

Given that a substantial proportion of records are matched in the later rounds, a natural question arises: 

what is the distribution of characteristics of individuals matched in earlier rounds compared to those 

matched in later rounds in the donor and recipient files? Intuitively, we would expect matches that occur 

in earlier stages and use more detailed subgroups to be of better quality than matches that occur in later 

stages. Rather than comparing the distribution of each characteristic by round, we report how similar the 

49

With small cells

CE Interview CE Diary

As expected, we observe that the matching quality in the first round is the highest. About 72 percent (75

percent) of the matched cases in round one with (without) small cells occur between individuals with a 

distance lower than 1. As the number of rounds increase, however, we observe a decline in the quality of 

the matches. After match 21, most matches are low-quality, with over 60 percent being matched to donors 

with a distance of 4 or above.

Interestingly, there are no notable differences in the distribution of observations based on match quality 

across rounds when comparing the Interview with the Diary match. Furthermore, we do not see 

significant differences in matching quality between the procedure that allow for small cells and the one 

that restricts the use of microcells. That being said, because there is a higher proportion of individuals 

matched in the first round when allowing for micro-cells, there is a slight advantage in terms of matching 

distance or matching quality compared to the case with restrictions. However, the small gains in matching 

0% 20% 40% 60% 80% 100%

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quality do not justify the drawbacks associated with potential model overfitting. Because of this, we 

implement the statistical matching procedure that restricts cell sizes.

3.3.1.2.3 Quality assessment

The usual practice in assessing quality is to check if the marginal distributions have been transferred from 

the donor to the recipient file. The verification involves comparing the imputed distribution and actual 

distribution (e.g., distribution of imputed hours spent on household production by employed women in the 

recipient file with the distribution of imputed hours of the same group in the donor file). In addition, 

comparisons of subgroup means, medians, and standard deviations are made across the two files for the 

key population subgroups that may figure in the potential uses of the imputed data. We present below the 

results from following this analysis path for the double matching procedure (i.e., treating the ATUS 

weekday and weekend diaries as separate donor files) without using small cells.

We begin with the results for the eight subgroups formed by three key variables shaping the time spent on 

household production: gender, presence of children, and employment status (employed or not). A feature 

of the constrained statistical matching procedure is that it would generally replicate the distribution of the 

variable of interest with near perfection in the recipient file. This feature is illustrated in Figure 3-3, which 

shows the distribution of daily hours of household production by subgroup in the donor and recipient 

files. 
51

Figure 3-3 Box Plots: Distribution of daily hours of household production on weekdays and weekends, 

imputed vs. observed, statistical matching (double matching method without small cells)

A. Weekdays

B. Weekends

Notes: 1. (n)w=(Not) Employed; m=Men, f=Women; (n)c = (No) Children. 2. “Hours of household production”

refers to our preferred definition of the time spent on household production, the sum of time spent on cooking, other 

housework and shopping, direct childcare, supervisory childcare, and adult care.
52

We next examine how close the imputed and observed mean and median values are for key population 

subgroups differentiated by gender. Ideally, we would like the imputations to replicate the same 

relationship we observed in the ATUS between a specific characteristic and number of hours (e.g., the 

average hours for women tends to increase with the number of children in the household). This target of 

imputation would be met automatically if the imputed and “true” (ATUS) values coincided. Our degree of 

success in meeting the target will be higher the closer the imputed values are to the ATUS values of 

means as well as medians.

We analyzed statistics for 33 subgroups, each differentiated by gender.23 The subgroups were created 

based on the following characteristics, taken one at a time: race or ethnicity (four groups), presence of 

children five years or younger (two groups), employment status and presence of children in the household 

(four groups), number of children in the home (four groups), number of adults in the household (three 

groups), age (five groups), family type (two groups), education (four groups), and family income bracket 

(five groups). The imputed and observed values were separately compared for the weekday and weekend 

matches for the Interview and Diary samples. Our online data appendix reports detailed estimates. Here, 

we provide a qualitative overview.

Figure 3-4 Actual (ATUS) values compared to imputed values, by sex and race/ethnicity (daily hours)

23 Women tend to devote more time to household production than men within each of the 33 subgroups. Our 

imputation replicates the gender disparity in hours by design because gender is a primary strata variable in our 

matching procedure.

0.00 1.00 2.00 3.00 4.00 5.00

TU-Men

TU-Women

Interview-Men

Interview-Women

Diary-Men

Diary-Women

Weekend--Mean values

Non-Hispanic White Black Hispanic Other

0.00 1.00 2.00 3.00 4.00 5.00

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TU-Women

Interview-Men

Interview-Women

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Weekend--Median values

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Weekday--Median values

Non-Hispanic White Black Hispanic Other
53

To illustrate our assessment procedure, consider the results for racial/ethnic categories shown in Figure 

3-4. Let us first look at the mean values of daily hours spent on weekend days by men in the ATUS 

(labeled “TU” in the figure). We observe that there is only a negligible difference (less than 15 minutes 

per day)24 between non-Hispanic Whites, those in the “other” group (a residual racial category), and 

Hispanics. On the other hand, the biggest gap is between non-Hispanic Whites and Blacks: the former 

spends about 50 minutes more per day, on average, than the latter. Our imputation for the Interview and 

Diary samples has preserved the same pattern in the weekend averages for men, i.e., small gaps between 

groups other than Blacks and a substantial gap between non-Hispanic Whites and Blacks. A quick 

inspection of the weekend averages for women in the ATUS would show a similar pattern as those for 

men, except that the largest gap is between women in the “other” group and Black women (a difference of 

88 minutes per day). Our imputations reproduce the same pattern in the Interview and Diary samples. 

Weekday averages show no sizeable differences among subgroups in the ATUS, and our imputations 

reflect the same. 

To complete the picture regarding racial differences, we should look at the value for the average person in 

a subgroup, i.e., the median, in addition to the average value for the subgroup. Estimates for men from the 

ATUS show that the median weekend values of groups other than Blacks display trivial differences from 

each other; the largest gap is between Blacks and those in the “other” group—the typical person in the 

latter group spends about 41 minutes more per day on household production than the former. Our 

imputations in the Interview and Diary sample indicate an identical pattern. Turning to weekday median 

values for women in the ATUS, we find the same results as for men, though the gap in median value 

between Blacks and “others” is much higher at 80 minutes. As for weekday median values, we find no 

notable variations among male racial subgroups, and our imputations reflect this.

In contrast, among female racial subgroups in the ATUS, we find that the weekly median values for nonHispanic Whites and Hispanics are practically the same and considerably higher than the median values 

for the other two subgroups, which, in turn, are almost the same. Our imputations do preserve these 

differences. However, we do end up overstating the median value for the “other” and, therefore, 

understating the gap in the median between them, on the one hand, and non-Hispanic Whites and 

Hispanics, on the other.25

24 If we set aside 30 hours in the 48-hour period of the weekend for sleep, personal care, and leisure there are 18 

hours left. A difference of 0.5 hours would thus make up less than 3 percent of the maximum time available for 

household production for those who are not at their jobs on weekends.

25 It may be noted that the “other” group constitutes only about 7 percent of the female population under study here. 

The gap between non-Hispanic White and “other” women was respectively 38, 6, and 10 minutes per day in the 
54

Similar results were also, in general, found for other subgroups. Overall, our results indicate that the 

matches replicated the ordering of group mean values formed by each characteristic. The same general 

observations also hold for the median values. We summarize the picture in Table 3-6. Panels A and B 

report our weekend and weekday matches assessment, respectively. If the matching could replicate the 

same qualitative relationship between the subgroups and time (as we saw above for the racial/ethnic 

characteristic), we have flagged the characteristic as “Yes.”

On the other hand, if our assessment raised any concern regarding the replication, we indicated “No.”

There are 72 cases in each panel. We marked “No” for the weekend matches in seven instances. One 

more problematic case was found for the weekday matches. As it turns out, the problematic cases 

pertained to the same characteristics in both matches: the number of adults in the household and 

education. Also, in both instances, we noted issues in matching records for women rather than men.

Table 3-6 Classification of characteristics based on whether the relationship between the characteristic and 

time is reflected in the matching imputation (“Yes”) or not (“No”), by type of statistic, sample, and sex

A. Weekend matches

Characteristic

Mean Median

Interview Diary Interview Diary

Men Women Men Women Men Women Men Women

Race/ethnicity Yes Yes Yes Yes Yes Yes Yes Yes

Presence of young (<=5yrs) 

children in household Yes Yes Yes Yes Yes Yes Yes Yes

Employment status and presence of 

children in the household Yes Yes Yes Yes Yes Yes Yes Yes

Number of children in the 

household Yes Yes Yes Yes Yes Yes Yes Yes

Number of adults in the household Yes No Yes No Yes No Yes No

Age Yes Yes Yes Yes Yes Yes Yes Yes

Family type Yes Yes Yes Yes Yes Yes Yes Yes

Education Yes No Yes Yes Yes No Yes No

Family income bracket Yes Yes Yes Yes Yes Yes Yes Yes

ATUS Interview and Diary samples. In our judgement, the size of the group and gaps are such (i.e., relatively small) 

that no major concern needs to be placed on the imputations with regard to racial groups.
55

B. Weekday matches

Characteristic

Mean Median

Interview Diary Interview Diary

Men Women Men Women Men Women Men Women

Race/ethnicity Yes Yes Yes Yes Yes Yes Yes Yes

Presence of young (<=5yrs) 

children in household Yes Yes Yes Yes Yes Yes Yes Yes

Employment status and presence of 

children in the household Yes Yes Yes Yes Yes Yes Yes Yes

Number of children in the 

household Yes Yes Yes Yes Yes Yes Yes Yes

Number of adults in the household Yes No Yes No Yes No Yes No

Age Yes Yes Yes Yes Yes Yes Yes Yes

Family type Yes Yes Yes Yes Yes Yes Yes Yes

Education Yes No Yes No Yes No Yes No

Family income bracket Yes Yes Yes Yes Yes Yes Yes Yes

Concerning the number of adults in the household, the ATUS shows that there is practically no difference 

between the mean and median values of time for women in one-adult households vs. three-plus–adult 

households. However, the values are higher for women in households with three-plus adults in our 

imputation. For the weekend matches, the gap ranges from 7 to 15 minutes per day, which may be 

considered small. The corresponding range for the weekday matches is somewhat higher: 20 to 31 

minutes per day. The gap arises mainly because our matching overstates the values for women in 

households with more adults. In our view, the discrepancy is not a matter of concern because of the size 

of the gap and the fact that women in households with three-plus adults constitute less than 10 percent of 

the matched records.

We have also flagged the relationship between education and time spent by women on household 

production. Here, the gradient of time with respect to educational attainment is the same in both “true”

and imputed data. The problem is with capturing, in our imputations, the extent of the increase revealed 

by the ATUS data in the time spent by women with a high school degree relative to women without. We 

found the biggest discrepancy in the gap in the average weekday values. According to the ATUS, women 

with a high school degree spend 65 minutes per day more than women without a high school degree, 

while the gap is only 10 and 8 minutes in our imputation for the Interview and Diary samples, 

respectively. Our imputation also failed to capture fully the extent of the decline in the time spent by 

women with some college education relative to women with a high school degree. Here the largest 

discrepancy occurs for the gap in weekday median values: the ATUS median value for the former group 

is about 34 minutes (per day) lower than the latter group, and our imputation for the Interview sample 
56

shows almost no difference in the median values. Therefore, we advise some caution in interpreting the 

subgroup differences among women in the categories of educational attainment mentioned above. 

However, the notably higher amount of time spent by college-graduate women compared to that of 

women without a college degree shown in the ATUS data is also replicated in our imputations.

Figure 3-5 ATUS weekday standard deviation compared to standard deviation in the matched weekday 

samples, by sex, subgroup, and sample

Finally, we also compared the dispersion of hours in the imputed data to the ATUS data for the 33 

subgroups (discussed above), differentiated by sex. We found that, in general, the imputed standard 

deviations were very close to the ATUS standard deviations. The closeness was true for the weekday and

weekend matches conducted for the Interview and Diary samples, respectively. Since the patterns are the 

same for weekday and weekend matches, we have shown only the results for weekday matches in Figure 

3-5 above. In addition, we can observe that the imputed standard deviation is much higher than the actual 

0.00

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ATUS Interview Diary

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Age group,

15 to 24 
57

standard deviation for women in the youngest age group (15 to 24 years). This group, on average, engages 

much less frequently with household production activities compared to older age groups, as we would 

expect. In the remaining 32 subgroups, no such discrepancy is visible, thus fostering considerable 

confidence in the ability of our imputation to capture the within-subgroup dispersion in the time spent on 

household production.

3.3.1.3 Imputation by prediction

3.3.1.3.1 Procedures

Our deployment of prediction methods also followed, just as with statistical matching, the treatment of 

the ATUS weekday and weekend diaries as separate donor files. Weekly data can then be constructed by 

obtaining a weighted sum of these two imputed values (5*weekday+2*weekend). 

As distinct from the matching procedure, the prediction methods require a clear a priori definition of 

dependent variables to be imputed. Since most potential users of the CE data product would like to have 

some level of disaggregation of household production activities for their analysis, we decided not to 

impute a single variable that represents the sum of time spent on various household production activities. 

Instead, we decided to create different components of household production that were described earlier in 

connection with our valuation schema: cooking; other housework (including shopping); adult care; direct 

childcare; supervisory childcare that overlaps with cooking, other housework, or adult care; and 

supervisory childcare that does not overlap with those activities (see Chapter 2). The time spent by the 

individual on each disaggregated category was considered a distinct dependent variable.

Ideally, we would like to estimate separate models for key population subgroups. But, the number of 

subgroups we can effectively use is limited by the available number of observations in the ATUS. This 

restricts the flexibility in specifying models. Nonlinear models, such as the tobit or interval regression 

model used here, are more sensitive to the problem of near-collinearity among regressors in a model. 

Typically, this would manifest as a failure of the estimation algorithm to converge after a reasonable

number of iterations. After several experiments, we estimated a multivariate model with all the dependent 

variables specified in the previous paragraph for individuals in households with children. For individuals 

in households without children, we estimate a multivariate model without the time spent on childcare and 

supervisory childcare.26 The covariates used in the models are the same used in the matching procedure 

described earlier (see Table 3-4 above). However, there are a couple of important differences. We 

26 A very small proportion of individuals in households without children report spending time on the care of 

household children. For the purposes of the estimation, we included such individuals in the group of individuals in 

households with children.
58

included gender interacted with age, and race as explanatory variables in the models. Also, variables of 

co-residence with children and number of children were used only in models of individuals with children.

3.3.1.3.2 Quality of imputation

3.3.1.3.2.1 Regression prediction

Earlier in this chapter, we described the two variants of the imputation-by-prediction method that we 

implemented. We begin with the results of the more straightforward approach of regression prediction 

(RP). We expect RP to perform poorly in replicating the distribution of the hours observed in the ATUS 

data. This is borne out in Figure 3-6, which depicts the distribution in eight key subgroups formed by 

interacting gender, presence of children, and employment status (employed or not) in the ATUS and 

imputed CE data. In contrast, we showed earlier that our statistical-matching procedure carried over the 

distribution almost perfectly for the same subgroups (Figure 3-3). 
59

Figure 3-6 Box Plots: Distribution of daily hours of household production on weekdays and weekends, 

imputed vs. observed, imputation by prediction (regression prediction)

A. Weekdays

B. Weekends

Notes: 1. (n)w=(Not) Employed; m=Men, f=Women; (n)c = (No) Children. 2. “Hours of household production”

refers to our preferred definition of the time spent on household production, the sum time spent on cooking, other 

housework and shopping, direct childcare, supervisory childcare, and adult care.
60

We next turn to inspect the quality of the RP imputation with regard to reproducing the mean and median 

values of household production observed in the ATUS data. It may be recalled that our assessment of the 

results of the statistical matching showed that, in general, matching was able to replicate the qualitative 

relationships between characteristics and time spent on household production. This ability was based on 

the relative closeness of the imputed and observed values of two statistics—mean and median—for 33 

subgroups, each differentiated by gender. Therefore, we focus on the closeness produced by RP vs. 

statistical matching (SM) to assess RP. Conducting the exercise for the Interview and Diary samples had 

similar results. Hence, we report here only the results of the Interview sample. The estimates for the 33 

subgroups of women are shown in Figure 3-7 below.27 

27 As we noted above, the imputation quality for the 15–24 age group is expected to be poor (see the paragraph 

below Figure 3-5 above). The spike in the middle of each panel in the figure below is associated with this age group.
61

Figure 3-7 Percentage deviation of the imputed CE Interview sample statistic from the ATUS statistic: comparison of statistical matching (SM) and 

regression-prediction (RP) for subgroups of women, by statistic and diary day

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62

Both methods perform equally well in replicating the ATUS mean values of time spent on household 

production by women in various subgroups, with one or two exceptions. Deviations are generally bound 

in the interval of ±10 percent. The tallest spike occurs for the youngest group of women (between 15 and 

24 years of age), which, as we noted earlier, constitutes a relatively small percentage of the female 

population under study here. However, the SM method outperforms the RP method by a large margin in 

reproducing the median values. Most of the median values imputed by the SM method fall within a ±10 

percent range of ATUS median values. The percentage deviation spanned by the RP method is much 

wider.
63

Figure 3-8 Percentage deviation of the imputed CE Interview sample statistic from the ATUS statistic: comparison of statistical matching (SM) and 

regression-prediction (RP) for subgroups of men, by statistic and diary day

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SM Interview RP Interview
64

Estimates for the same subgroups of men are shown in Figure 3-8 above. Similar to our findings for the 

female subgroups, the performance of the RP method is considerably inferior to that of the SM method in 

replicating the median values for male subgroups. In addition, in several cases, even the weekday mean 

values for subgroups of men imputed by the RP method exceed the ATUS values by 20 percent or more. 

This finding contrasts with our assessment of women, where both methods produced similar results for 

weekday mean values. Statistical matching performed notably better than RP in creating imputed 

weekday mean values close to the ATUS counterparts for male subgroups and equally well as it did for 

female subgroups.28

Figure 3-9 ATUS weekday standard deviation compared to imputed weekday standard deviation in the CE 

samples using regression-prediction (RP method), by sex, subgroup, and sample

28 The comparatively poor performance of RP with respect to capturing the mean values of male subgroups may be 

due to the larger number of zeroes in the dependent variable for particular subgroups. Also, our RP is not based on a 

linear regression, where unconditional mean is equal to the predicted unconditional mean by construction. It is, 

however, hard to pin down the reason for each subgroup because the imputations are done for the sample as a whole.

0.00

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ATUS Interview Diary
65

The explanation for the poor performance of RP in adequately replicating the median values lies in its 

weakness in capturing the within-subgroup distributional properties. By design, the technique aims to 

capture the conditional means of hours of subgroups defined by characteristics included as covariates in 

the regression model. For the subgroups we have chosen to analyze here, we expect the method to 

understate the dispersion in hours considerably. We found evidence for this in the weekday and weekend 

imputations. Therefore, we depict the results for the weekday imputations in Figure 3-9 above. A 

comparison with the corresponding figure for statistical matching (Figure 3-5) clearly demonstrates its 

superiority relative to RP in capturing the extent of the dispersion in the original data.

3.3.1.3.2.2 Multiple imputation

We now turn to the multiple-imputation (MI) variant of the prediction method. As noted earlier, the MI 

method adds a random component to the predicted value for each observation in the recipient file. The 

latter is simulated from the estimated variance-covariance matrix of the residuals of the intervalregression model implemented in the donor file. Hence, we expect the MI method to perform better than 

the RP method in reproducing the distribution of the hours observed in the ATUS within the eight key 

subgroups (formed by interacting gender, presence of children, and employment status). This is evident 

by comparing Figure 3-10 below (MI results) with Figure 3-6 (RP results). However, since MI is 

imputing predicted and SM–actual values, we expect the distribution’s tails to be replicated better by SM. 

Specifically, in some instances, the MI method seems to be creating “too many” large outliers when 

compared to those observed in the actual (ATUS) data, as can be seen by comparing Figure 3-10 below 

(MI results) with Figure 3-3 (SM results). This weakness of the MI method is starkly evident for the 

group “w_f_c” (employed women with children), shown as the fourth group from the top in the figures. 

