Socialized into Knowing: Gender Differences in Children's Knowledge of Parental Backgrounds as Early Relational Labor Across 45 Nations

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Lansford This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8541982/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The preservation and transmission of family knowledge is gendered work. Drawing on feminist frameworks of kinkeeping and relational labor, this study investigates how gendered socialization shapes children's knowledge of their parents' educational attainment and country of birth. We tested two hypotheses: (1) the Gendered Socialization Hypothesis, positing that girls—socialized into relational labor roles from an early age—have more accurate knowledge of parental backgrounds than boys, and (2) the Mother Involvement Hypothesis, suggesting that mothers' greater role as family historians results in children having more accurate knowledge of their mother's than their father's background. Using data from over 200,000 children across 45 national samples from TIMSS, and over 160,000 adolescents across 20 national samples from PISA, we conducted meta-analyses of logistic regression results. Both hypotheses received robust support. Girls demonstrated significantly higher odds of accurate knowledge than boys regarding both parents' education and country of birth (ORs = 1.14–1.32). Children across diverse cultures exhibited greater knowledge of their mothers' backgrounds compared to their fathers' (ORs = 1.13–1.40). Both patterns showed remarkable cross-cultural consistency, with the Gendered Socialization Hypothesis supported in an estimated 89–99% of countries and the Mother Involvement Hypothesis in 77–87%. These findings reveal that the cognitive and relational work of tracking family information is unequally distributed by gender from middle childhood, with implications for understanding how gendered divisions of labor are reproduced across generations. relational labor kinkeeping gender socialization family knowledge cross-cultural meta-analysis Introduction The preservation and transmission of family knowledge is not gender-neutral work. Across the lifespan, women disproportionately perform the cognitive and relational labor of maintaining family connections—remembering birthdays, tracking relatives' lives, and preserving family narratives across generations (Hornstra & Ivanova, 2023 ). This invisible work, termed "kinkeeping," represents a form of gendered labor that, like housework and childcare, is undervalued and unequally distributed (Daminger, 2019 ; Erickson, 2005 ). Although extensive research documents these patterns in adult women, far less attention has been paid to when and how this gendered division of relational labor emerges in development. Understanding the developmental origins of gendered relational labor matters for both theoretical and practical reasons. Theoretically, documenting when girls begin to outperform boys in family knowledge acquisition illuminates the mechanisms through which gender inequality is reproduced across generations. If girls are systematically socialized into tracking family information from childhood, interventions targeting the unequal mental load in adult partnerships may be addressing patterns with deep developmental roots. Practically, children's knowledge of their parents' backgrounds—including education and country of birth—serves as a common proxy measure in large-scale educational research, making systematic gender differences in this knowledge consequential for how we understand educational equity (Jerrim & Micklewright, 2014 ). This study investigates two types of gender differences in children's knowledge of parental backgrounds: whether girls know their parents' backgrounds better than boys (reflecting gendered socialization of children), and whether children know their mothers' backgrounds better than their fathers' (reflecting gendered divisions of parental labor). We draw on data from over 200,000 children across 45 national samples and over 160,000 adolescents across 20 national samples to examine whether these patterns represent near-universal features of gender socialization or culture-specific phenomena. Gendered Socialization and Family Knowledge The theoretical foundation for expecting girls to have greater family knowledge than boys rests on two interconnected literatures: research on gendered patterns in autobiographical memory and family communication, and feminist scholarship on relational labor and kinkeeping. From a developmental perspective, gender differences in family knowledge should arise from corresponding differences in how children engage with family narratives. A substantial body of research demonstrates that girls and boys differ systematically in their participation in family storytelling and their retention of family information (Elias & Brown, 2022 ). These differences emerge early in development and reflect both socialization practices and gendered patterns of relational engagement (Fivush & Kellas, 2025 ). From early childhood, mothers use more elaborative reminiscing styles with daughters than with sons, providing richer contextual detail and encouraging more extended conversations about the past (Fivush et al., 2011b ; Reese & Fivush, 2008 ). This differential socialization creates more opportunities for girls to learn and retain information about family history. Moreover, girls generally engage in more frequent and intimate conversations with their parents compared to boys (Parra et al., 2015 ), conversations that provide natural contexts for transmitting information about parents' backgrounds. Grysman and Hudson ( 2013 ), in their comprehensive review of gender differences in autobiographical memory, explain the mechanism through which these communication differences translate into lasting memory differences. Through elaborative conversations, mothers teach children which details are worthy of attention and should be encoded in memory. When a mother asks, "How did that make you feel?" or "Who else was there?", she signals that emotional and relational information constitutes an essential component of the narrative. For girls, who receive more of this elaborative questioning across thousands of conversations throughout childhood, background information about parents becomes integrated as core knowledge. For boys, who receive less elaborative scaffolding around these details, such information may be encoded as peripheral rather than central. Importantly, this reflects differences in learned priorities rather than cognitive ability. Fivush et al. (2011) demonstrated that adolescent boys could produce highly elaborative narratives when retelling their mothers' stories but used significantly fewer elaborations when narrating their own experiences. This suggests that boys possess the capacity to encode and retrieve family background information but may not view it as sufficiently central to warrant the cognitive effort of retention. From a feminist perspective, these developmental patterns reflect broader processes of socialization into gendered forms of labor. Di Leonardo's (1987) foundational analysis of "kin work" documented how women perform the invisible labor of maintaining family connections—sending cards, organizing gatherings, and preserving family knowledge. Rosenthal ( 1985 ) similarly identified "kinkeeping" as a gendered role within families, with women serving as the primary maintainers of family ties and transmitters of family history. This relational labor, like housework and emotional labor, is often invisible, undervalued, and assumed to flow naturally from women's caring orientation rather than being recognized as work requiring time, attention, and cognitive resources (Strazdins & Broom, 2004 ). Daminger's (2019) more recent analysis of the "cognitive dimension of household labor" is particularly relevant to understanding family knowledge as gendered work. She identifies four components of cognitive labor: anticipating needs, identifying options, deciding among options, and monitoring outcomes. Reich-Stiebert et al.'s ( 2023 ) systematic review of gendered mental labor extends this framework, arguing that the cognitive processes necessary to plan and execute family-related tasks constitute a distinct and underrecognized dimension of domestic work—one that, when ignored, leads to systematic underestimation of gender inequality. Tracking family information—knowing parents' educational histories, remembering their origins, maintaining awareness of family narratives—represents precisely this kind of cognitive work. It requires not passive reception but active inquiry, attentive listening, and intentional retention over time. Corcoran ( 1980 ) was the first to hypothesize that these gendered patterns would manifest in girls having more accurate knowledge of their parents' educational background. Subsequent empirical tests have produced mixed results, though this inconsistency is consistent with relatively small effect sizes requiring large samples to detect. Studies in Germany and the United States found that the odds of correctly reporting parental education were 1.2–1.3 times higher among girls than boys (Kreuter et al., 2010 ; Ridolfo & Maitland, 2011 )—modest but statistically significant effects. However, an early review found inconclusive evidence (Looker, 1989 ), and a study in Scotland found no gender differences (West et al., 2001 ). Notably, statistically significant results have only been observed in Western countries. A similarly sized gender effect in Qatar did not reach statistical significance (Wittrock et al., 2017 ). It thus remains possible that gendered patterns in family knowledge are strongly contingent on cultural context—a critical question given concerns about the generalizability of psychological findings beyond Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations (Henrich et al., 2010 ). Building on this theoretical foundation, we extend Corcoran's original hypothesis beyond educational attainment. The reasoning that gendered socialization leads girls to acquire more family knowledge than boys should apply equally to other aspects of parental background that children cannot directly observe. Parents' country of birth represents a prominent example—information that requires intergenerational communication to be known and that is commonly used as a background variable in research on immigrant families and educational equity (Appels et al., 2024 ; Van