Board of Governors of the Federal Reserve System

Description of the Survey

The Survey of Household Economics and Decisionmaking was fielded from October 21 through November 1, 2022. This was the 10th year of the survey, conducted annually in the fourth quarter of each year since 2013.⁶² Staff of the Federal Reserve Board wrote the survey questions in consultation with other Federal Reserve System staff, outside academics, and professional survey experts.

Ipsos, a private consumer research firm, administered the survey using its KnowledgePanel, a nationally representative probability-based online panel. Since 2009, Ipsos has selected respondents for KnowledgePanel based on address-based sampling (ABS). SHED respondents were then selected from this panel.

Survey Participation

Participation in the 2022 SHED depended on several separate decisions made by respondents. First, they agreed to participate in Ipsos's KnowledgePanel. According to Ipsos, 9.3 percent of individuals contacted to join KnowledgePanel agreed to join (study-specific recruitment rate). Next, they completed an initial demographic profile survey. Among those who agreed to join the panel, 60.8 percent completed the initial profile survey and became a panel member (study-specific profile rate). Finally, selected panel members agreed to complete the 2022 SHED.

Of the 18,430 panel members contacted to take the 2022 SHED, 11,775 participated and completed the survey, yielding a final-stage completion rate of 63.9 percent.⁶³ Taking all the stages of recruitment together, the cumulative response rate was 3.6 percent. After removing a small number of respondents because of high refusal rates or completing the survey too quickly, the final sample used in the report included 11,667 respondents.⁶⁴

Targeted Outreach and Incentives

To increase survey participation and completion among hard-to-reach demographic groups, Board staff and Ipsos used a targeted communication plan with monetary incentives. The target groups—young adults age 18 to 29; adults with less than a high school degree; adults with household income under $50,000 who are under age 60; and those who are a race or ethnicity other than White, non-Hispanic—received additional email reminders during the field period, as well as additional monetary incentives.

All survey respondents not in a target group received a $5 incentive payment after survey completion. Respondents in the target groups received a $15 incentive. These targeted individuals also received an additional follow-up email during the field period to encourage completion.⁶⁵

Survey Questionnaire

The 2022 survey took respondents 22.9 minutes (median time) to complete.

A priority in designing the survey questions was to understand how individuals and families—particularly those with low- to moderate-income—fared financially in 2022. The questions were intended to complement and augment the base of knowledge from other data sources, including the Board's Survey of Consumer Finances. In addition, some questions from other surveys were included to allow direct comparisons across datasets.⁶⁶ The full survey questionnaire can be found in appendix A of this report.

Survey Mode

While the sample was drawn using probability-based sampling methods, the SHED was administered to respondents entirely online. Online interviews are less costly than telephone or in-person interviews and can be an effective way to interview a representative population.⁶⁷ Ipsos's online panel offers some additional benefits. Their panel allows the same respondents to be re-interviewed in subsequent surveys with relative ease, as they can be easily contacted for several years.

Furthermore, internet panel surveys have numerous existing data points on respondents from previously administered surveys, including detailed demographic and economic information. This allows for the inclusion of additional information on respondents without increasing respondent burden.⁶⁸ The respondent burdens are further reduced by automatically skipping irrelevant questions based on responses to previous questions.

The "digital divide" and other differences in internet usage could bias participation in online surveys, so recruited panel members who did not have a computer or internet access were provided with a laptop and access to the internet to complete the surveys. Even so, individuals who complete an online survey may have greater comfort or familiarity with the internet and technology than the overall adult population, which has the potential to introduce bias in the characteristics of who responds.

Sampling and Weighting

The SHED sample was designed to be representative of adults age 18 and older living in the United States.

The Ipsos methodology for selecting a general population sample from KnowledgePanel ensured that the resulting sample behaved as an equal probability of selection method (EPSEM) sample. This methodology started by weighting the entire KnowledgePanel to the benchmarks in the latest March supplement of the Current Population Survey (CPS) along several geo-demographic dimensions. This way, the weighted distribution of the KnowledgePanel matched that of U.S. adults. The geo-demographic dimensions used for weighting the entire KnowledgePanel included gender, age, race, ethnicity, education, census region, household income, homeownership status, and metropolitan area status.

Using the above weights as the measure of size (MOS) for each panel member, in the next step a probability proportional to size (PPS) procedure was used to select study specific samples. This methodology was designed to produce a sample with weights close to one, thereby reducing the reliance on post-stratification weights for obtaining a representative sample.

After the survey collection was complete, statisticians at Ipsos adjusted weights in a post-stratification process that corrected for any survey non-response as well as any non-coverage or under- and oversampling in the study design. The following variables were used for the adjustment of weights for this study: age, gender, race, ethnicity, census region, residence in a metropolitan area, education, and household income. These weighting variables are consistent with those used in earlier waves of the survey. Demographic and geographic distributions for the noninstitutionalized, civilian population age 18 and older from the March CPS were the benchmarks in this adjustment. Household income benchmarks were obtained from the 2022 March CPS.

One feature of the SHED is that a subset of respondents also participated in prior waves of the survey. In 2022, about one-third of respondents had participated in the fall 2021 survey. Prior year case identifiers for these repeat respondents are available in the publicly available dataset, along with weights for this subset of respondents. These weights use a similar procedure as described above to ensure estimates based on the repeated sample are representative of the U.S. population.