The ATUS weekday data shows no outliers (i.e., no observations that fall beyond the right whisker), 

while the MI method creates quite a few. In contrast, the SM method creates none and is true to the 

original data.
66

Figure 3-10 Box Plots: Distribution of daily hours of household production on weekdays and weekends, 

imputed vs. observed, imputation by prediction (MI method)

A. Weekdays

B. Weekends

Notes: 1. (n)w=(Not) Employed; m=Men, f=Women; (n)c = (No) Children. 2. “Hours of household production”

refers to our preferred definition of the time spent on household production, the sum of time spent on cooking, other 

housework and shopping, direct childcare, supervisory childcare, and adult care. 3. We created five sets of imputed 

values for each observation using the MI method. Because the quality assessment based on all five showed 

practically no differences, we report results based on the first set of imputed values.
67

Since both RP and MI imputations are based on regression models, we do not expect a notable difference 

in their efficacy imputing the mean values of the hours spent on household production for the 33 

subgroups (each differentiated by gender) from the ATUS data. While discussing the RP results, we 

pointed out that the SM and RP methods performed similarly well in imputing the mean values for 

women on the weekdays and weekends. This observation also applies to the MI imputation (see Figure 

3-11 below).29 (Because the results from all five sets of imputed values are qualitatively the same, we 

report only those based on one set of values here.) Similarly, the MI method imputed the mean weekend 

values for men with reasonable accuracy (Figure 3-12 below). However, as evident in the same figure, 

MI’s performance was weaker for mean weekday values than was SM’s. Thus, for the imputation of 

weekday mean values for men, both variants of imputation by prediction were inferior to SM. As for the 

imputation of median values, MI displayed the same performance as RP. The imputed values turned out 

to be higher than the ATUS values by a considerable margin in many subgroups of women in the 

weekend and weekday imputations. In addition, we found the upward bias to be substantially worse for 

male subgroups on weekdays and weekends. The upshot is that SM outperforms RP and MI for the 

imputation of median values.

29 Because of the qualitative similarity between the assessments of imputations in the Interview and Diary samples, 

we report here only the results from the Interview sample. The full set of results are available in an online data 

appendix.
68

Figure 3-11 Percentage deviation of the imputed CE Interview sample statistic from the ATUS statistic: comparison of statistical matching (SM) and 

multiple imputation (MI) for subgroups of women, by statistic and diary day

-20%

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SM Interview MI Interview

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69

Figure 3-12 Percentage deviation of the imputed CE Interview sample statistic from the ATUS statistic: comparison of statistical matching (SM) and 

multiple imputation (MI) for subgroups of men, by statistic and diary day

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70

Figure 3-13 ATUS weekday standard deviation compared to imputed weekday standard deviation in the CE 

samples using multiple imputation (MI method), by sex, subgroup and sample

We expect the MI method to outperform RP in reproducing the dispersion in hours observed in the ATUS 

samples as a whole and within the 33 subgroups we have been discussing.30 Indeed, there is no reason to 

think that the MI method would perform differently than SM in capturing the amount of dispersion. As 

shown in Figure 3-13 above, unlike the RP method (see Figure 3-9), the standard deviations imputed by 

the MI method closely track the standard deviations we find in the ATUS and are very much comparable 

to those generated by SM (see Figure 3-5).

30 The spike in the middle of each panel corresponds to the 15–24 age group. We outlined earlier why we expect the 

quality of imputation to be poor for this group (see the paragraph below Figure 3-5 above).

0.00

0.50

1.00

1.50

2.00

2.50

3.00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33

Subgroup

Men

ATUS Interview Diary

0.00

1.00

2.00

3.00

4.00

5.00

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33

Subgroup

Women

ATUS Interview Diary
71

3.4 Aggregation to consumer units

Each of the three methods of imputation assigned time spent on household production for every 

individual aged 15 years or older in the CE samples. As mentioned, we treat the weekday and weekend 

diaries as separate donor files. Hence, we impute weekday and weekend values for each recipient in the 

CE samples. To arrive at the weekly values, we form a weighted sum of the weekday and weekend values 

with weights of five and two, respectively. This procedure is predicated on the assumption that other 

weekdays and weekend days (i.e., other than the diary day) are identical to the diary weekday and diary 

weekend days.

31

However, we also need estimates of household production (in terms of value and hours) at the consumerunit or household level. As noted before, since the unit of observation in the ATUS is one person per 

sample household, we cannot directly ascertain household-level time use. Indeed, the lack of householdlevel time-use data in the ATUS necessitates the current imputation strategy at the individual level. 

Consequently, an aggregation step is required in the recipient, i.e., CE files. We add up the imputed times 

assigned to individuals within the consumer unit to arrive at the household-level aggregates. Our 

aggregation method assumes that the time spent by an individual on household production is uncorrelated 

to those of other family members after controlling for the characteristics of the individual (e.g., age), other 

family members (e.g., employment status of the spouse), and their household (e.g., owner vs. renter).

Conceptually, we should not expect the assumption to be problematic in imputing the overall shape of the 

distribution and its location parameters. This is because we expect the correlation to be low once we 

account for the relevant covariates. Since the ATUS collects time-use information from only one 

respondent in each household, we cannot explicitly control for the time spent by other family members on 

household production in the imputation procedure. However, alternative approaches are possible, and 

they may lead to different outcomes.32

31 An alternative (more cumbersome) procedure would be to consider time diaries from each day of the week as 

separate donor files. Given the difference in the number of observations between the ATUS and CE samples, such a 

procedure would entail, in the case of SM and MI methods, using the same observation from the ATUS for a 

relatively (relative, that is, to our method) large number of records in the CE samples. Alternatively, when using 

simple conditional mean imputation, one can consider the imputed data as representing a typical week. With 

multiple imputation data, the assumption is similar to SM.

32 For example, an imputation procedure may be deployed in the ATUS to “fill in” the time spent on household 

production by members other than the respondent in each household. Once the imputations are completed, 

household-level aggregates of time-use activities could be formed in the ATUS. The household-level ATUS can 

then be used to impute time-use categories for consumer units in the CE samples. We prefer our method for various 

reasons: 1) because it uses only the actual information reported by respondents in the ATUS, 2) this alternative 

approach still requires the conditional independence assumption among household members’ activities, and 3) even 

with data for all household members, matching households is more difficult than matching individuals.


74

the prediction approach. For the case of statistical matching, specifically, we recommend imposing 

restrictions on the cell size to avoid problems of overfitting, despite the slight disadvantage the procedure 

has in matching quality compared to allowing the use of micro-cells.

The three imputation methods presented here have their advantages and limitations, which will depend on 

the type of analysis one aims to implement. RP is the simplest method to implement. As we have seen, it 

performs as well as the more complex MI and SM methods in replicating the mean values for most 

population subgroups we have considered here. However, both prediction methods fail to adequately 

capture the weekday mean values for male subgroups as SM. Both methods also overstate the median 

values of several population subgroups by a substantial margin, especially male subgroups. The clear 

superiority of SM over prediction methods in approximating subgroup median values was evident from 

our assessment exercise. Of the two prediction methods, RP performs notably worse than MI in predicting 

subgroup medians. Thus, matching outperforms prediction when replicating location measures for 

population subgroups.

Turning to the efficacy of capturing the dispersion within subgroups, we find no discernible difference 

between SM and MI in their imputed values of standard deviations—both are very close to their observed 

counterparts. Given its logic, the RP method was not expected to perform well in this regard, and our 

assessment confirmed our expectations. The difference between SM and MI in transferring the 

distribution seems to be in the ability to mimic the percentile points throughout the distribution. We 

mentioned earlier the tendency of MI to overstate median values, reflecting its weakness relative to SM in 

transferring the percentile points. The drawback is also reflected in the relatively sizeable number of high 

values that appear as outliers in the box plots imputed by MI. SM is the best approach for transferring 

distributions of time use to the CE, even for smaller subgroups.

The clear disadvantage of the SM and RP methods compared to MI is in the area of statistical inference 

using imputed values. Before the emergence of MI, most researchers treated the imputed variables in 

survey data on the same footing as the variables in the survey itself. However, it is now widely known 

that inference based on such an approach can be misleading because it does not account for the potential 

errors in imputation. The extent of these discrepancies depends on the details of the statistical model and 

imputed variables. What seems certain is that the extent cannot be known a priori. There are, thus,

tradeoffs in the choice between SM and MI for multivariate analysis using the imputed data, which needs 

to be explored further.

Our analysis of the imputed data suggests that there are some subgroups for which imputed data is 

unreliable. Individuals in the youngest age group (15 to 24 years) are one such group, as are individuals in 

households with three or more children, three or more adults, low levels of education, or low levels of 
75

income. Most of these groups are relatively small in size. The general guideline is, therefore, that one 

should avoid analyzing small population subgroups or subgroups in which a particular type of household 

production is quite low.
76

4 Imputing Care Received 

Nonmarket-time inputs to providing care for household members may come from people in the household 

and outside. A novel aspect of our definition of household production is the inclusion of nonmarket care 

services received by household members from people outside their household (see Chapter 2). 

Admittedly, the receipt of such services expands the household’s consumption possibilities; hence, their 

imputed value should be included in a broad measure of consumption. The empirical challenge is the lack 

of direct information. Government statistical agencies in the United States do not collect regular and 

comprehensive data on the receipt of nonmarket care. 

In contrast, the American Time Use Survey (ATUS) presently collects detailed data on time spent by 

individuals rendering care to nonhousehold children and nonhousehold adults. In the latter case, 

information is collected on strictly caring activities as well as a broader set of activities encompassing 

cooking and other housework (including shopping).33 No information is collected in the ATUS regarding 

the characteristics of the care recipients or their households.

This chapter describes the sources of data and methods used to impute the value of nonmarket care 

services received by individual consumer units in the CE Diary and Interview samples. In light of the data 

limitation noted above and discussed further below, it is evident that our imputations are not as 

comprehensive here, relative to the imputations performed using the ATUS of the time spent on 

household production (described in the previous chapter). However, as we shall see later, the contribution 

made by its members far outweighs that made by persons outside the household in terms of expanding the 

consumption possibilities of the household. The upshot is that the coarse imputations for the latter may 

not substantially impact the overall household production estimates. Yet, we include the imputations for 

the sake of completeness and advocate for better data collection on this front.

4.1 Care received by children

4.1.1 Background

The people outside the household providing nonmarket care services may be family members that live 

outside the household (e.g., older sibling, step or biological parent), members of the extended family (e.g., 

grandmothers or aunts), or nonrelatives (e.g., friends or neighbors). Scholars have examined the role of 

such care in investigating the constraints parents of young children, especially mothers, face in entering 

33 The broader set of activities consists of housework, cooking, and shopping assistance; house and lawn 

maintenance, and repair assistance; animal and pet care assistance; vehicle and appliance maintenance/repair 

assistance; financial management assistance; and household management and paperwork assistance.
77

the labor market and the fertility decisions of people of childbearing age.34 However, this type of care is 

mainly categorized under “informal” care, which includes paid care services provided by individuals 

(e.g., babysitters). For example, a recent study on the relationship between the availability of informal 

childcare and parental employment during the COVID-19 pandemic in the United States laments the 

failure of existing research to distinguish between different forms of informal care. Based on data from an 

online survey, the authors find that, while highly-educated and higher-income families relied more on 

paid informal care arrangements during the pandemic, those with lower education and income found 

relatively more support from older children and extended family in maintaining their hours on the job 

(Zang et al. 2022).

Publicly provided or subsidized formal childcare can go a long way toward meeting the childcare needs of 

many working parents. Yet, the growth of jobs with non-standard work schedules and irregular or 

contingent (“on call”) hours, as well as the growing share of those with multiple jobs over the last three 

decades, have made the task of combining care and employment more challenging for many working 

parents, especially single mothers (Brady 2016; Collins and Carson 1998). There is also the issue that the 

considerations involved in seeking replacement childcare are, for most people, substantially different than 

those entering into, say, substituting restaurant-made for homemade meals. A critique of the National 

Childcare Strategy introduced by the Labour government in the UK in 1997 focused on its exclusive 

reliance on a regulated market approach and purely financial incentives. The authors’ empirical study 

documented the widespread prevalence of a mix of market and nonmarket childcare—“the childcare 

jigsaw”—among working parents in an urban area (Tyneside) and a strong preference for nonmarket 

childcare, especially from grandparents (Wheelock and Jones 2002; see also Casper 1996 for a report on 

the conditions of employed mothers in the US around the same time).

The Survey of Income and Program Participation (SIPP) has collected information on childcare 

arrangements from 1985 onwards. In 2019, reference parents

35 of children under 15 were asked questions 

regarding childcare arrangements during the fall of the reference period. Questions pertained to the 

“typical” week of the fall season. Only employed or school-going parents were asked. They probed 

whether the child or children were cared for by parents, siblings older than 14, grandparents, other 

relatives, nonrelatives, family daycare provider, childcare center, before/after school programs for 

34 A recent example of empirically comparing the impact of formal and informal childcare arrangements in Southern 

Europe (where provision of childcare by grandmothers is relatively high) on women’s employment and fertility is 

Aassve, Meroni, and Pronzato (2012). On the other hand, Cardia and Ng (2003) provide a general equilibrium 

treatment of the issue of grandparents’ care versus formal care.

35 Mother is the reference parent in households with both parents; father can give responses only if the mother is not 

available for an interview. In single-parent families, the parent who lives with the child is the reference parent. If 

neither parent is in the household, the guardian serves as the reference parent.
78

children 3 to 14 years) or self (i.e., left alone). Estimates for 2019 indicate that 45.9 percent of children 

under 14 were under the care of older siblings, grandparents, relatives, or nonrelatives during a typical fall 

week. Since parents can use multiple sources of non-parental care, the previous proportion may also 

include children that receive care from centers. On the other hand, only 15.4 percent of children under 15 

and 26.2 percent of children under five were exclusively cared for by centers (family daycare plus 

childcare centers) in 2019.

The SIPP collected more detailed information regarding childcare arrangements before 2012. While only 

employed mothers were queried between 1985 and 1995, nonemployed mothers were added in 1996. 

Noteworthy from our perspective is that information on weekly hours spent by preschoolers in each care 

arrangement was also collected. For example, estimates36 for 2011 indicate that, on average, preschoolers 

with employed mothers spent 23 hours, and those with nonemployed mothers spent 15 hours under the 

care of their grandparents (Laughlin 2013). Further, given our goal of estimating the care received from 

nonhousehold members, we need to identify whether the caregivers (grandparents, relatives, or 

nonrelatives) live with the child; otherwise, we would end up double-counting the care received. Earlier 

rounds of SIPP did permit researchers to distinguish between nonrelative caregivers that lived with the 

child from those that did not. However, the earlier and present rounds of SIPP do not make that 

distinction for relatives (older siblings, grandmothers, aunts, etc.). Finally, we also need to know if the 

care received was directly in return for payment. If that were the case, we would not consider it 

“nonmarket.” Unfortunately, the earlier and current rounds of SIPP do not allow us to implement this 

distinction because they do not collect childcare expenditures disaggregated by type of care provider. 

Perhaps the best available data for alternative care arrangements for children under six are currently 

collected in the Early Childhood Program Participation Survey (ECPP) module of the National Household 

Education Survey (NHES). The ECPP has been conducted seven times: 1991, 1995, 2001, 2005, 2012, 

2016, and 2019. The latest round coincides with the year we seek estimates (see Cui and Natzke 2021 for 

a description of the main findings from the latest round and Herbst 2023 for a broader view). The survey 

collects the incidences of different care arrangements and durations of care. As noted above, the SIPP 

does not contain any information on the duration of care. Unlike the SIPP, however, the ECCP covers 

only preschoolers. The classification of care arrangements is similar to the SIPP: parents, relatives, 

nonrelatives, and centers not located in a private home (including a daycare center, preschool, and prekindergarten). As distinct from the SIPP, the survey records the payments for each care arrangement. 

Estimates indicate that among the children who received care from nonrelatives who did not live in their 

36 Average hours are based on those who reported using that specific arrangement.
79

household, only a tiny minority (11 percent) received it through nonmarket means (i.e., without payment) 

in 2019. 

In contrast, the majority of children (77 percent) who received care from relatives obtained it in the form 

of nonmarket services. However, the survey does not enable us to distinguish between caregiving 

relatives who live and do not live with the child. Therefore, we cannot easily classify the care as rendered 

by a nonhousehold member. But, we can ascertain whether the relative cared for the child in the child’s 

home, another home, or both places. We may classify those in the latter two categories as nonhousehold 

members. However, we may be understating the number of nonhousehold caregivers because at least 

some of those who provide care in the child’s home will not be members of the child’s household (e.g., 

the child’s grandmother who comes from her home to care for the child while the child’s parents are at 

work). By this reckoning, 71 percent of children who receive care from “nonhousehold relatives” do so 

without payment. Thus, the ECPP offers much better information than SIPP for estimating nonmarket 

care received by preschool children from people outside the household.

Before we compare ECPP with the CE samples and outline the imputation procedure, it is helpful to 

summarize the broad picture from the 2019 round of ECPP. Boys are slightly overrepresented compared 

with girls among the recipients of nonhousehold, nonmarket care (54 vs. 46 percent). The recipients are 

also disproportionately non-Hispanic White rather than non-White (58 vs. 42 percent). Children from 

families where both parents are employed constitute nearly 70 percent of care recipients, while only 45 

percent of all children live in such households.

4.1.2 Comparison of ECPP and CE samples

The NHES—based on a nationally representative sample covering 50 states and the District of 

Columbia—was administered between January and August 2019 by the US Census Bureau. As noted 

above, the ECPP is a module of the NHES. Questions in the ECPP were posed to the parent or guardian 

in the sample households regarding one child under the age of six and not enrolled in kindergarten. There 

were 7,076 children in the ECPP module, representing the national population of roughly 21 million 

preschoolers.

We were attracted to the ECPP because it contains data on the care received by the child from relatives 

and nonrelatives.37 For each type of caregiver, information is collected only for regular care arrangements, 

i.e., scheduled at least once per week. Duration of care given is obtained from responses to questions 

37 The ECPP questionnaire is available at https://nces.ed.gov/nhes/pdf/early/2019_ecpp.pdf
80

regarding “about how many hours each week does this child receive care from this relative 

(nonrelative)?”38 We can assess whether the arrangement is of a nonmarket type by examining the 

response to the “Yes or No” question: “Is there any charge or fee for the care this child receives from the 

relative (nonrelative), paid either by you or some other person or agency?” As noted in the previous 

section, membership in the child’s household can be directly gauged from the data for nonrelative 

caregivers. This is because there is a “Yes or No” question (i.e., “Does this person who cares for this child 

live in your household?”)” regarding nonrelatives. For caregivers that are relatives, we assume that if the 

respondent answers either “Both” or “Other home” to the question, “Is this care provided in your home or 

another home?” the relative is not a member of the child’s household.