Damme & Bellens, 2017 ). We found no prior research examining gender differences in children's knowledge of parents' country of birth, representing a significant gap. Thus, we propose the Gendered Socialization Hypothesis: Reflecting gendered socialization into relational labor and kinkeeping roles, girls have more accurate knowledge of their parents' backgrounds—including both educational attainment and country of birth—than boys. Differential Parental Involvement We turn now to a complementary question: do children know their mothers' backgrounds better than their fathers'? This question shifts focus from the gender of the child to the gender of the parent, examining how the gendered division of parental labor shapes what children know. Research consistently documents that mothers are more involved than fathers in the transmission of family narratives. Mothers serve as the primary "family historians," contributing more factual and emotional elaborations to family conversations and more frequently telling children stories focused on family relationships (Fivush et al., 2009 ). Adolescents accordingly recount more detailed narratives about their mothers compared to their fathers (Fivush et al., 2011a ). This pattern reflects broader differences in how mothers and fathers interact with their children. Research on parental time use reveals that mothers spend substantially more time in conversational interaction with children compared to fathers (Craig & Mullan, 2011 ). Whereas fathers' involvement often centers on shared activities and play, mothers' interactions more frequently involve the kind of verbal communication through which background information about education and origins would naturally be transmitted. Mothers often serve as the "default parent" for day-to-day conversations about family matters, history, and relationships. From a feminist perspective, mothers' role as family historians represents another dimension of the gendered division of domestic labor. Just as women perform disproportionate shares of housework and childcare, they also perform disproportionate shares of the narrative work that maintains family identity and transmits family knowledge across generations (Hornstra & Ivanova, 2023 ). This "matrilineal bias" in family knowledge transmission means that children of both genders may develop more detailed and accurate knowledge of their mothers' backgrounds compared to their fathers'. The empirical literature shows mixed results. Looker's (1989) early review concluded that fathers' educational attainment tended to be reported more accurately than mothers'. Some more recent studies echo this pattern (Kreuter et al., 2010 ; Wagmiller, 2009 ), whereas others find equal or greater accuracy for mothers' education (Ensminger et al., 2000 ; Hovestadt & Schneider, 2021 ). Nothing is known regarding children's knowledge of mothers' versus fathers' country of birth. We propose the Mother Involvement Hypothesis: Due to mothers' greater involvement in family storytelling and conversational interaction with children, children have more accurate knowledge of their mother's background—including both educational attainment and country of birth—than of their father's background. Cultural Variability and the Present Study A critical limitation of prior research is its near-exclusive focus on Western contexts. Gender roles, family communication patterns, and the division of parental labor vary substantially across cultures. It remains unknown whether the gendered patterns in family knowledge documented in Western samples represent universal features of gender socialization or culture-specific phenomena. This question has theoretical significance for feminist scholarship on relational labor. If gendered patterns in family knowledge are consistent across diverse cultural contexts—including societies with varying levels of gender equality, different family structures, and distinct cultural values—this would suggest that the socialization of children into gendered roles may be more resistant to cultural variation than often assumed. Alternatively, substantial cultural heterogeneity would indicate that these patterns are malleable and potentially amenable to intervention. The present study addresses these questions using data from two international large-scale assessments: the Trends in International Mathematics and Science Study (TIMSS) and the Programme for International Student Assessment (PISA). Our analysis encompasses 45 national samples for parents' country of birth (TIMSS 2015, with over 200,000 students aged approximately 10 years) and 20 national samples for parents' education (PISA 2006 and 2009, with over 160,000 students aged 15 years). This design offers several advantages. First, the unprecedented scale allows detection of effects that may be small in magnitude but systematic in direction—precisely the pattern suggested by prior mixed findings. Second, the cultural diversity enables rigorous testing of cross-cultural consistency. Third, the inclusion of two age groups (10 and 15 years) allows examination of whether gendered patterns are evident across different developmental periods, though we note that the datasets also differ in the background variable assessed, precluding