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1. Study on U.S. Parents' Divisions of Labor During COVID-19, Waves 1-3

   https://doi.org/10.3886/E194725V1  

   Carlson, Daniel L. ; Petts, Richard J. ( October 2023 , ICPSR - Interuniversity Consortium for Political and Social Research)

   The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic and beyond, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally. The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short-and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality. Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of the SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of the SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in October 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. Wave 4 of the SPDLC was collected in October 2022. Parents who participated in either of the first three waves were invited to participate again in Wave 4, and another new cohort of parents was also recruited to participate in the Wave 4 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through October 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study. SPDLC data will be released to the public two years after data is collected; Waves 1-3 are currently publicly available. Wave 4 will be publicly available in October 2024, with subsequent waves becoming available yearly. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available. 

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2. Managing a Household During a Pandemic: Cognitive Labor and Parents’ Psychological Well-being

   https://doi.org/10.1177/21568693231169521  

   Petts, Richard J. ; Carlson, Daniel L. ( May 2023 , Society and Mental Health)

   Rising domestic burdens for mothers fueled concerns that the COVID-19 pandemic exacerbated gender inequalities in well-being. Yet, survey research has not considered whether and how cognitive labor—planning, organizing, and monitoring family needs—contributed to gendered health disparities during the pandemic. Using data from the Study on U.S. Parents’ Divisions of Labor during COVID-19 (SPDLC) and a stress process perspective, we examine the association between cognitive labor and parents’ psychological well-being, and whether this association (1) differs between mothers and fathers and (2) is moderated by employment status and telecommuting. Mothers performed more cognitive labor during the pandemic than fathers, and cognitive labor was negatively associated with mothers’ psychological well-being—particularly for mothers who never or exclusively telecommuted. Mothers’ psychological well-being was higher when fathers did more cognitive labor, especially among mothers who worked outside the home. Overall, cognitive labor appears to be another stressor that contributed to increased gender inequality.

    

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3. Remote Work, Gender Ideologies, and Fathers’ Participation in Childcare during the COVID-19 Pandemic

   https://doi.org/10.3390/socsci13030166  

   Carlson, Daniel L. ; McPherson, Skye ; Petts, Richard J. ( March 2024 , Social Sciences)

   Jacobs, Jerry (Ed.)

   During the early days of the COVID-19 pandemic, remote work became the new reality for many fathers. Though time availability theory suggests that this newfound flexibility should lead to more domestic labor on the part of fathers, many were skeptical that fathers would step up to shoulder the load at home. Indeed, the findings are decidedly mixed on the association of fathers’ remote work with their performance of housework and childcare. Nonetheless, research has yet to consider how contextual factors, such as fathers’ gender ideologies and mothers’ employment, may condition these associations. Using data from Wave 1 of the Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC), we examine how gender ideology moderates the association between fathers’ remote work and their performance and share of childcare during the early days of the COVID-19 pandemic in both sole-earner and dual-earner families. The results show, for sole-earning fathers and dual-earner fathers with egalitarian gender attitudes, that the frequency of remote work was positively associated with fathers performing more, and a greater share of, childcare during the pandemic. Yet, only dual-earner fathers with egalitarian gender attitudes performed an equal share of childcare in their families. These findings suggest that the pandemic provided structural opportunities for fathers, particularly egalitarian-minded fathers, to be the equally engaged parents they desired.

    

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4. U.S. household food waste tracking data in support of Li et al. 2023

   https://doi.org/10.6073/pasta/33762da1c9eb20ba3e12d23fe73e67f7  

   Roe, Brian ( January 2023 , Environmental Data Initiative)

   These data were used to generate the results in the article “Household Food Waste Trending Upwards in the United States: Insights from a National Tracking Survey,” by Ran Li, Yiheng Shu, Kathryn E. Bender & Brian E. Roe, which has been accepted for publication in the Journal of the Agricultural and Applied Economics Association (doi – pending). The Stata code used to generate results is available from the authors upon request. U.S. residents who participate in consumer panels managed by a commercial vendor were invited by email or text message to participate in a two-part online survey during four waves of data collection: February and March of 2021 (Feb 21 wave, 425 initiated, 361 completed), July and August of 2021 (Jul 21 wave, 606 initiated, 419 completed), December of 2021 and January of 2022 (Dec 21 wave, 760 initiated, 610 completed), and February, March and April of 2022 (Feb 22 wave, 607 initiated, 587 completed). We are not able to determine if any respondents participated in multiple waves, i.e., if any of the observations are repeat participants. All participants provided informed consent and received compensation. Inclusion criteria included age 18 years or older and performance of at least half of the household food preparation. No data was collected during major holidays, i.e., the weeks of the Fourth of July (Independence Day), Christmas, or New Years. Recruitment quotas were implemented to ensure sufficient representation by geographical region, race, and age group. Post-hoc sample weights were constructed to reflect population characteristics on age, income and household size. The protocol was approved by the local Internal Review Board. The approach begins with participants completing an initial survey that ends with an announcement that a follow-up survey will arrive in about one week, and that for the next 7 days, participants should pay close attention to the amounts of different foods their household throws away, feeds to animals or composts because the food is past date, spoiled or no longer wanted for other reasons. They are told to exclude items they would normally not eat, such as bones, pits, and shells. Approximately 7 days later they received the follow-up survey, which elicited the amount of waste in up to 24 categories of food and included other questions (see supplemental materials for core survey questions). Waste amounts in each category are reported by selecting from one of several ranges of possible amounts. The gram weight for categories with volumetric ranges (e.g., listed in cups) were derived by assigning an appropriate mass to the midpoint of the selected range consistent with the food category. For the categories with highly variable weight per volume (e.g., a cup of raw asparagus weighs about 7 times more than a cup of raw chopped arugula), we use the profile of items most consumed in the United States to determine the appropriate gram weight. For display purposes, the 24 categories are consolidated into 8 more general categories. Total weekly household food waste is calculated by summing up reported gram amounts across all categories. We divide this total by the number of household members to generate the per person weekly food waste amount. 