Table 4-1 Demographic composition of the population under six years of age by survey, 2019

ECPP CE - Interview CE - Diary

Number 

(‘000) Percent

Number 

(‘000) Percent

Number 

(‘000) Percent

Gender Female 10,109 48.1 10,489 48.9 9,619 47.8

Male 10,900 51.9 10,969 51.1 10,519 52.2

Race or 

Ethnicity

White 10,399 49.5 10,999 51.3 10,575 52.5

Black 3,323 15.8 3,084 14.4 2,602 12.9

Hispanic 5,348 25.5 4,964 23.1 4,893 24.3

Other 1,939 9.2 2,410 11.2 2,067 10.3

Age

Less than one year 4,577 21.8 2,178 10.2 1,819 9.0

Two years 8,344 39.7 7,894 36.8 7,182 35.7

3-6 years 8,088 38.5 11,386 53.1 11,137 55.3

Number of 

children 

under six 

years

One 11,173 53.2 11,178 52.1 10,708 53.2

Two 8,005 38.1 8,118 37.8 7,272 36.1

Three or more 1,831 8.7 2,161 10.1 2,158 10.7

Number of 

children, 6 

to 18 years

None 11,945 56.9 11,451 53.4 10,851 53.9

One 5,294 25.2 5,869 27.4 5,410 26.9

Two 2,743 13.1 2,599 12.1 2,028 10.1

Three or more 1,027 4.9 1,539 7.2 1,849 9.2

Family type

Couple 16,533 78.7 15,550 72.5 15,160 75.3

Single mother 3,289 15.7 3,205 14.9 2,865 14.2

Single father 691 3.3 875 4.1 773 3.8

Other 496 2.4 1,829 8.5 1,339 6.6

38 It should be noted that the concept of the duration of caregiving here is different from that employed in the ATUS 

with respect to care given to nonhousehold children. The latter records the time spent by the respondent in such 

activity during the previous 24 hours and can include care that is not regularly scheduled, e.g., when the respondent 

babysits their friends’ three-year-old as a random act of kindness so that the friends can go out for a movie. It is 

arguable that regularly received care is a better measure of care with respect to augmenting a yardstick of 

consumption or income.
81

ECPP CE - Interview CE - Diary

Number 

(‘000) Percent

Number 

(‘000) Percent

Number 

(‘000) Percent

Parental 

employment

Both employed 9,508 45.3 11,153 52.0 10,577 52.5

One employed 9,917 47.2 8,986 41.9 8,861 44.0

Neither employed 1,584 7.5 1,319 6.1 700 3.5

Education: 

first parent

Less than HS 2,261 10.8 2,568 12.0 2,495 12.4

High school 4,200 20.0 4,367 20.4 3,883 19.3

Some college 5,462 26.0 5,870 27.4 5,681 28.2

College 9,086 43.2 8,653 40.3 8,079 40.1

Education: 

second 

parent

Less than HS 2,170 12.7 2,083 11.7 2,483 14.5

High school 3,667 21.5 3,902 21.9 4,166 24.3

Some college 4,217 24.8 4,207 23.6 4,310 25.1

College 6,978 41.0 7,599 42.7 6,203 36.1

Household 

income

Less than $30k 3,991 19.0 5,619 26.2 4,621 22.9

$30k-$60k 4,869 23.2 4,598 21.4 5,124 25.4

$60k-$100k 4,960 23.6 4,769 22.2 4,343 21.6

$100k-$150k 3,691 17.6 3,114 14.5 3,533 17.5

$150k or more 3,498 16.6 3,358 15.7 2,516 12.5

Tenure Renter 9,060 43.1 8,848 41.2 8,099 40.2

Owner 11,950 56.9 12,609 58.8 12,039 59.8

Region

Northeast 3,531 16.8 3,563 16.6 3,229 16.0

South 7,445 35.4 8,050 37.5 7,771 38.6

Midwest 4,717 22.5 4,931 23.0 4,199 20..9

West 5,317 25.3 4,914 22.9 4,938 24.5

All 21,009 100 21,458 100 20,137 100

We provide a comparison of the ECPP sample and CE samples in Table 4-1. A notably higher proportion 

of children less than a year old is seen in the ECPP compared to the CE samples (22 percent vs. 

approximately 10 percent). Children in all samples are almost identical regarding their gender, race, or 

ethnicity. There is no substantial difference in terms of household demographic characteristics such as the 

number of young children (under six years), the number of older children (6 to 18 years), and the type of 

family (couple, single-female, single-male, or other).39 There appears to be a lower proportion of families 

39 Definition of the family type had to be different across surveys though the difference is unlikely to matter 

significantly for the estimates presented here. In the CE samples, we can directly identify only the spouse and child 

(or grandchild) of the reference person. Hence, our family typology is based on the relationship to the reference 

person. However, in the ECPP, because the unit of observation is the child, fathers and mothers are identified 

directly if they live with the child. Father or mother could be biological, adoptive, step, foster, or same-sex partner 

of parent. An example may illustrate the difference clearly. In the ECPP, if the child’s mother lives in the household, 

we will know who that is, irrespective of the mother’s relationship to other people in the household. In the CE, for 

the same family, if the mother of the child is the niece of the reference person, we will not be able to know for sure 

the relationship between the child and mother.

83

about 13 percent of the total number of consumer units. As we show below, the ECPP indicates that 

preschoolers receiving regular care from kith and kin who are not members of the household constitute 

under 10 percent of all children under six. Further, our examination of the ECPP microdata showed 

notable differences among population subgroups of preschoolers in the recipiency rate. These 

characteristics of the problem suggest that a prediction method with “noise” and a straightforward 

technique of replicating conditional averages would be sufficient to arrive at reasonably accurate 

estimates of incidence and duration of care.

We began the imputation procedure by estimating, using the ECPP data, the likelihood of a child under 

six receiving nonmarket care from outside the household, conditional on the relevant characteristics—

derived from categorical variables already outlined in Table 4-1—of the child, their parents, and their 

household. We list the dummy variables and the estimation results in Table 4-2. Survey weights in the 

ECPP were used to generate the estimates in the table. 
84

Table 4-2 Estimates of the binary logit model (dependent variable is the recipiency of care by a child under 

six years)

Variable Estimate

Standard 

error

Intercept -2.468** 0.211

Race/Ethnicity (reference group is nonHispanic white)

Black -0.244 0.264

Hispanic -0.356** 0.138

Other -0.448** 0.197

Age (reference group is children under 

three years)

3 - 6 years -0.286** 0.107

Number of kids under six in HH 

(reference group is one kid)

Two or more -0.215* 0.120

Presence of older children (6 to 18 

years) in HH (reference group is no 

older child)

Older child -0.361** 0.141

Family type (reference group is a 

couple- household)

Not a couple-household -0.438** 0.186

Parental employment (reference group 

is households with at least one 

nonemployed parent)

Both parents or co-resident single 

parent employed 1.397** 0.191

Parental education (reference group is 

those without a college degree)

Parent 1 college graduate 0.195 0.131

Parent 2 college graduate -0.222* 0.117

Household income (reference group is 

under $60,000)

$60,000-$100,000 -0.177 0.169

$100,000 or more -0.573** 0.130

Home tenure (reference group is 

owner)

Renter -0.165* 0.096

Region (reference group is Midwest)

North 0.223 0.186

South 0.007 0.109

West 0.112 0.138

Note: N = 7,061. The unit of observation is a child under six years of age. ** - significant at the 5 percent level, * -

significant at the 10 percent level. Unmarked estimates are not statistically significant. Standard errors were 

calculated using the 80 replicate weights that account for the complex survey design and are available in the data 

file.

Source: Authors’ estimates from the public-use data file of the Early Childhood Program Participation.
85

Our focus here is not on building a causal explanation based on the model. Still, we highlight a few 

interesting aspects of the estimates. Some may be worth further scrutiny within a richer analytical and 

empirical approach, especially considering factors ignored here, such as proximity to kith and kin, 

involvement in social networks, and preferences.41 We find that the likelihood of receiving care is lower

for Hispanic children and “other” race children than for non-Hispanic White children, holding other 

covariates constant.42 The youngest is also favored compared to those in the 3–6 age group. We speculate 

that the probability of receiving care diminishes with the number of preschoolers in the family. A parent 

or guardian is more likely to be nonemployed to stay home if there are two or more young children. The 

need for care from kith and kin outside the household is reduced when there is a stay-at-home parent. The 

presence of older children (6–18 years of age) seems to reduce the need for external help because the 

older siblings may engage in caregiving. It is curious that not having two parents in the home reduces the 

likelihood of receiving care, as we would imagine that being a single parent increases financial and 

caregiving responsibilities. Perhaps, the estimates indicate that, after controlling for other covariates, such 

a demand may not be met with a corresponding supply. 

The variable reflecting parental employment has an expected positive sign, implying that, if both parents 

are employed in a couple-household or if the single parent in a single-parent household is employed, they 

are more likely to receive help compared to families where there is at least one nonemployed (possibly 

stay-at-home) parent. As proxied by the college graduate dummy, parental educational attainment 

demonstrates opposite signs for the first (positive) and second (negative) parent. Since we associate

greater employment probability with a college degree, we might expect that the child with a college 

graduate as a second parent may be more likely to receive care. However, the greater preference and 

ability to afford center-based care may offset this. Finally, income and wealth effects seem to operate in 

the same direction. Children from higher-income families seem less likely than those from lower-income 

families to receive care, and renters who generally are less wealthy than homeowners are less likely to get 

care.

The coefficients from the logit model estimated using the ECPP data were used to predict the probability 

of receiving care in the CE samples of children under six. We then employed a Bernoulli distribution to 

assign recipiency to individual children to introduce a degree of randomness. To examine the imputation 

quality, we provide estimated recipiency in the ECPP and CE samples in Table 4-3.

41 The ECPP contains detailed information regarding the reasons behind the choice of care providers. However, we 

cannot use that information for the imputation because of the absence of matching information in the CE survey.

42 This may be due to the higher average number of adults in Hispanic households. See the discussion in Section 

5.3.2 below.
86

Table 4-3 Observed and imputed recipiency of care (percent) among children under six years

Characteristic Subgroup Incidence (percent) Deviation from ECPP (%)

ECPP CE - Interview CE - Diary CE - Interview CE - Diary

Gender Female 8.8 8.9 8.4 1% -5%

Male 9.7 8.9 10.4 -8% 7%

Race or 

Ethnicity

White 10.9 11.1 10.3 2% -6%

Black 8.6 7.1 10.4 -17% 21%

Hispanic 7.4 7.1 7.6 -4% 3%

Other 7.0 4.9 8.3 -31% 18%

Age Under 3 yrs. 10.3 11.2 10.0 9% -2%

3 to 6 yrs. 7.7 6.9 9.0 -11% 17%

Number of 

children under 

6 years of age

One 10.1 10.0 10.2 -1% 1%

Two or more 8.3 7.7 8.5 -7% 3%

Number of 

children under 

6 to 18 years 

of age

None 10.8 10.2 10.6 -6% -2%

One or more 7.2 7.5 8.1 4% 12%

Family type Not a couple HH 9.6 9.4 9.4 -3% -2%

Couple HH 8.0 7.7 9.6 -4% 20%

Parental 

employment

At least one nonemployed parent 3.8 3.4 3.9 -11% 2%

No nonemployed parent 13.1 12.0 12.5 -9% -5%

First parent: 

college grad?

Not a college grad 8.2 8.6 9.0 4% 9%

College grad 10.6 9.4 10.1 -12% -5%

Second parent: 

college grad?

Not a college grad 9.2 8.7 9.7 -5% 6%

College grad 9.4 9.3 8.8 -1% -7%

Income

Less than $60k 8.7 8.4 8.8 -3% 2%

$60k-$100k 10.7 10.8 12.0 1% 12%

$100k or more 9.0 8.3 8.6 -8% -4%

Tenure

Renter 8.2 8.3 9.1 2% 11%

Owner 10.1 9.3 9.7 -8% -4%

Region

Northeast 10.9 12.0 12.0 11% 10%

South 8.5 8.5 9.3 0% 9%

Midwest 10.2 8.3 9.9 -18% -3%

West 8.4 7.9 7.7 -7% -9%

All 9.3 8.9 9.4 -4% 2%

Note: ECPP refers to the Early Childhood Program Participation Survey, 2019.

Overall, the imputed incidence in the Interview (8.9 percent) and Diary (9.4 percent) samples are very 

close to the observed incidence in the ECPP (9.3 percent). The imputation performed reasonably well for 

the population subgroups considered for our analysis and shown in the table. A simple rule-of-thumb 
87

examines whether the imputed and observed recipiencies diverge from each other by more than 15 

percent. Only three such instances can be found among the 29 subgroups in the table for the Interview and 

Diary samples. The first two are for the racial groups “Black” and “Other.” For the Interview sample, the 

region “Midwest” was the third culprit, while for the Diary sample, it was the “Couple” household. The 

estimates for these groups should, therefore, be used with caution. For example, for the racial groups, the 

non-White groups may be combined into a single category for comparison with the White group since the 

ECPP data indicates that the unconditional averages for the non-White groups are pretty similar. In sum, 

the imputations faithfully reproduce the differences in incidence apparent in the donor, i.e., ECPP data.

The next step in our imputation procedure was assigning hours of care received by children. We first 

estimated a Poisson model of hours using the ECPP data on children who received care.43 The covariates 

we used here are the same as those in the binary logit model described above. We show the results of the 

estimation in Table 4-4 below. As with the logit model, survey weights were used in our estimation.

43 We experimented with a truncated regression model. However, as happens frequently when the number of zeros 

in the dependent variable is “large” (i.e., the degree of participation is low), the model failed to converge. On the 

other hand, the Tobit model we estimated faced no convergence problems but the quality of imputation was inferior 

to the Poisson model we eventually chose.
88

Table 4-4 Estimates of the Poisson model (dependent variable is the weekly hours of care received by a child 

under six years)

Variable Estimate

Standard 

error

Intercept -2.468*** 0.139

Race/Ethnicity (reference group is nonHispanic white)

Black -0.044 0.127

Hispanic -0.107 0.083

Other 0.272*** 0.089

Age (reference group is children under 

three years)

3 - 6 years -0.007 0.056

Number of kids under 6 in HH 

(reference group is one kid)

Two or more -0.063 0.063

Presence of older children (6 to 18 

years) in HH (reference group is no 

older child)

Older child -0.031 0.068

Family type (reference group is couple 

household)

Not a couple household -0.099 0.097

Parental employment (reference group 

is households with at least one 

nonemployed parent)

Both parents or co-resident parent 

employed 0.392*** 0.124

Parental education (reference group is 

those without a college degree)

Parent 1 college graduate -0.136** 0.066

Parent 2 college graduate -0.056 0.066

Household income (reference group is 

under $60,000)

$60,000-$100,000 0.066 0.086

$100,000 or more 0.059 0.096

Home tenure (reference group is 

owner)

Renter 0.028 0.072

Region (reference group is Midwest)

North 0.046 0.090

South 0.176** 0.074

West 0.057 0.081

Note: N = 743. The unit of observation is a child under six years of age. ** - significant at the 5 percent level, *** -

significant at the 1 percent level. Unmarked estimates are not statistically significant. Standard errors were 
89

calculated using the 80 replicate weights that account for the complex survey design and are available in the data 

file.

Source: Authors’ estimates from the public-use data file of the Early Childhood Program Participation, 2019.

There are some interesting points of difference between the parameter estimates of the hours (Poisson) 

model and the incidence (logit) model. For example, while the incidence model clearly favors White 

children, the hours model indicates that, among recipients, children belonging to “other” racial groups 

receive more hours of care from their kith and kin than White children.44 The next point of contrast is the 

role of income and wealth. As noted above, higher amounts of both seem to translate into lower 

recipiency rates. However, among care recipients, children with higher family income and wealth receive 

a longer duration of care than children who have low family income or who live in rented homes 

(presumably, less wealthy on average). It is also notable that having a college graduate as the first parent 

appears to reduce the duration of the care received by the child in contrast to our earlier finding that it 

makes the probability of receiving care higher—both compared to children with less educated parents. 

44 It may be recalled that, in the Poisson model, the size of the implied effect can be found by exponentiating the 

parameter estimates. For example, the parameter estimate for parental employment status is 0.39 and exp(0.39) =

1.48. The average hours of care received by a kid in a family where both parents are employed or a family with a 

single, employed parent is 1.48 times higher (i.e., approximately 50 percent higher) than the kids in the reference 

group, i.e., kids with at least one parent who is not employed, other covariates held constant.
90

Table 4-5 Observed and imputed weekly hours of care received by children under six years

Characteristic Subgroup Weekly hours Deviation from ECPP (%)

ECPP

CE -

Interview CE - Diary

CE -

Interview CE - Diary

Gender Female 19.8 19.8 19.7 -2% 0%

Male 19.0 19.8 20.0 1% 5%

Race or 

Ethnicity

White 19.5 19.9 19.6 -1% 0%

Black 18.1 19.5 19.0 1% 5%

Hispanic 18.1 17.6 18.6 -2% 3%

Other 24.2 25.3 25.8 7% 7%

Age Under 3 yrs. 19.4 19.9 19.5 0% 0%

3 to 6 yrs. 19.2 19.5 20.2 0% 5%

Number of 

children under 

6 years of age

One 20.3 20.5 20.6 -1% 1%

Two or more 18.1 18.7 18.9 1% 5%

Number of 

children under 

6 to 18 years 

of age

None 19.6 19.8 20.1 0% 3%

One or more 18.9 19.7 19.4 0% 3%

Family type Not a couple HH 19.6 20.0 19.7 -1% 0%

Couple HH 18.1 19.1 20.3 3% 12%

Parental 

employment

At least one nonemployed 

parent 14.2 13.9 14.6 -4% 3%

No nonemployed parent 20.4 20.7 20.8 -1% 2%

First parent: 

college grad?

Not a college grad 19.8 20.5 20.5 1% 4%

College grad 18.9 18.8 19.0 -3% 0%

Second parent: 

college grad?

Not a college grad 19.5 19.9 19.9 0% 2%

College grad 19.1 19.6 19.6 -1% 3%

Income

Less than $60k 18.1 19.1 19.2 1% 6%

$60k-$100k 20.6 20.7 21.1 0% 2%

$100k or more 19.8 20.0 19.8 -1% 0%

Tenure

Renter 18.4 19.9 20.5 4% 11%

Owner 19.9 19.7 19.4 -3% -2%

Region

Northeast 17.9 19.6 17.9 10% 0%

South 21.0 21.4 21.3 2% 1%

Midwest 18.6 18.0 19.3 -4% 4%

West 19.1 19.0 19.7 0% 3%

All 19.4 19.8 19.9 0% 3%

We used the parameter estimates from the ECPP to predict the hours of care received by children in the 

CE samples to whom were imputed recipiency. The imputation quality can be evaluated by comparing the 

subgroup averages in the ECPP with their imputed counterparts in the CE Interview and Diary samples, 
91

respectively (Table 4-5 above). For the ECPP sample, recipients receive, on average, 19.3 hours of care 

per week. By construction, the imputed average in both the CE samples is practically identical to the 

ECPP average. None of the 29 subgroups in the table show a discrepancy larger than the 15 percent ruleof-thumb threshold we mentioned earlier. The deviations range from –4 percent (–5 percent for the Diary 

sample) to +10 percent. We conclude that the imputations are of a high quality in ascertaining the 

subgroup averages that may be of most interest to the research and policy community.

4.2 Care received by older adults

4.2.1 Background

Care received by older adults from people who do not live with them and without payment became of 

concern to economic policymakers in the mid-1980s. The interest was mainly because most such care was 

provided by women in their late–middle age helping their parents or parents-in-law with daily living 

tasks. Much more attention has been paid to the implications of this type of gendered-care arrangement in 

industrialized Western Europe than in the United States. Population aging is a more pressing problem in 

the former group of countries. Women should maintain or increase their labor force participation to 

reduce the growing dependency ratio. However, caregiving responsibilities toward elders can reduce 

women’s labor force participation, especially when state support and assistance for older adults are being 

rolled back (see Moussa 2019 for a recent review).

Early studies on this topic in the United States examined the relationship between women’s labor supply 

and parental care responsibilities using data on women who had such obligations, which prevented them 

from reliably estimating causal effects, as pointed out by Ettner (1995). In light of the then-existing policy 

concerns regarding the impact of societal aging, the SIPP collected information (including time spent) on 

caregiving for people who did not live in the respondent’s household from all respondents in the mid1980s using a topical module. The module was administered in Wave 7 of 1996, 2001, 2004, and 2008 

SIPP panels and the data has been used for estimating the causal effect of parental caregiving on labor 

supply and formal employment on caregiving (e.g., He and McHenry 2016; Maestas, Messel, and 

Truskinovsky 2020). Unfortunately, the topical module on the care of nonhousehold members appears to 

have been discontinued in the SIPP’s recent (i.e., post-2014) rounds.

A key disadvantage of the SIPP data is the lack of detailed information regarding the care receivers who 

live outside the home. The Health and Retirement Survey launched a module on functional limitations in 

the early 2000s, providing a breakthrough. Information was collected from the standpoint of the care 

receiver (respondent). Also, detailed information (e.g., sex, employment status, etc.) was collected about 
92

the caregivers related to the respondent. The joint information on care receivers and caregivers gave rise 

to extensive literature, including gender roles and the division of eldercare responsibilities among siblings 

(e.g., Grigoryeva 2017).

4.2.2 Data

4.2.2.1 Health and Retirement Survey (HRS) as the donor file

To our knowledge, the best source of information for our measurement purposes is the Health and 

Retirement Survey (HRS). The HRS uses a nationally representative panel sample of roughly 20,000 

individuals over the age of 50. It is sponsored by the National Institute on Aging and the Social Security 

Administration and conducted by the University of Michigan. For an overview of the HRS, see Fisher and 

Ryan (2018). We identified the amount of time nonhousehold individuals spend caring for persons aged 

50 years and over and for their spouses by using the 2016 HRS.