strong developmental conclusions. In select waves of these assessments, comparable questions about parental background were posed to both students and their parents. Under the assumption that parents' own reports are largely accurate, comparing student and parent reports allows estimation of students' knowledge accuracy. To test our hypotheses, we compare knowledge among girls and boys in each national sample, then perform meta-analyses to obtain pooled effect sizes and estimates of cross-cultural heterogeneity. Methods This study only analyzes secondary, publicly available data. Therefore, no ethics approval or consent was required. This study was not preregistered. LLMs (Claude Opus 4.5 and Gemini 2.5 Pro) were used to identify relevant literature in feminist scholarship on relational labor. Student Samples To obtain representative samples, PISA and TIMSS use different two-stage sampling strategies. PISA first samples schools and then students within these schools. TIMSS first samples schools and then entire classes within these schools. Both assessments provide sampling weights and replicate weights to account for this complex sampling design in analyses, as described in the official reports (Martin et al., 2016 ; OECD, 2009 ). PISA samples are generally 15 years old, whereas TIMSS samples are generally 10 years old. Countries Parent- and student-reported data on parents’ country of birth from 2015 TIMSS are available from 45 national samples, including Northern Ireland and Hong Kong, see Table 1 (first three columns). The Belgian sample is only from the Flemish part. Note that there are two Norwegian samples, one from grade 4 and one from grade 5. Parent- and student-reported data on parents’ education from the 2006 and 2009 waves of PISA are available from 20 national samples, including Hong Kong and Macao, see Table 1 (last column). Table 1 National samples used in this study. TIMSS data PISA data Armenia 4,650 Italy 4,109 Spain 6,955 Bulgaria 3,956 Australia 2,781 Japan 4,299 Sweden 3,602 Chile 4,913 Bahrain 3,823 Kazakhstan 4,700 Taiwan 4,274 Colombia 3,357 Belgium 4,997 Kuwait 2,913 Turkey 6,348 Croatia 8,963 Bulgaria 4,181 Lithuania 3,931 UAE 19,050 Denmark 5,874 Canada 9,631 Morocco 4,838 Germany 6,513 Chile 3,940 New Zealand 3,528 Hong Kong 7,566 Croatia 3,936 N. Ireland 1,849 Hungary 4,180 Cyprus 3,921 Norway gr 4 1,877 Iceland 2,270 Czechia 5,000 Norway gr 5 1,888 Italy 42,372 Denmark 3,279 Oman 8,587 Lithuania 4,299 Finland 4,767 Portugal 4,605 Luxembourg 2,819 France 4,273 Qatar 4,187 Macao 10,245 Georgia 3,840 Russia 4,882 New Zealand 6,341 Germany 2,470 Saudi Arabia 4,195 Panama 2,995 Hong Kong 3,470 Serbia 3,943 Poland 9,811 Hungary 4,830 Singapore 6,404 Portugal 8,666 Indonesia 3,917 Slovakia 5,616 Qatar 9,547 Iran 3,789 Slovenia 2,834 South Korea 9,605 Ireland 4,066 South Korea 4,648 Turkey 4,162 Note. The sample sizes in the table are the number of student participants with parent-reported data on parental background. Italy used PISA to study regional differences and therefore used an unusually large sample size. Measures Students’ Knowledge of Parents’ Country of Birth (TIMSS Data) The TIMSS questionnaires asked students and parents whether the mother and the father were born in the country. Possible responses are yes and no; students additionally have the response option ‘I don’t know.’ 205,815 students have parent-reported data on the father’s country of birth and 205,666 students have parent-reported data on the mother’s country of birth. Students’ knowledge of a parent’s country of birth is coded 1 if their answer coincided with the parent-reported answer and 0 if they gave the wrong answer or answered that they don’t know. (We exclude missing values, which were slightly more common among boys than girls, 3.0% vs. 2.5%.) Students’ Knowledge of Parents’ Educational Level (PISA Data) The PISA questionnaires ask for the level of education of each parent using national adaptations of the International Standard Classification of Education scale (OECD, 2009 ). The parent questionnaire only asks about higher qualifications, whereas the student questionnaire also distinguishes between lower levels of schooling. 162,232 students have parent-reported data on the father’s education and 163,740 students have parent-reported data on the mother’s education. Following prior research (Jerrim & Micklewright, 2014 ), we recode the data from students and parents on the same scale: bachelor's degree or higher (coded 3), shorter tertiary education (2), upper secondary education or post-secondary non-tertiary education (1), and any lower level of schooling than upper secondary education (0). If students’ and parents’ data on this scale coincide, students’ knowledge is coded as 1, and coded as 0 if the student gave another response. (We exclude missing values, which were slightly more common among boys than girls, 3.5% vs. 3.0%.) Analytic Strategy Given the dichotomous nature of the dependent variables (correct vs. incorrect knowledge), we aim to estimate the odds ratio of correct responses based on student gender. An odds ratio greater than 1 means that knowledge is more common among girls than boys. For example, if 90% of girls have correct knowledge but only 88% of boys, the odds of knowledge