Although weights allow the sample population to match the U.S. population (excluding those in the military or in institutions, such as prisons or nursing homes) based on observable characteristics, similar to all survey methods, it remains possible that non-coverage, non-response, or occasional disparities among recruited panel members result in differences between the sample population and the U.S. population. For example, address-based sampling likely misses homeless populations, and non-English speakers may not participate in surveys conducted in English.⁶⁹

Despite an effort to select the sample such that the unweighted distribution of the sample more closely mirrored that of the U.S. adult population, the results indicate that weights remain necessary to accurately reflect the composition of the U.S. population. Consequently, all results presented in this report use the post-stratification weights produced by Ipsos for use with the survey.

Item Non-response and Imputation

Item non-response in the 2022 SHED was handled by imputation. Typically, less than 1 percent of observations were missing for each question, although non-response was higher for some questions such as income amounts.⁷⁰ As a result, population estimates were not sensitive to the imputation procedure and a simple regression approach was used.⁷¹ For continuous variables such as income, rent, and mortgage payment amounts, a hot deck approach was used.⁷²

The imputation procedure was carried out as follows:

1. Impute questions, like income and education, to be used in the imputation models throughout.
2. Continue at the beginning of the survey and impute missing values sequentially, question by question.

In some cases, the imputation for one question affected later questions by switching an observation from out-of-universe to in-universe or vice versa. These cases were handled by imputing the missing "downstream" question response or recoding it to missing, where appropriate.

Each variable in the publicly available SHED dataset has a corresponding imputation flag, ‘var'_iflag, which is set to 1 if the observation was imputed and 0 otherwise.⁷³ For example, the first question of the survey about whether the respondent lived with their spouse or partner, L0_a, has a corresponding imputation flag of L0_a_iflag. This question had 28 missing values that were imputed, accounting for 0.2 percent of all observations.

 

References

 

 62. Data and reports of survey findings from all past years are available at https://www.federalreserve.gov/consumerscommunities/shed.htm. Return to text

 63. Four hundred seventy-four respondents were not included in the analysis because they started, but did not complete, the survey (known as break-offs). The study break-off rate for the SHED was 3.9 percent. Return to text

 64. Of the 11,775 respondents who completed the survey, 108 were excluded from the analysis in this report because of either leaving responses to a large number of questions missing, completing the survey too quickly, or both. Return to text

 65. All participants received a pre-notification email before the survey launch. They also received a reminder on the third day of the field period in addition to the initial survey invitation. Targeted respondents received one additional email reminder on day seven of fielding. Return to text

 66. For a comparison of results to select overlapping questions from the SHED and Census Bureau surveys, see Jeff Larrimore, Maximilian Schmeiser, and Sebastian Devlin-Foltz, "Should You Trust Things You Hear Online? Comparing SHED and Census Bureau Survey Results," Finance and Economics Discussion Series Notes (Washington: Board of Governors of the Federal Reserve System, October 15, 2015), https://doi.org/10.17016/2380-7172.1619. Return to text

 67. David S. Yeager et al., "Comparing the Accuracy of RDD Telephone Surveys and Internet Surveys Conducted with Probability and Non-Probability Samples," Public Opinion Quarterly 75, no. 4 (2011): 709–47. Return to text

 68. This approach also may allow for the retroactive linking of information learned about respondents from other data, as was done in 2022 to determine Asian respondents in earlier years of the survey. Return to text

 69. For example, while the survey was weighted to match the race and ethnicity of the entire U.S. adult population, there is evidence that the Hispanic population in the survey were somewhat more likely to speak English at home than the overall Hispanic population in the United States. In the 2022 SHED, the percent of Hispanic adults who speak Spanish at home is below estimates from the 2021 American Community Survey. See table B16006 at https://data.census.gov. For a comparison of results to select questions administered in Spanish and English, see Board of Governors of the Federal Reserve System, Report on the Economic Well-Being of U.S. Households in 2017 (Washington: Board of Governors, May 2018), https://www.federalreserve.gov/publications/files/2017-report-economic-well-being-us-households-201805.pdf. Return to text

 70. Because item non-response is very low in the SHED, 2022 estimates are comparable with prior years where item non-response was handled differently. Return to text

 71. A logit regression was used for binary variables, a multinomial logit for categorical variables, an ordinal logit for ordered values, and a linear regression for continuous values. Typical predictors included income, education, race and ethnicity, age, gender, and metropolitan status, but varied depending on how well they predicted the variable of interest and item non-response. Additional predictors were included as appropriate. Return to text

 72. This approach involved assigning values to non-responses by copying responses from demographically similar respondents. To do this, we first grouped respondents by characteristics such as education, age, and income, and we then arranged respondents within groups by the time of their survey completion. Each non-response was matched with the nearest neighbor within their group based on survey completion time, and values were imputed for each non-response by drawing from their nearest neighbor's response. Return to text

 73. The survey data can be downloaded from the Federal Reserve website at https://www.federalreserve.gov/consumerscommunities/shed_data.htm. Return to text