Some limitations of the HRS data for our exercise should be noted at the outset. First, it does not cover all 

adults but only older adults; therefore, we estimate a subset of adult care. The most sizeable omission here 

may be the nonmarket care received by nonelderly disabled persons. Second, the amount of time is 

reported by the care receiver rather than the caregiver and using the recall and not the diary method. Both 

aspects of data collection in the HRS may give rise to some (unknown) bias. Third, the questions on care 

received are not posed to all respondents but only to those who report some health difficulty or disability, 

thus rendering the care received by older people not afflicted by such problems impossible to measure.

Finally, the scope of questions on the duration of care is limited to assistance with certain activities, as 

described below. As a result, even though a higher proportion of HRS respondents in 2016 report 

receiving help with cleaning or maintenance of their residence than with activities related to health 

difficulty (19 vs. 12 percent), no information is available on the hours of help with housework or whether 

the help was paid or unpaid.

The HRS module on time spent by helpers for the respondents begins by asking them a set of screening 

questions to identify if they face difficulties with everyday activities due to health or physical problems

(e.g., walking several blocks, sitting for about two hours, etc.). Those that answered “yes” were asked 

about difficulty with Activities of Daily Living (ADLs) due to physical, mental, emotional, or memory 

problems.

45 All respondents were asked about difficulty with Instrumental Activities of Daily Living 

(IADLs).46 Those who reported difficulty with ADLs or IADLs were asked whether they received any 

45 ADLs include dressing; walking across a room; bathing or showering; eating, including cutting up food; getting in 

and out of bed; and using the toilet.

46 IADLs include preparing a hot meal, shopping for groceries, making phone calls, taking medications, and 

managing money.
93

help with the activities. A helper may be someone or an organization the respondent reported as assisting.

Questions were asked about the helper’s identity and the respondent’s relationship to each helper (up to 7 

helpers for ADL and 15 for IADL). From the information available in the HRS regarding the household 

members of the respondent, we can identify whether the helper in question resides with the person who 

received the help.47 We can also ascertain whether the assistance is obtained from nonmarket sources (by 

excluding helpers related to the respondent as an “employee of a facility” or a “paid helper”).48

Further questions were asked about the frequency and duration of help received from each nonmarket 

helper outside the respondent’s household. We estimate the time spent for each respondent from the 

responses to the following questions. The survey offers the respondent three options to report the 

frequency of help received from each helper: the number of days during the last month, the number of 

days per week during the last month, or every day during the last month (respectively, QG070, QG071, 

and QG072). Once the respondent answers, they are asked about the hours per day they received help on 

the days they received help (QG073). We used the frequency of help and hours per day to calculate each 

helper’s weekly hours of help to the respondent.49 The total hours of nonmarket, nonhousehold help 

received by the respondent from all helpers were then obtained by summing the hours each helper gave to 

the respondent.

The HRS data show that the recipients of help are predominantly women (77 percent). A little less than

half (44 percent) of the recipients are 81 years or older, and a majority (70 percent) are not currently 

married. The recipients also belong disproportionately to the bottom quintile of the income distribution 

(56 percent). The recipients are also disproportionately non-White and less educated compared with 

nonrecipients.

4.2.2.2 Comparing HRS and CE

A comparison of HRS and CE samples is not straightforward due to fundamental differences in design. 

The HRS follows individuals over 50 and their spouses, while the CE comprises a cross-section of 

consumer units. We first constructed a potential “HRS pool” from the Interview and Diary samples to 

47 If the helper is the spouse/partner of the respondent, we can identify whether they live together by utilizing the 

variable “2018 living arrangement status” (QLIVARR) in the so-called tracker file (Trk2018tr_r). If the helper is 

someone else, we can identify whether they live in the respondent’s household by ascertaining whether the person is 

included among the household members of the respondent and a resident of the household. For this purpose, we use 

the file that contains the records of household members (H18pr_mc) and the variable “residency status updated” 

(QX056_MC).

48 We use the variable in the helper-level file representing the relationship of the respondent to the helper (QG069) 

to perform this identification.

49 For those who reported the frequency of help from a given helper as the number of days during the previous 

month, we multiplied the days and hours per day and divided the product by four to obtain weekly hours.
94

compare with the HRS. A person in a consumer unit was assigned to the HRS pool if they were a 

reference person over the age of 50, the spouse of a reference person over 50, or any other person in the 

unit over 50. It should be noted that, for couples in the last category, we cannot account for their 

relationship (e.g., characterize them as a married couple or identify the spouse’s age) in our modeling.

Table 4-6 Demographic composition of the population over 50 years of age by survey

Characteristic Subgroup HRS 2016 CE - Interview CE - Diary

Gender Men 47 46 47

Women 53 54 53

Age

51 to 60yrs 40 39 39

61 to 70yrs 32 32 32

71 to 80yrs 18 19 20

81yrs or more 10 10 9

Household 

income

quintile

Lowest 20 20 20

Second 20 21 20

Third 20 20 19

Fourth 20 18 19

Highest 20 22 22

Family type

Couple-only 46 42 43

Single-person 25 22 22

A couple and other 

adults 18 21 20

Other households with 

2+ adults 11 15 14

Race Nonwhite 26 29 30

White 74 71 70

Education

Less than high school 14 12 12

High school 29 27 26

Some college 26 28 28

College 31 34 34

Tenure Renter 30 21 23

Owner 70 79 77

Division

New England 5 5 6

Mid-Atlantic 12 15 14

East North Central 16 13 14

West North Central

8 5 5

South Atlantic 22 21 21

East South Central 6 7 6

West South Central

9 11 11

Mountain 8 7 7

Pacific 14 16 15

Total 100 100 100
95

We compare the HRS 2016 sample with the CE samples (for 2019) in Table 4-6 in terms of their 

(weighted) composition. The samples are pretty much the same concerning gender, age, and distribution 

of household income across quintiles. However, some difference is found in the distribution of people 

across types of families. Compared to the CE samples, more people live in couple-only and single-person 

households as recorded in the HRS. We speculate that part of the problem here may be the difficulty of 

defining the relationships between the people in the HRS pool in the CE samples that were discussed 

before. Also, the HRS appears to have a lower proportion of White individuals and college graduates than 

the CE samples. The largest discrepancy is in home ownership. In the HRS, we observe 30 percent of 

people as renters while their shares are 21 and 23 percent, respectively, in the Interview and Diary 

samples. The difference may be driven by the fact the homeownership variable in the HRS is derived 

from the response to the question, “Do you [and your] [husband/wife/partner] own your home, rent it, or 

what?” Therefore, a father over 50 years of age who lives in a home owned by his son would not be 

recorded as a homeowner in the HRS, while that person would be coded as living in an owned home in 

the CE samples.50 In sum, however, we think the differences in the sample composition are not sizeable 

enough to affect the imputation quality.

4.2.3 Models and results

According to the HRS data, the share of older adults who receive nonmarket care from people outside 

their household is only 3 percent. We reckon that 38 percent of individuals in both CE samples are in the 

HRS pool; hence, the number of people who would be affected by the imputation is a little over 1 percent 

of the population. This is similar to the situation we discussed for the preschoolers earlier in this chapter. 

These aspects of the problem—the small number of people affected, the pronounced subgroup differences 

in incidence and hours, and a limited role for intragroup variation—suggest that the imputation strategy 

we adopted for preschoolers would also be appropriate for older adults. We now outline the results of 

implementing that strategy.

50 The HRS question allows responses “lives rent-free with relative/employer/friend” and “other” to the question 

regarding home ownership.
96

Table 4-7 Estimates of the binary logit model (dependent variable is the recipiency of care by persons over 50

years of age)

Variable Estimate

Standard 

error

Intercept -5.15** 0.200

Gender (reference group: female)

 Male -0.84** 0.103

Age (reference group: 51 to 60 

years)

 61 - 70 years 0.27** 0.130

 71 - 80 years 1.04** 0.146

 81 or more years 2.39** 0.134

Household type (reference group: 

composite householda)

 One-person household 0.48** 0.090

 Couple-only household -0.31** 0.156

Race or ethnicity (reference group: 

non-Hispanic white)

 Nonwhite 0.65** 0.111

Education (reference group: College 

graduates)

 Less than high school 1.09** 0.156

 High school 0.47** 0.168

 Some college 0.40** 0.161

Home tenure (reference group: 

Owner)

 Renter 0.67** 0.098

Division (reference group: South 

Atlantic)

 New England -0.30 0.252

 Mid Atlantic 0.08 0.164

 East North Central 0.24* 0.137

 West North Central 0.14 0.141

 East South Central 0.26 0.227

 West South Central 0.44** 0.181

 Mountain -0.08 0.198

 Pacific 0.12 0.151

Note: N = 18,927. The unit of observation is a person over 51 years of age. ** - significant at the 5 percent level, * -

significant at the 10 percent level. Unmarked estimates are not statistically significant. Standard errors were 

calculated using the “stratum” and “cluster” variables that account for the complex survey design and are available 

in the data file. a – Composite household: households with three or more persons (two of which form a couple) and 

households with two or more people unrelated via marriage

Source: Authors’ estimates from the public-use data file of the Health and Retirement Survey, 2016.
97

The recipiency model estimated from the HRS data is shown in Table 4-7. We used survey weights 

provided in the data file in our estimation. The independent variables used in the estimation are all 

dummy variables derived from the categorical variables shown above in Table 4-6. Our estimates indicate 

men are less likely to receive care than women. Further, the probability of receiving care increases with 

age, compared to people in the 51 to 60 age group. Men may be less likely than women to receive care, 

because women are overrepresented in the older age groups due to their longer life expectancy. Compared 

to older adults in composite households with at least two adults, people who live by themselves are more 

likely to receive care, while couple-only families are less likely to receive care. This is intuitive because a 

couple may be able to meet their care needs independently and are likely to be younger. College graduates 

appear to be less likely than people of lower educational attainment to receive care; this may reflect a 

cohort effect as the younger groups are more likely to be college graduates than older groups. Lower 

wealth also seems to be associated with a higher probability of receiving care, as indicated by the positive 

sign of the coefficient associated with renters. It is worth noting that all the variables, except some 

pertaining to geographical location, display high statistical significance. However, as in the case of care 

for preschoolers, we should recall that we are not factoring in proximity to family or friends, preferences, 

and other unobserved factors that can influence the recipiency of nonmarket care from people outside the 

household.

The next step in the imputation procedure involved assigning recipiency to persons over 50 years of age 

in the CE samples. We utilized the coefficients of the binary logit model shown in Table 4-7 above to 

accomplish the goal. A stochastic component was added to the prediction by assigning recipiency based 

on a draw from a Bernoulli distribution, using the predicted score for each individual as a parameter for 

the draw. We assessed the resulting imputation’s quality by comparing the CE samples’ imputed care

incidence with the HRS’ observed incidence (Table 4-8).
98

Table 4-8 Observed and imputed recipiency of care (percent) among persons over 50 years of age

Deviation from the HRS (%)

Characteristic Subgroup HRS

CE -

Diary

CE -

Interview CE - Diary CE - Interview

Gender Men 1.6 1.4 1.6 -9% 5%

Women 4.6 4.2 3.9 -10% -15%

Age

51 to 60 years 1.3 1.4 1.3 5% -1%

61 to 70 years 1.7 1.5 1.5 -8% -10%

71 to 80 years 3.7 3.3 3.7 -13% 0%

81 years or more 15.1 12.8 12.0 -15% -21%

Household 

type

Couple-only 1.4 1.4 1.5 0% 10%

One-person 6.5 5.9 6.0 -9% -9%

Other 3.2 2.8 2.5 -12% -22%

Race Nonwhite 5.0 4.1 4.5 -19% -11%

White 2.6 2.4 2.2 -8% -13%

Education

Less than high school 8.6 7.0 7.2 -18% -16%

High school 3.4 3.5 3.2 4% -6%

Some college 2.5 2.2 2.4 -13% -2%

College 1.3 1.5 1.5 20% 20%

Tenure Renter 5.8 5.5 5.6 -6% -3%

Owner 2.1 2.1 2.1 1% 3%

Division

New England 2.1 2.2 1.9 8% -7%

Mid-Atlantic 3.3 2.7 3.0 -18% -9%

East North Central 3.1 3.1 2.7 -1% -13%

West North Central 3.0 2.8 2.3 -6% -25%

South Atlantic 2.7 2.3 2.7 -17% -2%

East South Central 3.2 2.9 3.3 -9% 2%

West South Central 5.3 4.8 4.4 -9% -17%

Mountain 2.2 2.1 1.7 -5% -26%

Pacific 3.5 2.9 2.9 -17% -18%

All 3.2 2.9 2.9 -10% -10%

Note: HRS refers to the Health and Retirement Survey, 2016.

Overall, we estimated that the recipiency rate in the HRS was 3.2 percent. The imputed recipiency rate in 

the Diary and Interview samples was slightly lower at 2.9 percent. The table also shows the observed and 

imputed recipiency rates for 26 population subgroups. Once again, using the rule-of-thumb of 15 percent 

deviation, we find that six and seven subgroups exceeded the threshold in the Diary and Interview 

samples, respectively. In both samples, three “problem” cases were associated with the Census division of 

the household’s location. Two educational attainment groups also show divergence in both samples: our 

procedure seems to assign higher-than-observed incidence to college graduates and the converse for 
99

people who have not completed high school. In the Diary sample, the remaining problem case was an 

understatement of recipiency among non-Whites. In contrast, the imputation in the Interview sample was 

much closer to the observed value. However, the imputation for “Other” types of households and the 

oldest age group in the Interview sample fell short of their observed counterparts in the HRS.

While the 15 percent divergence threshold we are using is instructive, it is important to note for the 

present application that it may exaggerate divergences because of the relatively low base values. 

Consider, for example, the most considerable deviation of 26 percent for the Mountain division in the 

Interview sample. The observed incidence in that division is only 2.2 percent. In comparison, our 

imputation assigns an incidence of 1.7 percent—a discrepancy of 0.5 percentage points that may not be 

considered as far off the mark. In sum, we judge that imputations are reasonably accurate in reproducing 

the subgroup differences in the recipiency of nonmarket care by older adults from people outside their 

household.

Once the recipients were identified in the manner described above, we proceeded to impute the hours of 

care received by individuals in the CE samples. Just as for preschoolers, we found that a Poisson model 

produced the best predictions. The independent variables used in the regression were dummies reflecting 

the same characteristics used in the incidence model, although the reference group was changed for some. 

Survey weights were used in the estimation. The results are shown in Table 4-9.
100

Table 4-9 Estimates of the Poisson model (dependent variable is the weekly hours of care received by a person 

over 50 years of age)

Variable Estimate 

Standard 

error

Intercept 3.49*** 0.219

Gender (reference group: female)

Male -0.26* 0.190

Age (reference group: 51 to 60 

years)

61 - 70 years -0.53** 0.267

71 - 80 years -1.01*** 0.261

81 or more years -0.44*** 0.165

Household type (reference group: 

single-person household)

HH with 2+persons 0.26 0.210

Couple-only HH 0.08 0.237

Race or ethnicity (reference group: 

non-Hispanic white)

Nonwhite 0.47** 0.181

Education (reference group: Less 

than high school)

High school -0.07 0.214

Some college -0.12 0.240

College -0.24 0.375

Home tenure (reference group: 

Renter)

Owner -0.11 0.169

Division (reference group: South 

Atlantic)

New England -0.66 0.558

Mid Atlantic -0.24 0.239

East North Central -0.17 0.265

West North Central -0.60* 0.362

East South Central -0.28 0.293

West South Central -0.17 0.281

Mountain -0.71*** 0.292

Pacific -0.38 0.272

Note: N = 877. The unit of observation is a person over years of age. ** - significant at the 5 percent level, *** -

significant at the 1 percent level. Unmarked estimates are not statistically significant. Standard errors were 

calculated using the cluster and strata variables that account for the complex survey design and are available in the 

data file.

Source: Authors’ estimates from the public-use file from the Health and Retirement Survey (2016).
101

Estimates for some covariates in the hours model have an opposite sign compared to the same covariate in 

the incidence model. We found a similar reversal of signs for preschoolers too. Perhaps the most striking 

is age. The incidence model showed a positive association between age and the probability of receiving 

external care. However, the hours model shows the opposite: conditional on the other covariates, the 

average hours of care received tends to fall with age. There are also sign reversals for some of the 

geographical divisions. Except for the coefficient on couple-only households, all parameter estimates are 

significant at the 5-percent level.

The final step in our imputation procedure was assigning weekly hours of care to those identified earlier 

as recipients in the CE samples. We used the coefficients of the Poisson model described above to attain 

this goal. As usual, we examined the imputation quality by comparing the observed and imputed average 

hours (Table 4-10).
102

Table 4-10 Observed and imputed weekly hours of care received by adults 50 years and older

Deviation from the 

HRS (%)

Characteristic Subgroup HRS

CE -

Diary

CE -

Interview

CE -

Diary

CE -

Interview

Gender Men 16.3 18.7 18.3 15% 12%

Women 24.0 23.6 24.3 -2% 1%

Age

51 to 60 years 18.9 19.7 20.9 4% 10%

61 to 70 years 13.9 11.6 13.0 -17% -6%

71 to 80 years 21.6 20.3 20.3 -6% -6%

81 years or more 27.0 29.5 29.2 10% 8%

Household 

type

Couple-only 20.6 22.8 23.3 11% 13%

One-person 20.2 19.3 20.2 -4% 0%

Other 26.9 26.5 26.2 -1% -3%

Race Nonwhite 28.4 28.9 28.4 2% 0%

White 18.1 17.7 18.2 -2% 1%

Education

Less than high school 26.2 25.8 28.4 -1% 8%

High school 21.0 23.5 23.6 12% 12%

Some college 19.4 18.3 19.4 -6% 0%

College 18.1 19.9 16.7 10% -8%

Tenure Renter 23.9 23.5 24.1 -1% 1%

Owner 20.3 21.7 21.9 7% 8%

Division

New England 15.1 17.7 14.5 18% -4%

Mid-Atlantic 22.6 23.4 24.0 4% 6%

East North Central 21.3 19.2 21.4 -10% 0%

West North Central 14.5 14.7 14.2 1% -2%

South Atlantic 29.3 27.1 30.1 -8% 3%

East South Central 21.6 17.4 19.3 -20% -10%

West South Central 25.3 27.5 24.1 9% -5%

Mountain 14.1 11.7 13.1 -17% -7%

Pacific 21.8 23.8 19.8 9% -9%

All 22.6 22.5 22.8 -1% 1%

According to our estimates from the HRS, people who reported receiving nonmarket care from persons 

outside their household received, on average, 22.6 hours of care per week. Our imputation, by design, hits 

the mark at 22.5 and 22.8 hours per week in the Diary and Interview samples, respectively. Turning to the 

26 population subgroups depicted in the table, we observe that none of the imputations for the Interview 

sample fall outside the 15-percent rule-of-thumb threshold of deviation. For the Diary sample, there are 

four instances where the deviations crossed the threshold, of which three were found in the Census 

division. The remaining one occurred for the age group 61–70. None of the four cases showed a deviation 
103

exceeding 20 percent. As a result, we can conclude that the imputations succeed in replicating the overall 

and subgroup averages of care received.51

4.3 Summary

We estimate that, of a population of nearly 21 million children under six years of age in 2019, about 1.9 

million (9.3 percent) receive, without any payment, regular care from relatives or nonrelatives who do not 

live with them. For those who receive such care, the average hours of care received amount to 19.4 hours 

per week. In the aggregate, this translates to approximately 38 million hours per week. Or, in terms of 35-

hour-per-week jobs, a little over one million. 

The care received by the children is vital to them. Additionally, through their caring activity, the kith and 

kin (especially grandmothers, who constitute the majority of such caregivers) can also potentially 

facilitate the children’s parents’ pursuit of education or employment, thus enabling families to sustain or 

enhance their standard of living. The benefits of having well-cared-for children and promoting productive 

activity of their parents extend beyond the families of children to the broader community and society. 

Turning to similar care received by older adults, about 3.6 million persons over 50 receive care from 

people (relatives or nonrelatives) outside their household for no payment. The incidence of such 

nonmarket care was only 3.2 percent in the total population of 112 million older adults in 2019. We 

estimate that the average hours of care received by the recipients is about 22 hours per week. Therefore, 

about 80 million weekly hours of caring activity occur in this sphere, which would amount to nearly 2.3 

million full-time (35 hours per week) jobs. The HRS data shows that daughters or daughters-in-law carry 

out most of this caring activity. Their “hidden” labor is part of the social costs of caring for older adults. 

Just as with the care of preschoolers, the beneficiaries of the care of older adults are also not the 

immediate recipients of care alone.