for girls is 0.90/(1-0.90) = 9.00, the odds for boys is 0.88/(1-0.88) = 7.33, and the odds ratio is 9.00/7.33 = 1.23. To estimate the odds ratio we use binary logistic regression of knowledge on gender. This analysis yields a regression coefficient that estimates the log-odds ratio, that is, taking the exponential of the coefficient yields the odds ratio. Analyses of each national sample are performed in SPSS using syntax created by the IDB Analyzer (IEA, 2017), which ensures the appropriate application of weights. Thereby, we obtain correct standard errors for the estimated gender effect (log-odds ratio) in each sample. Based on these effect estimates and their standard errors, we perform a meta-analysis in SPSS to obtain a meta-analytic estimate of the gender effect. This analysis also yields an estimate of heterogeneity, that is, the true variance in gender effect across societies, whereby we can estimate the proportion of countries in which the Gendered Socialization Hypothesis holds. We also analyze whether students have better knowledge of mothers than fathers. To test this we calculate the McNemar odds ratio ( b / c ) in each sample, where b is the number of students who knew their mother’s background but not their father’s, and c is the number of students who knew their father’s background but not their mother’s. Thus, greater knowledge of mothers than fathers corresponds to a McNemar odds ratio greater than one (or, equivalently, a McNemar log-odds ratio greater than zero). The standard error in the McNemar log-odds ratio is given by √(1/ b + 1/ c ). Based on these effect estimates and their standard errors, we perform a meta-analysis of the difference in knowledge of mothers and fathers as above to test the Mother Involvement Hypothesis. Data Availability The raw data are freely available from OECD ( https://www.oecd.org/en/about/programmes/pisa/pisa-data.html ) and IEA ( https://www.iea.nl/studies/iea/timss ). All effect estimates and standard errors derived and analyzed in this study are available in Supplementary Tables 1 and 2. Results Descriptive Statistics We begin with descriptive statistics of the two datasets. In the average sample in the TIMSS dataset, 84.4% of fathers and 82.9% of mothers were born in the country. In the average sample in the PISA dataset, 34.7% of fathers and 35.9% of mothers had no upper secondary education, 37.1% vs. 36.8% had upper secondary education, 10.1% vs. 11.0% had shorter tertiary education, and 18.1% vs. 16.3% had bachelor's degree or higher (according to parent reports). Table 2 shows for girls and boys the mean (SD) proportions of correct answers about their parents' backgrounds across the various national samples. Strikingly, answers are consistently more correct among girls than boys and more correct for mothers than fathers. The standard deviations indicate moderate variability across national samples, with knowledge of parents' education showing somewhat greater cross-national variation than knowledge of country of birth. Table 2 Students' knowledge of parents' backgrounds by gender. Parent Gender Country of Birth % Correct (SD) Education % Correct (SD) Father Girls 89.1 (6.3) 67.6 (10.5) Boys 87.9 (6.9) 63.8 (10.3) Mother Girls 90.3 (6.7) 70.3 (10.7) Boys 88.8 (7.4) 64.7 (11.0) Note. Entries are mean percentages across national samples with standard deviations in parentheses. Correct knowledge means the student's response matched the parent's self-report. Country of birth data are 45 national samples from TIMSS 2015; education data are 20 national samples from PISA 2006 and 2009. Percentages in each sample are reported in Supplementary Tables 1 and 2. Testing the Gendered Socialization Hypothesis To test the differences between girls and boys in the accuracy of their knowledge of their parents’ backgrounds, we ran logistic regressions of accuracy on gender in every sample. See Supplementary Tables 1 and 2 for log-odds ratio estimates with standard errors for each national sample. Meta-analytic estimates of the odds ratio for the gender effect of knowledge for each background variable of each parent are reported in Table 3 (corresponding to the percentage point differences shown in Tables 2 ). Note that the estimated effects of gender on knowledge of parents’ education are in line with the odds ratios of 1.2–1.3 obtained in prior studies in the United States and Germany (Kreuter et al., 2010 ; Ridolfo & Maitland, 2011 ). The estimated effects of gender on knowledge of parents’ country of birth are slightly smaller. However, the confidence intervals have considerable overlap. Table 3 Meta-analytic estimates of odds ratios and heterogeneity for the gender difference in knowledge of parents’ country of birth and education. Country of birth Education Statistic Mother’s Father’s Mother’s Father’s Log-odds ratio 0.18 [0.13, 0.23] 0.13 [0.08, 0.18] 0.28 [0.21, 0.34] 0.19 [0.15, 0.24] Odds ratio 1.20 [1.14, 1.26] 1.14 [1.08, 1.19] 1.32 [1.24, 1.40] 1.21 [1.16, 1.27] τ 2 0.011 0.011 0.015 0.006 Prop. OR > 1 96% 89% 99% 99% Note. 95% confidence intervals are given within brackets. Country of birth data are from 45 national samples from TIMSS 2015; education data are from 20 national samples from PISA 2006 and 2009. The last two rows are measures