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5. Defining First-generation and Low-income Students in Engineering: An Exploration

   https://doi.org/10.18260/1-2--34373  

   Atwood, Sara A ; Gilmartin, Shannon K ; Harris, Angela ; Sheppard, Sheri ( January 2020 , ASEE Annual Conference proceedings)

   First-generation (FG) and/or low-income (LI) engineering student populations are of particular interest in engineering education. However, these populations are not defined in a consistent manner across the literature or amongst stakeholders. The intersectional identities of these groups have also not been fully explored in most quantitative-based engineering education research. This research paper aims to answer the following three research questions: (RQ1) How do students’ demographic characteristics and college experiences differ depending on levels of parent educational attainment (which forms the basis of first-generation definitions) and family income? (RQ2) How do ‘first-generation’ and ‘low-income’ definitions impact results comparing to their continuing-generation and higher-income peers? (RQ3) How does considering first-generation and low-income identities through an intersectional lens deepen insight into the experiences of first-generation and low-income groups? Data were drawn from a nationally representative survey of engineering juniors and seniors (n = 6197 from 27 U.S. institutions). Statistical analyses were conducted to evaluate respondent differences in demographics (underrepresented racial/ethnic minority (URM), women, URM women), college experiences (internships/co-ops, having a job, conducting research, and study abroad), and engineering task self-efficacy (ETSE), based on various definitions of ‘first generation’ and ‘low income’ depending on levels of parental educational attainment and self-reported family income. Our results indicate that categorizing a first-generation student as someone whose parents have less than an associate’s degree versus less than a bachelor’s degree may lead to different understandings of their experiences (RQ1). For example, the proportion of URM students is higher among those whose parents have less than an associate’s degree than among their “associate’s degree or more” peers (26% vs 11.9%). However, differences in college experiences are most pronounced among students whose parents have less than a bachelor’s degree compared with their “bachelor’s degree or more” peers: having a job to help pay for college (55.4% vs 47.3%), research with faculty (22.7% vs 35.0%), and study abroad (9.0% vs 17.3%). With respect to differences by income levels, respondents are statistically different across income groups, with fewer URM students as family income level increases. As family income level increases, there are more women in aggregate, but fewer URM women. College experiences are different for the middle income or higher group (internship 48.4% low and lower-middle income vs 59.0% middle income or higher; study abroad 11.2% vs 16.4%; job 58.6% vs 46.8%). Despite these differences in demographic characteristics and college experiences depending on parental educational attainment and family income, our dataset indicates that the definition does not change the statistical significance when comparing between first-generation students and students who were continuing-generation by any definition (RQ2). First-generation and low-income statuses are often used as proxies for one another, and in this dataset, are highly correlated. However, there are unique patterns at the intersection of these two identities. For the purpose of our RQ3 analysis, we define ‘first-generation’ as students whose parents earned less than a bachelor’s degree and ‘low-income’ as low or lower-middle income. In this sample, 68 percent of students were neither FG nor LI while 11 percent were both (FG&LI). On no measure of demographics or college experience is the FG&LI group statistically similar to the advantaged group. Low-income students had the highest participation in working to pay for college, regardless of parental education, while first-generation students had the lower internship participation than low-income students. Furthermore, being FG&LI is associated with lower ETSE compared with all other groups. These results suggest that care is required when applying the labels “first-generation” and/or “low-income” when considering these groups in developing institutional support programs, in engineering education research, and in educational policy. Moreover, by considering first-generation and low-income students with an intersectional lens, we gain deeper insight into engineering student populations that may reveal potential opportunities and barriers to educational resources and experiences that are an important part of preparation for an engineering career. 

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