The scope of our present study is to incorporate the contribution made by these types of nonmarket care, 

usually left out of the analysis, into a comprehensive measurement of nonmarket care. From this 

51 Our imputations were not completely effective in terms of replicating the averages by household income quintiles. 

A reason could be the differences in the definition of household income. In the HRS, household income includes 

only the income of the respondent and their spouse while in the CE samples, incomes of all members are included. 

About 70 percent of people over 50 years of age live in couple-only or single-person households (see Table 6). For 

these households, the income definitions in the two surveys coincide. However, that leaves out 30 percent of people 

for whom there may be discrepancies across surveys. In spite of these differences, we found that the average hours 

imputed for the bottom 40 percent and the top 20 percent of households in the income distribution in the two CE 

samples were identical to the observed averages in the HRS. For the third quintile, we understate the average hours 

and for the fourth quintile, we do the opposite. An obvious choice for those interested in looking at income-based 

differences in the hours may be to lump the third and fourth quintiles in the CE samples into a single category.
104

perspective, the estimates of nonhousehold, nonmarket care we provide underestimate the actual volume 

of such activity. We do not account for the care that older children (especially those in the 6 to 13 years 

age group) may receive from people outside their household—their relatives, neighbors, or nonrelatives. 

It is reasonable to think that regular care received by the children in this age group will primarily take 

place for a limited period before the start or end of the school day. On average, the duration of care 

received by older children is thus likely to be shorter than that of younger children. Yet, the population of 

children in the 6 to 13 age group is almost double that of the younger group. Consequently, we may be 

missing a considerable number of hours.52 To get an approximate idea of how much we are missing, we 

can compare the ECPP aggregate hours of care to the total weekly hours of care given by respondents in 

the ATUS to nonhousehold children.

53 The latter is 127 million hours, about 3.3 times higher than its 

ECPP counterpart. While the ECPP collects only information on regular care received by preschoolers,

the ATUS measure encompasses all children under 18 years and does not distinguish between regular and 

irregular care.

Finally, we are not accounting for the nonhousehold, nonmarket care received by adults in the 18 to 50 

age group. Systematic data collection on the nonmarket help received by those with disabilities who live 

in households is probably required to fill this gap. The ATUS collects data on time spent caring for 

nonhousehold adults, but it does not contain any information regarding the age of the adults. Therefore, 

we cannot develop separate estimates for the care received by adults in the 18 to 50 age group, just as we 

cannot estimate the care received by nonhousehold children in the 6 to 13 age group. We also noted that 

our source of data for the care of older adults, HRS, covered only help received in daily activities (such as 

bathing or paying bills) by those with some mental or physical difficulties. The gap in coverage leaves an 

unknown but perhaps nontrivial amount of the care received by older adults from our measurement 

exercise. We may use the ATUS benchmark of aggregate hours of care given to have a rough idea of the 

omission’s magnitude. Estimates from the 2019 round of ATUS indicate that help given by respondents 

in the ATUS to nonhousehold adults amounted to 119 million weekly hours.

54 This amount is about 1.5 

times higher than that recorded in the HRS. 

52 While the ATUS collects information on care given by respondents to nonhousehold children, it does not record 

the age of nonhousehold children.

53 Our assumption is that, in principle, the aggregate hours of care given and received should be identical. We 

calculated the aggregate hours of care given as those reported under the category of “Caring and helping 

nonhousehold children” delineated in the ATUS tables published by the BLS.

54 We estimated this using the category of “Caring and helping nonhousehold adults” in the ATUS tables published 

by the BLS.
105

5 Consumption Expenditures and Household Production

5.1 Introduction

The purpose of this chapter is to present a preliminary analysis of the imputations. Our estimates in this 

chapter were calculated from the synthetic file created by statistically matching the third-quarter CE 

Interview file with the ATUS, augmented by the imputations from the ECPP and HRS for nonmarket help 

received from people outside the consumer unit. The CE sample for the third quarter has a little over 

5,000 consumer units. All the calculations were done using the weight variable “finlwt21.” The matching 

procedure is described in Chapter 3, and the imputations from ECPP and HRS are discussed in Chapter 4.

We first indicate the extent to which the inclusion of household production alters the average value of 

expenditures. In light of the elaboration of the differences between our preferred measure and the “BEA”

measure (Chapter 2), we examine the impact using both measures. Our preliminary analysis is also 

extended to assess subgroup disparities. Here, we present evidence of the much higher contribution made 

by women to household production. Further, we also consider how much the inclusion of household 

production affects the measured differentials among households differentiated by two key demographic 

characteristics—size and composition of the household as well as by the race and ethnicity of the 

reference person. Next, we turn to the distributional profile of household production, i.e., the gap in the 

average value of household production across the deciles of expenditures. To better understand the 

distributional impact of adding the value of household production to expenditures, we also conduct 

decomposition analyses of the Gini coefficient by groups (with and without children) and by source 

(expenditures and categories of household production). Apart from analyzing overall distributional issues, 

synthetic data may also be used for analyzing subsets of spending and household production. We briefly 

illustrate this point by considering food and childcare. The final section offers some concluding 

comments and caveats.

5.2 The overall impact of the inclusion of household production

We begin with an overview of the average monetary values of various categories of household 

production, namely, unpaid (nonmarket) help received from persons not residing in the household and 

home production by household members for their household. The former is further split into help received 

in caring for young children and help received in caring for older adults (Error! Reference source not 
106

found.). We report two sets of monetary values.55 Those labeled “Preferred” are based on our household 

production definition and our valuation method. Alternatively, “BEA” indicates results derived from 

using what we described earlier as the “BEA method.” This approach excludes supervisory care and uses 

a single “generalist” wage, instead of specialist wages as we do, to convert time into dollars. We 

discussed the differences between the two approaches and wage rates earlier in Chapter 2.

The definition of expenditures shown in Error! Reference source not found., line 1 was derived by 

subtracting, from total quarterly expenditures,56 certain expenditures on owner-occupied primary homes 

(mortgage interest, property taxes, maintenance, repairs, insurance, and other expenditures) and 

expenditures classified as miscellaneous, cash contributions, and personal insurance and pensions. We 

divided the resulting quarterly amount by three to get a monthly value and added the monthly rental value 

of owned primary home to obtain the estimates shown in line 1. 

Specifically, we first calculated the sum of expenditures in the current quarter (TOTEXPCQ) and 

previous quarter (TOTEXPPQ) and divided it by three to obtain monthly amounts. In the next step, we 

subtracted certain categories of expenditure from the sum obtained in the second step. The categories are: 

mortgage interest, property taxes, maintenance, repairs, insurance, and other expenditures on owneroccupied primary home; expenses classified as miscellaneous, cash contributions, and personal insurance 

and pensions. Each expenditure item was formed by summing the expenditures in current and previous 

quarter and dividing the sum by three to obtain monthly amounts.57 Finally, we added the rental 

equivalent for owned homes (RENTEQVX), which is available in the file as a monthly amount.

The expenditures we subtracted from total expenditures are also excluded by Garner et al. (2023). 

Similarly, our addition of imputed rent for own-homes also mirror their approach.58 However, our 

measure does not reflect some key modifications made by Garner et al. (2023) to arrive at their measure 

55 We report monthly values of household production rather than weekly values because they match expenditures 

reported on a monthly basis. The monthly values were calculated by multiplying weekly values by 4.345. However, 

this implies the assumption that the weekly values we constructed are typical for all the weeks of the month. (See 

Section 3.4.)

56 Total expenditures consist of expenditures in 14 major categories: food, alcohol, housing, clothing, transportation, 

healthcare, entertainment, personal care, reading, education, tobacco, miscellaneous, cash contributions, and 

personal insurance and pensions.

57 The variables used to create the quarterly expenditures are as follows: Mortgage interest: 

MRTINTPQ+MRTINTCQ; property taxes: PROPTXPQ+PROPTXCQ; maintenance, repairs, insurance, and other 

expenditures on owner-occupied primary home: MRPINSPQ + MRPINSCQ; miscellaneous: MISCPQ + MISCCQ; 

cash contributions: CASHCOPQ + CASHCOCQ; and, personal insurance and pensions: PERINSPQ + PERINSCQ. 

Each expenditure item was divided by three to obtain monthly amounts.

58 We did not make corresponding adjustments in constructing a consumption measure in our validation exercise 

using the PSID data because the categories we subtracted could not be readily identified and an estimate of imputed 

rent was not available.
107

of consumption. First, we do not remove net outlays and financing charges associated with vehicles 

because an estimate of service flow from vehicles is not available to replace them in the public-use CE 

files. Second, we did not net out education and medical expenditures in accordance with the ILO and 

OECD guidelines. Finally, we do not include imputed monetary values for in-kind transfers because the 

requisite imputed values are not available in the public-use CE files. Analyzing the impact of including 

household production on alternative measures of consumption would be interesting but it falls outside the 

scope of our current research.

Table 5-1 Average monthly values of expenditures and household production per consumer unit, 2019 (third 

quarter)

Preferred* BEA**

Line Categories No 

children

With 

children

All No 

children

With 

children

All

1 Expenditures***

 

4,402 

 

5,490 

 

4,726 

 

4,402 

 

5,490 

 

4,726 

2 Household production

 

1,889

 

5,433 

 

2,989 

 

1,836 

 

3,564 

 

2,368 

3

Unpaid help from 

nonhousehold people 48

 

99 

 

64 

4

Help with childcare for 

children under 6

 

79 

 

24 

5

Help with adult care for 

adults over 50

 

48 

 

20 

 

40 

6

Home production of 

household members

 

1,840 

 

5,334 

 

2,925 

 

1,836 

 

3,564 

 

2,368 

7

Food preparation and 

clean-up

 

408 

 

696 

 

494 

 

410 

 

697 

 

496 

8

Housework (except food 

preparation)

 

1,377 

 

1,648 

 

1,458 

 

1,377 

 

1,648 

 

1,458 

9 Childcare

 

2,920 

 

914 

 

1,157 

 

362 

10 Active care

 

1,449 

 

453 

 

1,157 

 

362 

11 Supervisory care

 

1,471 

 

461 

12

Not joint with home 

production

 

1,014 

 

318 

13

Joint with home 

production

 

458 

 

144 

14 Adult care

 

55 

 

70 

 

60 

 

49 

 

63 

 

53 

Addendum:

15

Augmented expenditures

(Line 1 plus Line 2)

 

6,242 

 

10,923 

 

7,715 

 

6,238 

 

9,055 

 

7,095 

16

Augmented expenditures (as a 

percent of Expenditures) 142% 199% 163% 142% 165% 150%
108

Notes: *Includes unpaid help from nonhousehold people and supervisory childcare in the definition of home 

production. A specialist-wage method is used for valuation. **Excludes unpaid help from nonhousehold people and 

supervisory childcare in the definition of home production. A generalist-wage method is used for valuation. *** 

Excludes mortgage interest, property taxes, maintenance, repairs, insurance, and other expenditures on owneroccupied primary home; also excluded are expenses classified as miscellaneous, cash contributions, and personal 

insurance and pensions. Includes estimated monthly rental value of owned primary home.

In assessing the results, it is essential to differentiate between households with and without children 

(defined as persons under 18 years). In our estimates, most households have zero values for time spent on 

childcare. However, a sizeable minority (approximately 30 percent of consumer units) have positive 

childcare time values. On average, time spent on household production differs drastically between the two 

groups of households. The difference is manifested in the results we now discuss.

The effect of adding the value of home production to expenditures is shown in line 15 with the label 

“Augmented expenditures.” Our method increases the average expenditures of the entire sample by 63 

percent, while the increase under the BEA method is smaller at 50 percent.59 In either scenario, the impact 

of home production on measured average consumption expenditures is enormous.

Notably, both methods show that the impact on the average value of units with children is higher than that 

for all units: the increase is virtually 100 percent with our approach and 65 percent with the BEA method. 

On the other hand, for units without children, both methods produce approximately the same percentage 

increase of a little over 40 percent. Since standard expenditures (shown in line 1) are relatively higher for 

households with children than without, another way to interpret the finding is that accounting for home 

production widens the gap further in favor of the former group. Our method does this more so than the 

BEA approach.

The fact that preferred and BEA methods produce the same result for those without children indicates that 

our inclusion of nonmarket care received from outside the household had an insignificant impact. Indeed, 

the estimates shown in line 3 show that the average amount was pretty small relative to the rest of home 

production.60 Further, the use of specialist instead of generalist wage had a negligible effect on the home 

production of household members in consumer units without children, as shown by the practical 

concurrence of average values under both valuation strategies (line 6). The trivial effect is not surprising 

because, under our method, the generalist wage is used for valuing housework (other than cooking) and 

59 The estimated value of “Nonmarket services” (mostly household production) in the BEA’s satellite account 

amounted to 22 percent of personal consumption expenditures in 2020 (Bridgman et al. 2022, Table 2). The lower 

share in NIPA as compared to our measure is not surprising because of the broader definition of expenditures used 

in the national accounts.

60 As noted in Chapter 4, our estimate of nonmarket help from outside the household is downward biased because 

we did not include care received by older children and adults under 50 with disabilities or serious illness.
109

shopping. Another reason is that the specialist wage for cooking is not much different from the generalist 

wage, as described in Chapter 2.

We should now address why our estimate of the average value of home production for households with 

children is notably higher (52 percent) than the BEA counterpart, as shown in line 2. Here, just as for 

households without children, we find virtually the same average values for cooking and other housework 

for the same reason. The driving force behind the discrepancy between the two methods is childcare. Two 

factors determine the difference in the average value of childcare. The first and less important reason is 

that the specialist wage for active childcare (used by us) is somewhat higher than the generalist wage 

(employed in the BEA method). We can observe the impact of this difference in the valuation of active 

childcare (line 10), which shows that our estimate is about 25 percent higher than the BEA counterpart. 

But, the difference between our estimate of childcare and the BEA estimate shown in line 9 is a much 

larger 152 percent! Thus, the second and more important reason for the discrepancy is the inclusion of 

supervisory care in our definition and its omission in the BEA definition of childcare. The value of 

supervisory care is almost as large as active childcare even though it is valued at one-half the rate of 

active childcare because the time allocated to supervisory care compensates for the differential in the 

hourly rate. About 70 percent of the value of supervisory care stems from care that is not overlapping 

with other household production and the remaining from care that overlaps with other household 

production. Defining what counts as childcare trumps how it is valued (at least under the current wage 

structure) in determining the impact of including home production in a broader measure of consumption.61

5.3 Subgroup disparities

5.3.1 Gender

We first focus on the well-known gap between men and women regarding the time spent on household 

production. Our estimates show that including supervisory care widens the gap further (Figure 5-1). 

While women work about 124 hours per month on home production, men work about 40 percent less, or 

75 hours. But, the gap in supervisory childcare is even higher: Men’s engagement in this type of 

caregiving is 52 percent less than women's (16 vs. 33 hours per month). The gap in our context implies 

that, by working longer hours providing household services, women make a greater contribution to 

enhancing the consumption of their households. As a result, we estimate that women account for the 

61 The definition of time spent on supervisory care was given in Section 2.2 (see especially Table 2-1 and text below 

the table). We discuss the valuation of supervisory care in Section 2.3 (please see especially the paragraph above 

Table 2-2 and text below the table).
110

overwhelming share—78 percent—of the total $386 billion added to monthly consumption expenditures

in 2019.

Interestingly, women’s contribution to aggregate hours of home production is the same (about 78 percent 

of the 28.6 billion monthly hours). Thus, the larger contribution of women in augmenting consumption is 

due to their sheer volume of work that far exceeds that of men. Gender differences in the composition of

home production (e.g., women spending more time than men on childcare, valued at a higher rate) or 

differences among the specialist wages we used in valuation played practically no role in shaping this 

outcome.

Figure 5-1 Average monthly hours of home production and supervisory care by persons 15 years and over by 

gender, 2019

Note: N=10,357. Calculations are based on the individual-level synthetic file for the Interview sample (2019: Q3) 

and imputations via statistical matching. All men and women are included in the calculation including those who did 

not engage in any home production.

5.3.2 Size and composition of consumer units

We have already considered how the incorporation of household production exacerbates the measured 

disparity in consumption between households with and without children in favor of the former. The 

presence of children, in general, creates more demand for home production, and such demand would 
111

increase with the number of children, other things remaining constant. Older children can, of course, be 

providers of home production too. On the other hand, the amount of household production also tends to 

rise, all else remaining the same, with the number of adults in the household because of its effect on both 

demand and supply of home production. As is well-known, the supply side is affected positively not just 

because more people can engage in home production; more people also bring in the possibility of 

someone in the household dedicating themselves to taking care of the house and dependents. We now 

present some evidence on the extent of these variations among consumer units differentiated by the 

number of adults and children. We are interested in how much the variations in home production alter the 

pattern of differences in market consumption expenditures between consumer units. It is a separate matter 

to assess these differences normatively by placing them against differences in “needs” with the latter,

codified by an explicit or implicit equivalence scale. The question of equivalence scales will have to be 

confronted for building a measure of poverty that incorporates deprivation in home production. Properly 

treating this issue falls outside the scope of our present research.62

In Figure 5-2, we have shown the differences in the average value of market expenditures and augmented 

expenditures among consumer units differentiated by the number of adults and children. We have adopted 

the group with the largest share in the total number of consumer units, the consumer unit with two adults 

and no children, as the “base group”;

63 the average value of each group is expressed as a ratio to the 

average value of the base group. Hence, by construction, the average value for the two-adult-no-child 

household equals one. The ratios calculated using standard expenditures are represented by the broader 

bars, while the ratios corresponding to augmented expenditures are shown by the thinner bars.

62 One of us has argued strongly against using either the Betson three-parameter scale or square-root scale in the 

context of home production elsewhere and proposed an alternative equivalence scale (Folbre, Murray-Close, and 

Suh 2018). A different approach is taken in Zacharias et al. (2019, Appendix B) as a part of constructing a measure 

of time and consumption poverty. Here, a reference group of households is identified first and thresholds for 

household production, differentiated by the number of young children, older children, nonelderly adults, and elderly 

adults are estimated next for the reference group by means of a nonlinear regression model. The estimated time 

thresholds are then applied to all households for ascertaining their time-poverty status. This approach is similar in 

spirit to the standard procedure of constructing absolute income or consumption-poverty thresholds.

63 The two-adult–no-child households made up 31 percent of all consumer units. A close second position was taken 

by the single-person households (30 percent).
112

Figure 5-2 Relative average expenditures by the number of adults and children in the household, standard vs. 

augmented expenditure definition, 2019

Note: Average expenditures are expressed as a ratio to the average expenditures of households with two adults and 

no children. We estimate that for the latter, the average values for standard and augmented expenditures were, 

respectively, $5,106 and $7,446. The broader bars show the ratios calculated with standard expenditures, and the 

thinner bars indicate the ratios associated with augmented expenditures (standard expenditures plus our preferred 

value of household production. The latter includes supervisory childcare and nonmarket help from outside the 

household, and valuation is performed according to specialist wages.

We find that the ordering of household types changes notably. Relative expenditures are highest for the 

two-adult–two-child households when home production is ignored (1.25). But, with the inclusion of home 

production, the highest average is for the group with the largest number of adults and children (1.91). 

Without home production, the group’s average was very close to that of the base group (1.04). A less 

spectacular increase is found for three-plus–adults–two-child households, whose relative consumption

widens from parity of standard expenditures to 1.66 with augmented expenditures as the yardstick. The 

lowest average value occurs for the same type—single-person household—with standard and augmented 

expenditures. But, their distance from the base group widens further when home production is included 

(0.61 vs. 0.56).

As we would expect, given the number of adults, the gradient with respect to the number of children 

becomes clearly positive with the inclusion of home production. This change is perhaps most visible for 
113

households with three or more adults: average expenditures are generally lower in consumer units with 

more children, but the opposite holds when augmented expenditures are considered. For households with 

two adults and households with a single adult, three or more children are associated with lower average 

expenditures relative to two-children households. However, no such decline is discernible with 

augmented expenditures as the yardstick.64

5.3.3 Race and ethnicity

As we have just seen, the presence of children in the household—as well as the number of adults and 

children—has a decisive impact on the amount of home production. We now examine briefly how these 

factors shape racial differences in augmented consumption expenditures. We use the standard approach of 

assigning groupings for households based on the group of the household reference person. We designate 

Hispanics of all races as “Hispanic.” Non-Hispanic Whites and Blacks are referred to hereafter as 

“White” and “Black.” Non-Hispanic householders of races other than White or Black (primarily Asian) 

are grouped as “Other.” Because of sample size limitation and our use of imputed variables, we may not 

obtain reliable estimates if we were split the Others into Asians and everyone else, though that would 

have been desirable. Our tabulations showed that Whites, Blacks, Hispanics, and Others comprised 66.7

percent, 13.3 percent, 13.6 percent, and 6.4 percent of consumer units in 2019, respectively.