of heterogeneity: τ 2 is a measure of the variance of the true effect sizes across studies, while “Prop. OR > 1” stands for the estimated percentage of nations in which the true odds ratio is greater than one. The heterogeneity measure τ 2 estimates the true variance in the log-odds ratio across nations. We can use this measure to estimate how common it is that the true log-odds ratio is positive (i.e., the odds ratio is greater than 1). Assuming the true log-odds ratio is normally distributed across nations, the proportion of nations where it is positive is given by 𝜙( M / τ), where M is the meta-analytic log-odds ratio and 𝜙 is the cumulative distribution function for the standard normal distribution. The last row of Table 3 reports the results of using this formula, indicating that the vast majority of countries exhibit true gender differences in the expected direction. In sum, we conclude that the Gendered Socialization Hypothesis holds across two different parental background variables and that it is highly consistent across cultural contexts. Testing the Mother Involvement Hypothesis In each sample, we also calculated the McNemar odds ratios and log-odds ratios for knowledge of mother’s vs. father’s background. See Supplementary Tables 1 and 2 for log-odds ratio estimates with standard errors for each national sample. A meta-analysis of the results obtained in different samples yields the results reported in Table 4 . The meta-analytic odds ratios are 1.40 for country of birth and 1.13 for education, both significantly larger than 1, indicating that students had more knowledge of their mother than their father. From the heterogeneity estimate, we estimate that this finding holds in the vast majority of countries, 87% for country of birth and 77% for education. We conclude that the Mother Involvement Hypothesis was supported for both parental background variables and that its cross-cultural robustness is high. Table 4 Meta-analytic estimates of McNemar odds ratios for the difference in knowledge of mothers’ and fathers’ background (country of birth and education) and their heterogeneity. Statistic Country of birth Education Log-odds ratio 0.34 [0.25, 0.43] 0.12 [0.05, 0.20] Heterogeneity (τ 2 ) 0.088 0.028 Odds ratio 1.40 [1.28, 1.54] 1.13 [1.05, 1.22] Prop. OR > 1 87% 77% Note. 95% confidence intervals are given within brackets. Country of birth data are from 45 national samples from TIMSS 2015; education data are from 20 national samples from PISA 2006 and 2009. The last two rows are measures of heterogeneity: τ 2 is a measure of the variance of the true effect sizes across studies, while “Prop. OR > 1” stands for the estimated percentage of nations in which the true odds ratio is greater than one. Discussion This study investigated gender differences in children's knowledge of their parents' backgrounds from a feminist perspective on relational labor and gendered socialization. Drawing on data from over 360,000 children and adolescents across 45 culturally diverse national samples, our findings provide robust support for two complementary hypotheses. Girls demonstrated significantly more accurate knowledge of both parents' backgrounds compared to boys, and children exhibited greater knowledge of their mothers' backgrounds compared to their fathers'. The absolute magnitude of these differences is modest (typically a few percentage points in accuracy), which is unsurprising given that TIMSS and PISA rely on these proxy reports precisely because most children report accurately. However, the consistency of the pattern across 45 culturally diverse samples indicates it reflects systematic socialization rather than random variation, making even modest differences theoretically meaningful, suggesting they reflect fundamental features of how gender organizes family life and the transmission of family knowledge. Methodological Contributions Our methodological approach differs substantially from prior developmental research on gendered family knowledge. Research documenting elaborative reminiscing and narrative transmission has necessarily relied on intensive coding methods with samples typically ranging from 30–65 participants within single cultural contexts (Fivush et al., 2011; Grysman & Hudson, 2013 ). While this research provides invaluable mechanistic insight into how mothers differentially scaffold family knowledge with daughters versus sons, the small scale and cultural specificity left three critical questions unanswered: (1) Do these socialization differences translate into measurable differences in what children actually know? (2) Are such patterns culturally universal or limited to WEIRD populations? and (3) What is the population-level magnitude of gender differences? Our study addresses these questions through a complementary approach: assessing factual accuracy of parental background knowledge across over 360,000 children in 45 nations. This design reveals that the gendered patterns documented in intensive narrative studies manifest as systematic differences in children's knowledge—with girls showing 14–32% higher odds of accurate knowledge depending on the domain—and that these differences show remarkable cross-cultural consistency. The convergence between narrative studies revealing