64It might be useful to note that these estimates have implications for the ratio of adult consumption to child 

consumption: household consumption can increase while adult consumption declines.
114

Table 5-2 Racial differences in monthly average values of household production and their potential 

determinants

Race/Ethnicity

Value of 

home 

production

Hours of 

home 

production

Implicit unit 

value of 

home 

production

Share of 

households 

with 

children

Number 

of 

children

Number 

of adults

A. All consumer units

White 2,754 205 13.4 24% 0.44 1.82

Black 2,690 200 13.5 34% 0.69 1.78

Hispanic 4,013 292 13.7 50% 1.04 2.18

Other 3,801 270 14.1 41% 0.71 2.11

B. Consumer units with children

White 5,391 399 13.5 1.87 2.14

Black 4,835 354 13.7 2.04 1.97

Hispanic 5,783 418 13.8 2.09 2.37

Other 5,875 412 14.3 1.71 2.31

Note: Household production includes supervisory childcare and nonmarket help received from outside the 

household, and valuation is performed according to specialist wages. The implicit unit value is obtained by dividing 

the value of household production by the household production hours. Consumer units are assigned to racial groups 

based on the racial group of the household reference person. See the text above the table for the definition of the 

groups.

The average monthly value of home production was the highest for Hispanic consumer units (Table 5-2

Panel A, above). The Hispanic value was 46 percent higher than the average value for White households. 

An almost similar proportionate difference between the two groups exists in the average hours of home 

production. This suggests that the difference in the volume, rather than the implicit unit value of home 

production,65 is the main driver behind the intergroup gap. Proximate factors related to the higher average 

hours for Hispanic consumer units are presented in the last three columns of the table. Half of all Hispanic 

households have children compared to only a quarter of White households. The average number of 

children is about 2.4 times more in Hispanic than White households (1.04 vs. 0.44), and the average 

number of adults is also larger (2.18 vs. 1.82), although the gap here is smaller, as we would expect.

Turning to the other groups, we observe almost no difference between Blacks and Whites regarding the 

average monthly value of household production. However, the share of households with children is higher 

for Blacks than Whites (34 percent vs. 24 percent), and the average number of children is also greater for 

Blacks (0.69 vs. 0.44). These factors should push Blacks’ average hours of home production above that of

65 We refer to the unit value as “implicit” because different components of home production are assigned different 

hourly values in our valuation procedure. Thus, substantial differences in implicit unit values would indicate 

corresponding differences in the composition of home production (e.g., a higher share of childcare in the total hours 

of home production for a group compared to other groups). 
115

Whites. But, the average number of adults is lowest among Black consumer units (1.78), and this may 

help explain, at least partially, why their average hours are virtually the same as Whites’. 

The same factor (i.e., the difference in the number of adults) also probably contributed to the gap between 

Blacks and Others. Though the average number of children is similar for the two groups (0.69 and 0.71), 

the number of adults is notably higher for Others (2.11 vs 1.78). The higher number of adults and the 

greater prevalence of households with children compared with Blacks (41 vs. 34 percent) help us 

understand why, despite having a similar average number of children, the average hours of Others are 

closer to those of Hispanics than Blacks. However, the average value of home production between 

Hispanics and Others shows a smaller gap than average hours because the implicit unit value of 

household production is highest for Others among the groups.

We find that racial differences in the monthly average hours of home production are notably less when we 

consider only consumer units with children (Table 5-2 Panel B, above). Clearly, the principal factor here 

is the drastically narrow range of differentials in the average number of children. Blacks continue to 

register the lowest average amount of time spent on household production, probably because they have 

the lowest average number of adults at 1.97, below the 2-adult benchmark. Perhaps as a consequence, 

White consumer units with 2.14 adults per household are found to have higher average monthly hours 

even though their average number of children is lower than that of Blacks (1.87 vs. 2.04). 

The implications of incorporating home production for the picture of racial disparities in expenditures 

must also be considered. Given the decisive influence of the presence of children, we examined the 

differences among all consumer units and, separately, for consumer units with children. We use White 

households as the base group so that the average expenditures of consumer units in other racial groups are 

expressed as a ratio to the White household average. 
116

Figure 5-3 Relative average expenditures by race/ethnicity of the household, standard vs. augmented 

expenditure definition, 2019

Note: Average expenditures are expressed as a ratio to the average expenditures of households with a White 

household reference person. The average for White households without children was equal to $5,036 and $7,789 for 

standard and augmented expenditures, respectively. White families with children registered mean standard 

expenditures of $6,253 and mean augmented expenditures of $11,644. The broader bars show the ratios calculated 

with standard expenditures, and the thinner bars indicate ratios associated with augmented expenditures (standard 

expenditures plus our preferred value of household production). The latter includes supervisory childcare and 

nonmarket help received from outside the household, and valuation is performed according to specialist wages.

Perhaps the most surprising finding is the ranking reversal between Whites and Hispanics when we 

examine the whole sample. In terms of standard expenditures, the average Hispanic value is about 20 

percent lower than Whites; however, Hispanics are marginally better off than Whites in terms of

augmented expenditures. As we saw before, the greater prevalence of consumer units with children, the 

higher average number of children, and the higher average number of adults among Hispanics were the 

proximate factors behind the rather large wedge in the average value of household production between 

Hispanics and Whites. We now see that the lead is sufficient to close the gap in measured expenditures 

between the two groups. On the other hand, when we consider the subsample of units with children, 

White households maintain their lead over Hispanics even after accounting for household production. 

But, the size of the gap is lower. The reason behind this result is the smaller White–Hispanic gap in 

household production for the subsample compared with the whole sample, which, in turn, can be 
117

attributed to the lower gaps in the average number of adults and especially in the average number of 

children.

5.4 Distribution of household production

Not surprisingly, we find that the value of household production (our preferred measure) is distributed 

much more equally than expenditures across the deciles66 of expenditure (the expenditure definition used 

in calculating the deciles is “standard expenditures,” as defined in Error! Reference source not found.

above). This is illustrated in Figure 5-4, which plots the average spending and average value of household 

production across expenditure deciles. Interestingly, average expenditures and home production values 

are similar for the bottom half of the expenditure distribution. Beyond the 50th percentile, home 

production rises slightly, and expenditures increase much faster up to the 90th percentile. Finally, at the 

top, there is a sharp increase in spending but a slight decline in home production.

Figure 5-4 Average monthly values of expenditures and home production by expenditure decile, 2019

The distributional profiles of the major components of home production, cooking, housework (including 

shopping), and childcare also suggest much lower inequality than the distribution of expenditures (Figure 

66 We are not using an equivalence scale in defining the deciles (see below, Section 5.3.2, for a discussion of 

equivalence scales). The deciles are constructed such that each consists of approximately the same weighted number 

of consumer units.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

1 2 3 4 5 6 7 8 9 10

Monthly values (2019$)

Expenditure decile

Expenditures Home production
118

5-5). Throughout the distribution, housework is the dominant component, followed by childcare. A subset 

of childcare is supervisory care, and its average value across deciles is quite close to that of cooking, 

especially in the top half of the distribution. Childcare and its subset, supervisory care, show the sharpest 

gradient. Using the simple ratio of the top decile’s mean to the bottom decile’s mean, we find the value to 

be 2.1 for home production as a whole but 2.9 for childcare. In comparison, the same ratio is equal to 10.7 

for expenditures.

Figure 5-5 Average monthly values of the major components of home production by expenditure decile, 2019

Note: Childcare includes supervisory care.

Families with children are overrepresented in the higher deciles. The compositional effect contributes to 

the positive correlation between the rank in the expenditure distribution and the value of household 

production because the latter tends to be higher for families with children than those without. We can see 

the composition effect in how, as a proportion of the number of consumer units without children, families 

with children change across the distribution. The value is only 0.17 in the bottom decile, but that value 

rises to almost 0.5 by the middle decile and 0.65 in the top decile. Hence, it is crucial to examine the 

distribution of the value of home production within each group (Figure 5-6). Our estimates show that the 

distribution of home production is actually more equal among families with children across the 

expenditure deciles than among households without children. While there are notable bumps in the 

average value for families with children at the third and seventh deciles, the curve connecting the values 

is notably flatter than its counterpart among households without children. Using our crude metric of the 

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

1 2 3 4 5 6 7 8 9 10

Monthly values (2019$)

Expenditure decile

Cooking Housework Childcare Supervisory care
119

ratio of the top decile’s mean to the bottom decile’s average, we find a value of 2.0 for consumer units 

without children, practically identical to the value of 2.1 we found for the whole sample. However, the 

corresponding value for families with children is only 1.24.

Figure 5-6 Average monthly value of home production by expenditure decile and presence of children (under 

18) in the household, 2019

Note: Expenditure deciles are defined using the cutoffs for the whole sample and not for subgroups.

The large gap in the mean values of household production between those with and without children 

indicates that adding home production to expenditures will result in a wider gap between the two groups 

in the augmented measure compared to the standard estimate of expenditures. Intergroup inequality will 

therefore increase when we incorporate household production. However, because the distribution of 

household production is far more equal than the distribution of spending, the inequality in the augmented 

measure within each group will be lower than in the standard measure. Given the larger values of home 

production and the greater equality in its distribution across expenditure deciles among households with 

children, we expect their decline in inequality to be much sharper compared to households without 

children. We also expect the reduction in within-group inequality to overwhelm the increase in betweengroup inequality and, therefore, the measured inequality to decrease with the introduction of home 

production.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

1 2 3 4 5 6 7 8 9 10

Monthly values (2019$)

Expenditure decile

No children With children


122

Figure 5-7 Factors affecting the contribution of home production to consumption inequality by alternative 

measures, 2019

The numbers shown in Figure 5-7 help interpret the reasons behind the higher contribution of home 

production to inequality with an expanded definition of childcare. As we know, the contribution of a 

component of expenditures to the Gini of total expenditures (standard or augmented) is simply the 

product of its concentration coefficient with respect to total expenditures and the component’s share of 

total expenditures. A comparison of “With children” bars in the two definitions clearly shows that home 

production's larger contribution to within-group inequality of preferred measure is almost entirely due to 

its higher share in augmented expenditures (because of the inclusion of supervisory care). The 

concentration coefficients are practically identical for both measures. However, a comparison of the “All”

bars demonstrates that the concentration coefficient of the preferred measure is also notably higher (0.35 

vs. 0.29). In turn, the higher concentration coefficient reflects the greater inequality in the distribution of 

home production across the expenditure distribution because of the larger gaps between families with and 

without children.

5.5 Applications to food and childcare

We hope the synthetic data will also be useful for investigating issues that require information regarding 

the joint distribution of specific categories of household production and expenditures. As noted earlier 

(see Appendix A of Chapter 3), the favorable conclusion about our imputation strategy that emerged 

from our validation exercise was restricted to the joint distribution of total household production and 

0.000

0.100

0.200

0.300

0.400

0.500

0.600

No children With children All No children With children All

Preferred BEA

Concentraion coefficient Share in expenditures
123

total expenditures. In this section, we examine specific subsets of spending and household production to 

illustrate the synthetic data's potential uses rather than offer a full-fledged study of the particular topics.

We begin with food and assess the existence of an inverse relationship between the share of food 

expenditures in total food expenditures (a measure of the degree to which food requirements are met via 

outsourcing) and household hours of “cooking.”69 Next, we explore the relationship between childcare 

expenditures and hours of childcare. For married-couple families, we are focused on the intrahousehold 

division of childcare responsibilities and investigating whether our synthetic data indicate that dual-earner 

families who spend, on average, more on childcare than “male-breadwinner” families also display a lower 

share in wives’ total family hours of childcare. We also examine if families with children and a single 

employed reference person who purchase childcare services engage in a lower number of hours of 

childcare than their counterparts who do not. Our findings are generally consistent with economic 

intuition. However, they must be considered tentative because we do not account for the data's special 

features, and some of our estimates are generated from a relatively small number of observations, as 

discussed below.

5.5.1 Food

The average household expenditures on food constituted about 16 percent of the monthly average total 

expenses of about $4,700. Spending on food consumed at home accounted for two-thirds of total food 

expenditures, and spending on food away from home made up the remainder.70 Households also perform 

a considerable amount of household production devoted to the preparation of food and drinks as well as 

cleaning up afterward (referred to as “cooking” here).71 On average, cooking accounted for about 17 

percent of monthly total household production hours of 220. As expected, it is also an almost universal 

activity with a participation rate of 94 percent. 

69 Our question is related to, yet different from the mainstream approach of viewing “eating” as a commodity 

produced by food expenditures and “time” inputs, which includes, in addition to the hours of food preparation etc., 

also the time spent on eating and drinking (see, Hamermesh 2007 for an early discussion and Gardes 2019 for a 

recent reformulation). The main difference is that we do not consider the time spent on eating and drinking as 

contributing to a product. Our interest is also not to analyze the reasons why people choose outsourcing of food over 

cooking at home but to look at the effect of the mix (outsourcing and own preparation) of food expenditures on 

hours of cooking. 

70 The food expenditure variables are taken from the public-use FMLI file, FMLI193 (the variable names are shown 

in the parentheses): food expenditure is the sum of expenditures on food at home (FDHOME) and food away from 

home (FDAWAY). 

71 Please see Table 2-1 for the ATUS categories included in our measure of “cooking” time. Basically, it consists of 

hours of food and drink preparation; food and drink presentation; and kitchen and food clean-up. 
124

Food expenditures differ considerably by household size and composition (Figure 5-8, Panel A). Several 

factors are at play, and let us note a few. Variations in need exist among households because more people 

require more food, with children requiring, generally, less food than adults. Then, there are economies of 

scale in expenditures (e.g., the price per quart of soft drink purchased in a 20oz vs. 2-liter bottle), which 

can dampen the variation. On the other hand, the means to afford more and higher-priced food varies 

across households, partly as a function of the number of earners, which also exerts an influence over the 

variation in food expenditures.

Figure 5-8 Average monthly food expenditures and hours of cooking by number of adults and children in the 

household, 2019

1

2

2

1

2
125

Variations in the hours of cooking are also affected by the size and composition of the household (Figure 

5-8, Panel B). Some of the proximate factors at work here are the same as those discussed earlier in 

Section 5.3.2. Admittedly, economies of scale exist in cooking. But, this may be somewhat offset by 

differences in preferences among household members. The presence of children and their number in the 

household alters the quantity and mix of food, which can influence the cooking hours. Both expenditures 

and hours of cooking generally display a positive gradient with respect to the number of adults and 

children. However, an additional factor that can influence the gradient of hours and expenditures is the 

degree to which food requirements are met via outsourcing, i.e., food away from home. An illustration of 

this effect may be seen by comparing the gradients of hours and expenditures, both with respect to the 

number of children and holding the number of adults in the household constant (Figure 5-9).

Figure 5-9 Average monthly food expenditures, average monthly household hours of cooking, and share of 

expenditures on food away from home in total food expenditures by size and composition of households, 2019

Note: “Expenditure ratio” refers to the ratio of each group's average expenditures to the subgroup's average 

expenditures with no children. Similarly, the “Hours ratio” is calculated by dividing the mean household hours by 

the mean hours of the subgroup with no children. “FDAWAY ratio” is the ratio (share) of average expenditures on 

food away from home in total food expenditures.
126

The red line in each panel of the figure represents, for a given number of adults, the average food 

expenditures of households with a certain number of children as a ratio of the average spending of 

households with no children. We can observe along the blue line the same information but for hours of 

cooking. For example, in families with one adult and one child, the average food expenditure is 1.16 

times higher, and the average hours of cooking are 1.49 times higher than that of the corresponding 

averages of one-adult households with no children. The spending and hours gradients appear to coincide 

roughly for the two-adult households. But for families with one adult and families with three or more 

adults, the hours' gradient lies markedly above the expenditure gradient, i.e., average hours increase 

relatively more than average expenditures when the number of children increases by one. A potential 

explanation would lie in the extent to which food supply is outsourced, i.e., a greater proportion of food 

requirements are met via expenditures on food away from home. This proportion is consistently higher for 

families with two adults than households with one adult and households with three or more adults. For 

example, in households with two children, the share of expenditures on food away from home in total 

food expenditures in one-adult, two-adults, and three-or-more-adult families constituted 29, 33, and 28 

percent, respectively.

Table 5-5 OLS estimates of a simple model of household hours of cooking

Variable Parameter 

estimate 

Intercept 17.82** 

(0.90) 

One child 7.60** 

(1.29) 

Two children 15.20** 

(1.35) 

Three or more children 21.57** 

(1.70) 

Two adults 20.46** 

128

years)—constitute a minority, though a substantial one (30 percent of the sample). The actual buyers 

make up 20 percent of the units with children or 6 percent of all consumer units. We should note that the 

quality of imputation for such a small segment is not likely to be as good as for larger subgroups and for 

categories of household production with much larger participation rates (e.g., housework or cooking). To 

avoid small cell sizes, we use all five quarters relevant to 2019.73

With these caveats in mind, we examine the relationships between employment, the hours of childcare 

provided by the reference person or spouse of the reference person, and the purchase of childcare 

services. We focus on employed households—households in which the reference person, spouse, or both 

are employed—because the expenditure data shows that almost all childcare expenditures are incurred by 

employed households. We use the standard family typology: heterosexual married couples, households 

with a single female reference person, and families with a single male reference person. For the first type 

of family, our main interest is in the intrahousehold division of childcare responsibilities (as reflected in 

the total hours of childcare provided by all family members). For single-person families, we assess the 

impact of spending on childcare services on household childcare hours. 

We consider married couples first, restricting our sample to families with exactly two adults (the 

reference person and spouse) to avoid complications arising from the contributions made by other 

relatives or nonrelatives in the household to childcare. Our interest is in examining how average childcare 

expenditures and hours of childcare vary between families with an employed and nonemployed wife. The 

strongly gendered division of childcare, coupled with the fact that almost all of the husbands in our 

sample are employed full-time, justifies the focus (e.g., Foster and Kreisler 2012). We estimate from the 

expenditure data that the proportion of families with an employed wife purchasing childcare services is 

approximately twice as much as those with a nonemployed wife (Table 5-6). Among families who report 

childcare expenditures, this category of spending makes up roughly 10 percent of total consumption 

expenditures for those with an employed wife compared to 5 percent when the wife is nonemployed.

73 That is, we use “191x, 192, 193, 194, and 201” matched files. 
129

Table 5-6 Employed married-couple families with children and two adults: Average monthly childcare 

expenditures and average monthly hours of childcare provided by the family, 2019

Characteristic All Wife not 

employed 

Wife 

employed 

Difference 

Percent reporting childcare expenditures 26 16 29 13 

Average expenditures among spenders 676 407 722 315 

Percent of total expenditures 9.0 5.0 9.7 5 

Household hours of childcare 

Total 295 350 278 -72 

Supervisory 197 228 187 -42 

Direct 99 122 92 -30 

Wives' hours of childcare 

Total 179 237 161 -76 

Supervisory 116 150 105 -44 

Direct 63 87 56 -32 

Husbands’ hours of childcare 

Total 109 104 111 6.3 

Supervisory 76 71 77 5.5 

Direct 34 33 34 0.8 

Notes: (i) The column “Difference” shows the result of subtracting values listed under “Wife employed” from “Wife 

not employed.” (ii) The total household hours are greater (by a small amount) than the sum of the hours of husbands 

and wives because it also includes childcare rendered by family members (probably older children) between 15 and 

17 years. (iii) Components may not add up to their respective totals due to rounding. (iv) There were 4,396 families, 

of which 1086 and 3310 had nonemployed and employed wives, respectively. (v) 1,120 consumer units reported 

spending on childcare, of which 170 and 950 had, respectively, employed and nonemployed wives.

Turning to hours of childcare, we observe that the average monthly hours are 72 hours less for families 

with an employed wife than a nonemployed wife, reflecting, to some extent, the higher spending on 

childcare services among the former. Both supervisory and direct care hours are lower. Divergent 

patterns of change are evident for wives and husbands. Employed wives’ average hours of childcare are 

76 hours less than their nonemployed counterparts. The intrahousehold division of childcare 

responsibilities shifts markedly in wives’ favor with employment—the share of employed wives in total 

household hours of childcare is 58 percent, ten percentage points lower than that of nonemployed wives’ 

average share. However, on average, husbands with an employed wife engage in childcare for about 6 
130

hours more than those with a nonemployed wife. Most of the difference between them is accounted for 

by the higher average hours of supervisory care for husbands in dual-earner families. Our additional 

tabulation (not reported in the table) showed that 96 percent of the difference in supervisory care among 

husbands could be attributed to supervision that does not overlap with other household production (e.g., 

keeping an eye on the children while watching TV). Supervisory care that coincides with other household 

work (e.g., watching children while cooking) played a trivial role in shaping the difference. 

Families with children and a single, employed reference person face different constraints. Most are 

households with one adult, thus eliminating the possibility of sharing childcare or household financial 

responsibilities with another adult. Since the constraints imposed by childcare are generally more severe 

for families with children under 13 (because they admittedly require supervision), we consider this group 

here. The caveats we outlined earlier regarding relatively small cell sizes apply naturally, with even 

greater force for families with a single reference person. Our estimates indicate that childcare 

expenditures are associated with fewer hours of childcare (Table 5-7). The reduction appears true for both 

single females and single males, amounting to roughly 40 minutes per day for females and 1 hour and 10 

minutes for men. However, the cell sizes are pretty low for those who purchase childcare services (144 

and 29, respectively). Single-female families purchase childcare services at about the same rate as dualearner families (29 percent), while the rate among single-male families is lower at 21 percent (these 

estimates are not shown in the table).

Table 5-7 Families with children under 13 years and a single, employed reference person: average monthly 

childcare expenditures and average monthly hours of childcare provided by the family, 2019

Family hours of childcare Average expenditures 

of families purchasing 

childcare 

Gender of the 

reference 

person 

Does not buy 

childcare 

services 

Buys 

childcare 

services 

Amount Percent of 

total 

expenditures 

Female 193 174 401 10 

Male 156 120 498 8 

Notes: (i) Family hours of childcare refer to the sum of direct and supervisory care hours provided by all family 

members 15 years or older. (ii) 615 families are included in the sample for the estimates shown in the table, of which 

475 and 140 have female and male reference persons, respectively. Buyers of childcare services in the sample were 

173, of which 144 and 29, respectively, had female and male reference persons.
131

5.6 Summary

If a measure of consumption is to include the value of household production, the average value of that 

measure will be substantially higher than the value before the inclusion. We found this to be true 

irrespective of which measure—ours or the BEA’s—is used. However, the extent of the increase in 

expenditures is much larger when our measure is employed (1.5 vs. 2.0 times). The main reason is that we 

include supervisory childcare while the BEA measure excludes it. Defining childcare is far more crucial

than how it is valued for determining the size of household production. In contrast, the inclusion of 

unpaid help from non-household persons seems to have a minor impact on the average value of household 

production. As noted before, this may be partly due to the inadequacies in the available data to capture the 

full extent of such help.

The additional consumption that is made available to household members via household production is not 

made possible through household members' equal contribution to domestic labor. Indeed, this reflects the 

well-known gender disparity in time spent on household activities and childcare. Our estimates showed 

that women contributed 78 percent of the extra potential consumption due to household production.

We briefly examined how measured disparities between population subgroups are affected by the 

inclusion of household production. The number of adults and children strongly influences the volume of 

household production. Without including home production, we find that the average expenditures were 

highest for households with two adults and two children. When household production is added to 

expenditures, the highest average value is observed for the group with three or more adults and children.

It also became apparent that the presence of children caused the re-ranking. For households without 

children, the ranking is unaffected by the inclusion of household production. Our scrutiny of the change in 

racial disparities also showed the importance of the incidence of households with children. The most 

striking finding in this regard was the rank reversal of Hispanic and White families; Hispanics ranked 

slightly above Whites when household production was added to expenditures.

Including household production will lower the observed inequality in the distribution of a measure of 

consumption. Our assertion is based on analyzing the addition of household production to a standard 

measure of consumption expenditures. But, it is likely to hold for any other reasonable consumption 

metric. Our estimates show that, for consumer units in the bottom half of the standard expenditure 

distribution, the average value of household production is roughly as large as that of expenditures. For 

those in the top half, the average value of household production shows only a small variation across 

expenditure deciles compared with the sharp gradient of average expenditures. Thus, the relatively large 

size of household production and its somewhat equitable distribution across the expenditure distribution 
132

ensures that its addition will increase the expenditure shares

74 of those on the lower rungs of the 

distribution. This shift will be reflected in the new distribution, displaying a lower inequality than the 

prior distribution.

Two opposing forces affect the change in inequality. First, since the mean value of household production 

is notably higher for those with children than those without, adding home production to expenditures 

widens the intergroup inequality between the two groups in the augmented measure compared to the 

standard estimate of expenditures. However, because of the lesser inequality in the distribution of 

household production compared to the distribution of spending, the inequality in the augmented measure 

within both groups will be lower than in the standard measure. Irrespective of whether we use the BEA or 

our definition of household production, we found that the reduction in within-group inequality 

overwhelms the increase in between-group inequality. 

The extent of the reduction in inequality is larger with the BEA definition than ours. The difference is 

primarily because our measure contributes more to inequality in augmented expenditures than the BEA 

measure. Assessing the contributions made by the components of household production to inequality 

revealed that the difference stems from childcare. As noted above, our childcare measure is broader than 

the BEA measure because we include supervisory care, and hence the share of childcare in augmented 

expenditure is higher under our definition. In turn, the higher share of childcare results in a bigger

contribution to inequality.

Finally, we should note that the analysis of the matched data we provide illustrates the types of uses it 

has. Other assumptions and techniques can be applied, as in any scholarly research. For example, some of 

the findings reported here regarding subgroup disparities and Gini coefficients may be changed 

substantially if an equivalence scale were applied. We have also not estimated confidence intervals for the 

statistics reported in this chapter. Such an exercise may end up altering some of the inferences that we 

have made, e.g., regarding subgroup differentials.75 Further, our results are predicated on our assumptions

in converting the imputed values from a single weekday diary and single weekend-day diary to form 

weekly and monthly values. They also depend on our premises in aggregating from the imputed 

individual-level to household-level values. Alternative hypotheses and techniques could be deployed to 

74 For example, the share of each decile in the aggregate value of the new expenditures that includes the value of 

household production.

75 A technical question here is the appropriateness of using the CE replicate weights with variables imputed via 

statistical matching. We cannot provide estimated imputation errors associated with our statistical matching. On the 

other hand, imputation errors can be accounted for by using the values imputed by the MI method. A proper 

investigation of this issue falls outside the scope of our report.
133

address these questions as well. We hope the matched data and our initial analysis spur further research

on these topics.
134

6 Conclusion and Recommendations

The purpose of this chapter is to outline our recommendations for incorporating home production into a 

broader measure of consumption. We should note at the outset that, if such a measure is to be used for 

analyzing poverty, thresholds must be adjusted to account for home production needs in order to maintain 

consistency between the definitions of needs and resources.76 Our recommendations do not address the 

issue of such adjustments as that would need to be based on separate research. Instead, we focus on 

recommendations based on the analysis and conclusions outlined in Chapters 2–4. Below, we present 

them in the same order as the chapters.

6.1 Scope of home production

Recommendation 1.1. The definition of home production should be expanded to include supervisory care 

of household children under 13. The measure of supervisory care in the ATUS (secondary care) 

approximates this form of care provisioning in the home.

Our estimates show that, on average, consumer units with children under 18 spend 87 and 175 hours per 

month on active and supervisory childcare, respectively. Because we assign to supervisory care only onehalf of the rate we assign to active care, their monetary values are almost identical at just under $1,500 

per month. Even so, the monetary value of supervisory care is about 27 percent of the total average 

market expenditures of households with children. Ignoring this would be neglecting a sizeable part of 

caregiving services used by the household.

Recommendation 1.2. Care received from people outside the consumer unit without payment (i.e., care 

given by nonhousehold relatives and nonrelatives) should be included in the definition of home 

production. In addition, data collection efforts should be enhanced for gathering information on 

nonmarket care received from nonhousehold members by older children (5 to 13 years) and disabled or 

sick individuals under 50 years living in households.

We estimated the care children under 6, and older adults (over 50) received from nonhousehold relatives 

and nonrelatives. Among consumer units with children under 6, we found that roughly 12 percent (2.1 

million families) benefitted from the care given by nonhousehold people. The magnitude of the care 

received was quite substantial for recipient households. We estimated that the average monthly help for 

76 This issue of consistency has been discussed in detail in Citro and Michael (1995). The Levy Institute Measure of 

Time and Consumption Poverty that has been estimated for a variety of countries represents a potential approach to 

developing such a measure. More information is available at: https://www.levyinstitute.org/research/the-levyinstitute-measure-of-time-and-income-poverty
135

these households was 90 hours with a monetary value of $1,500 or about 27 percent of the average 

monthly market expenditures of the recipient units. Among consumer units with a person over 51, about 4 

percent of consumer units, or about 2.9 million households, received care from people outside the 

household. For the beneficiaries, the help received was sizeable in terms of time (about 106 hours per 

month) and as a proportion of their monthly market expenditures (40 percent). Given the omission of care 

received by people outside the age range of our estimates (individuals between 6 and 50 years) as well as 

the limited scope of care activities covered in the data sources for our calculations discussed in Chapter 4, 

our estimate is a lower-bound estimate of this type of caregiving.

6.2 Valuation of home production

Recommendation 2.1.

At this point in time, input valuation based on replacement cost using specialist wages is the best 

available option for valuation of non-market work. Activities should be disaggregated where feasible and 

appropriate, with attention to joint production. Active childcare should be treated as a relatively skilled 

task that builds on child-specific knowledge and relationship continuity to yield important developmental 

consequences. Supervisory care of young children should be recognized as a significant constraint on 

parental time that imposes costs. Valuation of supervisory care for children under 13 should take into 

account engagement in simultaneous activities; domestic work undertaken alongside supervisory care 

should be valued more highly than that without. We recommend valuation of supervisory care at 

approximately one half the value of active care (typically less than a babysitter’s wage). Further, 

geographical variation in replacement costs should be accounted for in the valuation by, e.g., using wage 

rates differentiated by metro areas.

6.3 Imputations from the ATUS

Recommendation 3.1. Considering that the ATUS is representative for quarters, we recommend that each 

quarterly CE Interview or Diary sample should be treated as an independent recipient file in the 

imputation rather than, for example, pooling all four quarters of the Diary sample for a given year as a 

single recipient file. This will ensure, at the very least, that imputed values for consumer units in each 

quarter are representative of the full-year data.

Recommendation 3.2. The weekday and weekend 24-hour diaries in the ATUS for a given year should be 

considered separate donor files, given the significant differences in time-use patterns between weekends 

and weekdays. Therefore, individuals over 14 years in the quarterly CE or Diary sample should have 
136

separate imputations from the weekday and weekend diaries, which would provide a better representation 

of weekly time-use patterns. Hours spent on home production per week can be calculated as a weighted 

average of the imputed weekday and weekend values.

Recommendation 3.3. Home production times should be disaggregated for each record in the synthetic 

(imputed) CE files. The categories to be created include cooking, other housework (including shopping), 

adult care, active childcare, supervisory childcare overlapping with other home production, and 

supervisory childcare that does not overlap with other home production. This may provide enough 

disaggregation for future research, without compromising on the quality of the data imputation. To avoid 

double counting in calculating the total time spent on household production, the time during which 

supervisory care coincides with other household production should be included only in one of the 

following categories: supervisory care overlapping with other household production, cooking, other 

housework, or adult care.

Recommendation 3.4. Supervisory childcare time for individuals in multi-adult households should be 

adjusted to avoid potential double-counting. Adjustments should be made on parameters differentiated by 

gender and the number of likely caregivers estimated from the ATUS. Better information is required to 

understand the dynamics of supervisory care provision in households with multiple caregivers and care 

receivers. Unfortunately, this cannot be obtained under the current ATUS sampling strategy of collecting 

time diaries from only one respondent per household.

Recommendation 3.5. For each record, the home production categories mentioned in Recommendation 

3.3 above should be imputed according to the three imputation methods described in Chapter 3. The 

methods are regression prediction (RP), multiple imputations (MI) based on regression models that 

account for censoring, and constrained statistical matching (SM). The availability of imputations based on 

alternative methods will offer the users some flexibility in exercising their methodological preferences.

Recommendation 3.6. Consumer unit-level values of home-production times according to the categories 

identified in Recommendation 3.3 should be constructed by adding up the times for individuals within the 

consumer unit. This operation should be conducted for the three imputation methods mentioned in 

Recommendation 3.5. 

Recommendation 3.7. The monetary values of household production times described in the previous 

recommendation should be constructed according to our preferred method and the generalist wage 

method outlined in Recommendation 2.1 above. Researchers would thus have the option of testing the 

sensitivity of their findings to the valuation principle. Providing individual-level time-use data may be 
137

advisable, giving researchers further flexibility to test hypotheses and sensitivity to the valuation 

assumptions.

6.4 Imputations of care received from outside the consumer unit

Recommendation 4.1. Nonmarket help received by members of the consumer unit from people outside 

their household (relatives and nonrelatives) should be imputed from the Health and Retirement Survey 

(HRS) and Early Childhood Program Participation (ECPP) survey. In our view, the HRS and ECPP are 

the best, though far from ideal, sources currently available for the incidence and duration of these forms 

of care. The hours of adult and childcare received at the consumer-unit level can be derived by adding the 

hours of care received by their members. Further, the hours should be converted into monetary values 

using the hourly rate applied to adult care and active childcare hours provided by household members. 

6.5 Recommendations for the use of synthetic CE public-use files

In theory, imputations are only as good as the strength of the conditional independence assumption (CIA). 

A direct test of CIA is generally impossible because the key variables we seek to study jointly are not 

available in the same sample, hence the need for imputation. Nevertheless, in our case, information on 

consumption expenditures and time spent on household production is collected in the Panel Study of 

Income Dynamics (PSID). While the expenditure definitions and time-use categories in the PSID do not

match those in the CE and ATUS, we believe that PSID is a reasonably good (and only, so far) data 

platform to conduct a test of the CIA. As we discussed in Appendix A of Chapter 3, the results from our 

testing strongly support the CIA.77 Indirectly, the evidence offered solid methodological support for the 

joint analysis of imputed time-use variables and observed consumption expenditures in the CE files. It is 

also worth noting that reputed academic journals publish papers that include imputed data as though it 

were actual data in statistical modeling without much concern for the CIA or adjustment for imputation 

errors (see Chapter 3 for some examples).

We have provided detailed information regarding the quality of the imputations in the report and onlinedata appendix. Overall, statistical matching (SM) performed as well and, in some cases, somewhat better 

77 As noted in Appendix A, Chapter 3, our validation exercise was conducted using total consumption expenditures. 

However, the method outlined there can be deployed to study the relationship between a specific broad category of 

expenditure, say food expenditures, and time-use variables using the PSID, bearing in mind the limitations of the 

data with respect to the categories. Advanced users seeking to explore such relationships using the imputed 

household production variables in the CE samples may want to conduct supplementary analysis using the PSID as a 

robustness check.
138

than the prediction methods, i.e., conditional mean (RP) and multiple imputations (MI), in replicating the 

average values of time spent on household production by men and women in various population 

subgroups from the American Time Use Survey (ATUS). Regarding medians of the same subgroups, SM 

produced imputed values that are, in general, closer to their ATUS counterparts than the other two 

techniques. Further, we found that the within-subgroup distribution of hours in the original data was also 

transferred to the synthetic CE files in a much more reliable manner by SM than by the other two 

imputation strategies. Thus, we recommend using the variables imputed by SM for descriptive analysis, 

such as calculating means, medians, and inequality measures (e.g., the Gini coefficient).

A caveat applies to our quality assessment. Suppose the research focus is at the consumer-unit level rather 

than the individual level. In that case, the researcher should bear in mind that our evaluation was 

conducted at the individual level (e.g., looking at the closeness of the imputed and observed median value 

of hours spent on cooking by employed men with children). We cannot perform quality assessments at the 

household level because the ATUS does not collect time diaries from everyone above 14 years in the 

household. However, since the household-level aggregate would result from the summation of 

individuals’ values in the home, we should expect good-quality imputations at the individual level to 

translate into good-quality imputations at the household level.

Further, imputations generally tend to be of lower quality for smaller population subgroups. This caveat 

holds especially if the group, on average, does not engage much in household production (e.g., men 

between 15 and 24 years). Users should, therefore, exercise caution in splitting the imputed data into finer 

subgroups. We would suggest a preliminary inspection of the ATUS data to ascertain the incidence of 

household production (or subcategories of household production such as cooking) in the subgroups of

their interest to avoid misleading results.

The main drawback of the SM and RP strategies is their inability to account for imputation errors in 

conducting statistical inference in the synthetic file, e.g., testing the equality of means for hours spent on 

childcare between two subgroups in the CE Interview sample from the second quarter of 2019. 

Specialized procedures are available in statistical software such as SAS and Stata to analyze multipleimputed data (e.g., PROC MIANALYZE in SAS or mi estimate in Stata). Users of the multipleimputed home-production values in the CE synthetic files should consult the documentation of their 

statistical software in choosing the methods appropriate for such data. 

Since SM is better at replicating the original ATUS data than MI, there is a paradox. While the MI 

method allows theoretically justifiable statistical inference, the statistically matched values may yield 

better-quality parameter estimates. Therefore, we recommend the analysis of interest using both sets of 

imputed values and comparing the results before stating solid conclusions. For example, consider using 
139

imputed hours of childcare of employed mothers as the dependent variable and some measure of market 

consumption as the independent variable, along with control variables (e.g., marital status, age, number of 

children, etc.) in a regression model. First, we would estimate the model using the imputed values of SM 

and MI separately. Then, suppose a comparison shows that the size and statistical significance of the 

coefficient on market consumption is the same irrespective of the set of imputed values used in the 

estimation. In that case, we can be reasonably confident about the estimated relationship. However, 

caution is required if there are incongruences in the parameter estimate or significance. And the 

researcher should exercise their judgment regarding the substantive importance of the incongruence.
140

Appendix A Validation exercise using the PSID

Background

The fundamental assumption behind imputation methods is the Conditional Independence Assumption 

(CIA). Basically, the premise is that, once we control for a set of relevant characteristics, the distribution 

of variables that cannot be observed jointly and require imputation is as good as independent. The 

assumption justifies the creation of a synthetic joint dataset by taking random draws from the conditional 

empirical (with or without replacement) or theoretical distribution.78

In the current project that seeks to impute time spent on household production by individuals in the CE 

using the ATUS, the CIA implies that time spent on household production activities by individuals is 

conditionally independent of household consumption expenditures. While this assumption is, in principle, 

untestable, we use the Panel Study of Income Dynamics (PSID), which has data on both time-use and 

consumption expenditures, to explore the level of correlation between time use and consumption as a 

validation exercise. Of course, the scope and nature of PSID are different from those of the ATUS and 

CE. However, several studies have found that the PSID provides good approximations of aggregate 

expenditure and time use, comparable to the CE and the ATUS.79 Thus, findings using this dataset may 

prove helpful as evidence in favor of or against the CIA.

Data

We use data from the PSID for the year 2019. This dataset has rich information on household and family 

characteristics, expenditure patterns, and detailed information on usual time-use activities and work 

history for the head of the household and his (her) spouse. 

It should be emphasized that the definition of time use in the PSID is different from that captured in the 

ATUS. In the PSID, respondents are the reference people and spouses in married-couple households 

and the reference person if that person is single. The ATUS collects time-use information from one 

member, 15 years or older, of each household in the sample. Also, the PSID collects information via 

78 Rubin (1986) proposed a method that does not assume CIA. Instead, he proposes to combine the donor and 

recipient files using assumptions about the value of the unobserved partial correlation between the variables of 

interest (e.g., time spent on household production and consumption expenditures). The initial value of the partial 

correlation is estimated from the parameters of separate linear regressions using common regressors in the donor and 

recipient files (e.g. OLS of consumption expenditures on a set of characteristics in the CE and OLS of time spent on 

household production on the same set of characteristics). In contrast, CIA assumes the value to be equal to zero. 

However, both approaches involve placing restriction on the partial correlation coefficient. Our exercise in this 

appendix uses a particular method and data where both variables are observed to assess the appropriateness of our 

assumption of CIA.

79 See Insolera et al. (2019) for a comparison of PSID and ATUS. Andreski et al. (2014) provide a comparison of 

consumption expenditures in PSID and CE.



144

Household:

Dummy for home ownership and number of rooms in the housing unit.

Family Characteristics:

Average years of education of all adult members, age of the youngest child, number of 

children by age group (0–1; 2–5; 6–12; 13–17), number of adults by gender in various 

age groups (18–24; 25–44; 45–64; 65+), number of employed adults, and a dummy for a 

child (0–17 years) in the household.

Except for gender and the presence of a child in the home, all other variables are used as is in the set of 

control variables Z. Given the sizeable difference in the time spent on household production between men 

and women and between individuals with and without children in their household, a combination of these 

two variables is used as a strict strata variable for the average conditional correlation approach,

81 or used 

interacted with the rest of the variables, for the regression approach.

Results

We start with the analysis of the CIA using the conditional correlation approach. In all cases, the main 

focus should be the rank correlation between total consumption and time spent on household production. 

As supplementary information, we also present the changes in the conditional correlation for the

components of household production that can be identified in the PSID.

Figure 1 relates the changes in the conditional correlation between household production and total 

household expenditure. The first point to consider is that, when no controls are used, the rank correlation 

between spending and time spent on household production is just above 0.12. Once we control for at least 

gender and the presence of a child, it drops drastically to 0.038, a level considered small and with few

economic implications. Interestingly, using the sub-clusters does not further reduce the average 

conditional correlation, which remains low, around 0.05. 

81 Using these as strict strata variables means that we will never form a cluster that will include both men and 

women or include men in households with and without children or women in households with and without children.
145

Appendix Figure A-1 Conditional correlation: total household production

Figure A2 presents a similar exercise as Figure A1, but where correlations are estimated using the 

components of household production: housework (“core”), shopping (“procurement”), childcare, and 

adult care. Since childcare is often the focus of the literature on the care economy, it is interesting to note 

that, when no controls are included, there is a strong correlation of almost 0.2. Like total household 

production, accounting for gender and the presence of children alone absorbs almost completely the 

correlation between childcare and consumption to negligible levels. In contrast, time spent on adult care 

seems uncorrelated with household consumption, even when we do not control for any characteristics. 

Appendix Figure A-2 Conditional correlation: components of household production

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

No controls Gender X

AnyChild

GC *

Cluster 10

GC *

Cluster 20

GC *

Cluster 30

GC *

Cluster 40

GC *

Cluster 50

GC *

Cluster 60

GC *

Cluster 70

GC *

Cluster 80

GC *

Cluster 90

GC *

Cluster 100

-0.05

0

0.05

0.1

0.15

0.2

0.25

No controls Gender X

AnyChild

GC *

Cluster 10

GC *

Cluster 20

GC *

Cluster 30

GC *

Cluster 40

GC *

Cluster 50

GC *

Cluster 60

GC *

Cluster 70

GC *

Cluster 80

GC *

Cluster 90

GC *

Cluster 100

T. Core

T. Procurement

T. Child Care

T. Adult Care

147

Appendix Table A-1 Residual correlation

Household 

Production Core Procurement Childcare Adult Care

No controls 0.0515 0.0367 0.0707 0.1443 -0.0168

Gender and Child Presence -0.0419 0.0316 0.0909 -0.0240 -0.0054

GC X Z 0.0260 0.0270 0.0560 0.0160 0.0150

The results align with the previous approach's findings, although there are some differences. At the outset, 

the correlation coefficients without any controls are relatively low for all household production 

categories, except perhaps for childcare (0.14). Controlling for gender and child presence seems to 

reverse the sign of the correlation coefficient for household production and childcare. In fact, the 

correlation coefficient drops considerably for childcare to –0.02. Controlling for other factors has almost 

no effect on core activities or procurement. However, for total household production, we observe a sharp 

decline. As in the previous case, procurement still correlates highest with expenditure compared to other 

time components.

Finally, we present the results from the direct regression approach of testing CIA—which includes the 

variable of interest in the model specification—and verify that the effects are small and non-significant. In 

Table A2, we report only the coefficient for the variable of interest and its standard error. Similar to the 

two previous cases, we report the estimated coefficient for a model without controls, except for the log of 

household expenditure, a model that controls for gender and the presence of a child, and the fully 

interacted model. Once again, the results are pretty consistent with the previously presented evidence. 

Appendix Table A-2 Conditional correlation coefficient of log(hh Exp)

Coefficient of log (HHexp)

Household 

Production Core Procurement Childcare Adult Care

No controls 3.712*** 0.742** 0.599*** 19.24*** -2.551

(0.710) (0.233) (0.107) (1.360) (1.675)

Gender and Child Presence -2.707*** 0.622** 0.775*** -2.735* -0.830

(0.674) (0.229) (0.108) (1.215) (1.706)

GC X Z 0.883 0.564 0.815*** 0.967 0.903

(0.820) (0.293) (0.133) (1.513) (2.188)

Note: Standard errors in parenthesis. *** - significant at 1%, ** - significant at 5%.

In the models with no controls, the impact of household consumption is statistically significant (except 

for time spent on adult care), especially in the models for the time spent on childcare and total household 
148

production. Controlling for gender and child presence substantially reduces the coefficient on childcare 

and reverses its sign. Nevertheless, the effect remains significant. We also observe that the impact on 

household production remains significant but with a negative sign. However, once we use the fully 

interacted model, the statistical significance of the relationship between household expenditure, total time 

on household production, and time spent on childcare disappears. While the effects for procurement and 

core activities remain statistically significant, the magnitudes of the effects suggest that they may not be 

economically significant. Specifically, the model predicts an increase of 0.07 and 0.08 hours (less than 

five minutes) in time spent on core activities and procurement for a 10 percent change in expenditure. 

To summarize, the evidence suggests that the CIA assumption holds, at least once demographic 

differences and family structure characteristics are controlled. Moreover, these results are consistent 

across all the methodologies for testing for the CIA we presented here. But, our testing is with total 

expenditures. The outcomes may not be so encouraging if the relationship is examined for subcategories 

of expenditure and time use (e.g., food expenditures and time spent on household production).
149

Appendix B Comparison of the results from two algorithms of multiple 

imputation 

As described in the main text, we use two different algorithms for the multiple imputation (MI) approach. 

First, when using the statistical software Stata, we use an interval regression model, a generalization of 

the Tobit-censored regression model, to identify the data-generating process of time-use activities. The 

model has sound econometric properties and is meant to correctly address the presence of excess zeroes in 

data, as is the case for time use.

Due to software limitations, when using the statistical software SAS, we use a fully conditional 

specification method (FCS) based on a linear regression (LR) model. Predictions of the model are 

censored out after all imputations are created. Because this approach uses an LR model for a limited 

dependent variable with a large number of zeroes, we expect the coefficients and predictions to be biased 

after the censored correction. SAS does not have a Tobit model for multiple imputation procedures.

To compare the performance of the procedures, we estimate the absolute relative difference of mean, 

median, and standard deviation for imputed total hours of household production for various subgroups 

and compare them across the Stata (Tobit) and SAS(LR-FCS) procedures. We concentrate on household 

production over weekends, focusing on a single replicate. The results for weekdays are similar. 

Figure B1 provides a scatter plot of the absolute relative differences for mean, median, and standard 

deviation. If the SAS procedure were superior, we would see that the scatterplot of points to lean below 

and to the right of the main diagonal. In contrast, if the Stata procedure is superior, we should see the 

opposite, with the scatterplot of points leaning upward and to the right of the main diagonal. 

What we observe, however, is that, despite the theoretical advantages of the Tobit model that Stata has on 

characterizing the conditional distribution of time, given the large presence of zeroes, there is no clear 

advantage for the Stata results. The cloud of points distribution suggests that both approaches have 

advantages and disadvantages across different subgroups.82

82 The cloud of points represents absolute percentage difference between the ATUS subgroup statistic and the 

imputed data. The closer the point is to zero, the better was the imputation to carry over the distribution of time use. 

Points below the 45-degree line suggest SAS FCS procedure produces better imputed values, whereas points above 

the line suggest Stata-censored regression imputation to perform better.
150

Appendix Figure B-1 Comparison of SAS and Stata methods of multiple imputation
151

References

Aassve, A., Meroni, E., & Pronzato, C. (2012). “Grandparenting and Childbearing in the Extended 

Family.” European Journal of Population, 28(4), 499–518.

Andreski, P., Li, G., Samancioglu, M. Z., & Schoeni, R. (2014). “Estimates of Annual Consumption 

Expenditures and Its Major Components in the PSID in Comparison to the CE,” American 

Economic Review, 104(5): 132–135.

Antonopoulos, R. (2008). The Unpaid Care Work-Paid Work Connection.Annandale-on-Hudson, NY: 

Levy Economics Institute of Bard College. Working Paper No. 541.

Armstrong, G., Cho, C., Garner, T. I., Matsumoto, B., Munoz, J., & Schild, J. (2022). “Building a 

Consumption Poverty Measure: Initial Results Following Recommendations of a Federal 

Interagency Working Group. AEA Papers and Proceedings, 112: 335–339. 

https://doi.org/10.1257/pandp.20221041

Attanasio, O., & Pistaferri, L. (2014). “Consumption Inequality over the Last Half Century: Some 

Evidence Using the New PSID Consumption Measure,” American Economic Review, 104(5):

122–126.

Brady, M. (2016). “Gluing, Catching and Connecting: How Informal Childcare Strengthens Single 

Mothers’ Employment Trajectories,” Work, Employment & Society, 30(5): 821–37.

Bridgman, B., Craig, A., & Kanal, D. (2022). “Accounting for Household Production in the National 

Accounts: An Update 1965–2020,” Survey of Current Business. https://apps.bea.gov/scb/2022/02-

february/0222-household-production.htm

Cardia, E., & Ng, S. (2003). Intergenerational Time Transfers and Childcare,” Review of Economic 

Dynamics, 6(2): 431–54. https://doi.org/10.1016/S1094-2025(03)00009-7

Casper, L., M. (1996). Who’s Minding Our Preschoolers? (No. P70-53; Household Economic Studies). 

U.S. Census Bureau. https://www.census.gov/library/publications/1996/demo/p70-53.html

Citro, C. F. and Michael, Robert T. 1995. Measuring Poverty: A New Approach. National Academy 

Press.

Coleman, M. (1998). “Homemaker as Worker in the United States.” Challenge 41(6): 75–87.

Collins, A. & Carlson, B. (1998). Childcare by kith and kin: Supporting family, friends and neighbors 

caring for children (No. 5; Issue Brief). National Center for Children in Poverty. 

https://www.researchconnections.org/childcare/resources/2157

Crossley, T., Levell, P., & Poupakis, S. (2020). “Regression with an Imputed Dependent Variable” (No. 

20/25; Working Paper). London: Institute for Fiscal Studies. 

Cui, J. & Natzke, L. (2021). Early Childhood Program Participation: 2019. (NCES 2020-075REV), 

National Center for Education Statistics, Institute of Education Sciences, U.S. Department of 

Education. Washington, DC. Retrieved January 28, 2023 from 

http://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2020075REV.

Enders, C. K. (2022). Applied missing data analysis (Second Edition). The Guilford Press.

Ettner, S. L. (1995). “The Impact of “Parent Care” on Female Labor Supply Decisions,” Demography, 

32(1): 63–80. https://doi.org/10.2307/2061897

Fisher, G. G. & Ryan, L. H. (2018). Overview of the Health and Retirement Study and Introduction to the 

Special Issue. Work, Aging and Retirement, 4(1), 1–9. 
152

Fisher, J. D., Johnson, D. S., Smeeding, T. M., & Thompson, J. P. (2020). “Estimating the Marginal 

Propensity to Consume Using the Distributions of Income, Consumption, and Wealth,” Journal 

of Macroeconomics, 65, 103218.

Folbre, N., Murray-Close, M., & Suh, J. (2018). “Equivalence Scales for Extended Income in the U.S. 

Review of Economics of the Household, 16(2): 189–227. https://doi.org/10.1007/s11150-017-

9387-8

Folbre, Nancy. (2009). “Time Use and Living Standards.” Social Indicators Research 93(1): 77–83.

Folbre, N., Yoon, J., Finnoff, K., & Fuligni, A. S. (2005). “By What Measure? Family Time Devoted to 

Children in the United States.” Demography 42 (2): 373–90.

Frazis, H. & Stewart, J. (2011). How Does Household Production Affect Measured Income Inequality? 

Journal of Population Economics, 24(1): 3–22.

Fuchs, V. R. (1986). “His and Hers: Gender differences in Work and Income, 1959-1979.” Journal of 

Labor Economics 4(3, Part 2, S245-S272.

Garner, T. I., Matsumoto, B., Schild, J., Curtin, S. & Safir, A. (2023). “Developing a consumption 

measure, with examples of use for poverty and inequality analysis: a new research product from 

BLS,” Monthly Labor Review, U.S. Bureau of Labor Statistics, April. 

https://doi.org/10.21916/mlr.2023.8

Gautham, L. & Folbre, N. (forthcoming). “Parental Expenditures of Time and Money on Children in the 

U.S.,” Review of Income and Wealth. 

Goldschmidt-Clermont, L. (1993). “Monetary Valuation of Non-Market Productive Time: 

Methodological Considerations.” Review of Income and Wealth 39(4): 419–33.

Grigoryeva, A. (2017). Own Gender, Sibling’s Gender, Parent’s Gender: The Division of Elderly Parent 

Care among Adult Children. American Sociological Review, 82(1): 116–46.

Hawkins, J., Habib, K. N. (2022). “A Multi-source Data Fusion Gramework for Joint Population, 

Expenditure, and Time use Synthesis”. Transportation. https://doi.org/10.1007/s11116-022-

10279-8

He, D. & McHenry, P. (2016). “Does Formal Employment Reduce Informal Caregiving?” Health 

Economics, 25(7): 829–43. https://doi.org/10.1002/hec.3185

Herbst, C. M. (2023). “Childcare in the United States: Markets, Policy, and Evidence,” Journal of Policy 

Analysis and Management, 42(1): 255–304. https://doi.org/10.1002/pam.22436

Insolera, N., E., Johnson, D., S., & Simmert, B., A. (2019). Evaluation of the Time Use data in PSID with 

comparisons to ATUS (#19-02; Technical Series Paper). Institute for Social Research, University 

of Michigan.

Jenkins, S. P. & O’Leary, N. C. (1996). Household Income Plus Household Production: The Distribution 

of Extended Income in the U.K. Review of Income and Wealth, 42(4): 401–19.

Jokubauskaitė, S. & Schneebaum, A. (2021). “Assessing the Value of Household Work Based on Wages 

Demanded on Online Platforms for Substitutes,” Review of Economics of the Household 20: 153–

60.

Kneeland, H. (1929). Woman’s Economic Contribution in the Home. Annals of the American Academy of 

Political and Social Science, 143(May): 33–40.

Kum, H., & Masterson, T. N. (2010). Statistical matching using propensity scores: Theory and application 

to the analysis of the distribution of income and wealth. Journal of Economic and Social 

Measurement, 35(3), 177–196. 
153

Kuznets, S, Epstein, L. & Jenks, E. (1941). “National Income and Its Composition, 1919-1938, Volume 

II.” New York: National Bureau of Economic Research (NBER). 

http://www.nber.org/books/kuzn41-3 (January 4, 2013).

Landefeld, J. S., Fraumeni, B. M., & Vojtech, C. M. (2009). “Accounting for Household Production: A 

Prototype Satellite Account Using the American Time Use Survey,” Review of Income and 

Wealth, 55(2): 205–25.

Landefeld, J. S., & McCulla, S. H. (2000). “Accounting for Nonmarket Household Production within a 

National Accounts Framework.” Review of Income and Wealth, 46(3): 289–307.

Laughlin, L. (2013). Who’s Minding the Kids? Childcare Arrangements: Spring 2011 (No. P70-135; 

Household Economic Studies). U.S. Census Bureau. 

https://www.census.gov/library/publications/2013/demo/p70-135.html

Maestas, N., Messel, M., & Truskinovsky, Y. (2020). “Caregiving and Labor Force Participation: New 

Evidence from the Survey of Income and Program Participation,” No. WI20-12). Madison, WI: 

Center for Financial Security University of Wisconsin Madison Retirement and Disability 

Research Center.

Milkie, M. (2022). “Beyond Mothers’ Time in Childcare: Worlds of Care and Connection in the Early 

Life Course.” Manuscript, Department of Sociology, University of Toronto, under review at Time 

and Society.

Moussa, M. M. (2019). “The Relationship between Elder Care-giving and Labour Force Participation in 

the Context of Policies Addressing Population Ageing: A Review of Empirical Studies Published 

between 2006 and 2016,” Ageing & Society, 39(6): 1281–310. 

https://doi.org/10.1017/S0144686X18000053

Mullan, K. (2010). “Valuing Parental Childcare in the United Kingdom.” Feminist Economics 16(3): 

113–39.

National Research Council. (2005). Beyond the Market: Designing Nonmarket Accounts for the United 

States (K. G. Abraham & C. Mackie, Eds.). The National Academies Press. 

https://doi.org/10.17226/11181

Newman, D. A. (2014). “Missing Data: Five Practical Guidelines.” Organizational Research 

Methods, 17(4), 372–411. https://doi.org/10.1177/1094428114548590

Osuna, V. (2021). “Subsidising Formal Childcare Versus Grandmothers’ Time: Which Policy is More 

Effective?” Economics: Journal Articles, 15(1): 85–111. https://doi.org/10.1515/econ-2021-0007

Reid, M. G. (1934). Economics of Household Production. New York: J. Wiley & Sons.

Royston, P. (2007). Multiple Imputation of Missing Values: Further Update of Ice, with an Emphasis on 

Interval Censoring. The Stata Journal: Promoting Communications on Statistics and Stata, 7(4), 

445–464. https://doi.org/10.1177/1536867X0800700401

Rubin, D. B. (1986). “Statistical Matching Using File Concatenation with Adjusted Weights and Multiple 

Imputations” Journal of Business & Economic Statistics, 4(1) (Jan., 1986): 87–94.

Shapiro, L. (2009). Perfection Salad: Women and Cooking at the Turn of the Century. University of 

California Press. 

Suh, J. & Folbre, N. (2023). “The Responsibilities of Parental Childcare: Evidence from the American 

Time Use Survey,” paper presented at the meetings of the Allied Social Science Meetings, New 

Orleans, LA, January.
154

Suh, J. & N. Folbre. (2015). “Valuing Unpaid Childcare in the US: A Prototype Satellite Account Using 

the American Time Use Survey.” Review of Income and Wealth, April, 1–17.

van Buuren, S. (2007). Multiple imputation of discrete and continuous data by fully conditional 

specification. Statistical Methods in Medical Research, 16(3), 219–242. 

Wheelock, J. & Jones, K. (2002). ‘Grandparents Are the Next Best Thing’: Informal Childcare for 

Working Parents in Urban Britain. Journal of Social Policy, 31(3), 441–463. 

https://doi.org/10.1017/S0047279402006657

Wolff, E. N. & Zacharias, A. (2007). “The Levy Institute Measure of Economic Well-Being: United 

States, 1989–2001.” Eastern Economic Journal, 33(4): 443–70.

Yao, S. (1999). On the decomposition of Gini coefficients by population class and income source: A 

spreadsheet approach and application. Applied Economics, 31(10), 1249–1264. 

https://doi.org/10.1080/000368499323463

Ye, P., Hu, X., Yuan, Y., & Wang, F.Y. (2017). “Population Synthesis Based on Joint Distribution 

Inference without Dis-aggregate Samples,” JASSS (2017). https:// doi. org/ 10. 18564/ jasss. 

3533

Zacharias, A., Masterson, T., Rios-Avilla, F., Nikiforos, M., Kim, K., & Khitarshvili, T. (2019). 

Macroeconomic and Microeconomic Impacts of Improving Physical and Social Infrastructure: A 

Macro-Micro Policy Model for Ghana and Tanzania (September). Levy Economics Institute of 

Bard College. http://www.levyinstitute.org/publications/macroeconomic-and-microeconomicimpacts-of-improving-physical-and-social-infrastructure-a-macro-micro-policy-model-for-ghanaand-tanzania

Zang, E., Yang, Y. M., & Calarco, J. M. (2022). Patterns in Receiving Informal Help with Childcare 

Among US Parents During the COVID-19 Pandemic (SSRN Scholarly Paper No. 4108245). 

https://doi.org/10.2139/ssrn.4108245