mechanisms and our large-scale findings establishing generalizability provides compelling evidence that gendered socialization into family knowledge represents a robust cross-cultural phenomenon rather than an artifact of small Western samples. Moreover, our approach offers a novel behavioral indicator of differential parental involvement. Rather than relying solely on self-reported communication patterns or observational coding of family interactions, children's relative accuracy about mothers versus fathers provides an unobtrusive measure of whose background information is more thoroughly transmitted—with our findings suggesting mothers remain the primary family historians even in contemporary diverse societies. The Early Roots of Relational Labor The theoretical significance of these findings lies in what they reveal about the developmental origins of gendered relational labor. Acquiring and retaining family knowledge is not passive; it requires active inquiry, attentive listening during family conversations, and intentional retention over time. That girls consistently perform this cognitive work more thoroughly than boys suggests they are being prepared for the disproportionate relational and cognitive labor documented in adult women's lives (Daminger, 2019 ). Importantly, the gender differences we observe likely reflect differences in socialized priorities rather than cognitive capacity. Grysman and Hudson ( 2013 ) note that boys demonstrate the ability to produce elaborative, detail-rich narratives when the context signals that such detail is important. Fivush et al. (2011) similarly found that adolescent boys could produce highly elaborative narratives when retelling their mothers' stories but used significantly fewer elaborations in their own accounts. Boys' lower accuracy in reporting parents' backgrounds thus likely reflects not an inability to learn this information but rather that they did not encode it as central or worthy of retention. Through elaborative conversations emphasizing relational and background information, girls learn to treat parents' biographical details as fundamental knowledge; boys, receiving less scaffolding around such details, encode them as peripheral. This distinction has implications for intervention. If the gender gap in family knowledge reflected cognitive differences, it might be less amenable to change. But if it reflects socialized priorities, then different socialization practices—including more elaborative reminiscing with sons—could potentially reduce gender differences in relational labor from an early age. Mothers as Family Historians Our findings also supported the Mother Involvement Hypothesis. Children were significantly more likely to know their mothers' backgrounds than their fathers' across two important domains: country of birth and education. This pattern aligns with research documenting mothers' more prominent role in family narrative transmission (Fivush et al., 2009 ) and their greater time in conversational interaction with children (Craig & Mullan, 2011 ). Cross-Cultural Patterns Perhaps the most striking finding is the remarkable consistency of both gendered patterns across different national samples from all inhabited continents, representing diverse cultural values regarding gender equality, individualism-collectivism, and family structure. Our analyses suggested that the Mother Involvement Hypothesis holds in the vast majority of countries (77–87%). The Gendered Socialization Hypothesis held even more universally (89–99%). For feminist scholarship, this near-universality suggests that gendered socialization into family knowledge may be deeply entrenched and resistant to change through cultural shifts. Implications for Research Using Proxy Reports The patterns we document also have methodological consequences for research on gender differences using children’s proxy reports of parental background. Without accounting for systematic gender differences in accuracy, they risk misattributing effects. Observed differences in the effects of parental background on girls and boys, or in the effects of maternal versus paternal education, when based on student reports, may partially reflect differences in children's knowledge rather than true differential effects. Limitations and Future Directions While we interpret our findings through the lens of feminist theory on relational labor, it is important to note that we do not directly test the proposed mechanisms. Future research employing observational methods could more directly examine the mechanisms underlying these patterns. An important direction for such work involves examining whether these patterns vary by race, ethnicity, social class, or family structure within national contexts, as gendered socialization may intersect with other social positions. We also acknowledge that although we use parents' own reports as the accuracy criterion, their reports may not always be perfectly accurate; however, any measurement error in parental reports is expected to attenuate rather than inflate our estimates. Conclusion This study provides compelling evidence that girls know their parents' backgrounds better than boys, and that children know their mothers' backgrounds better than their fathers'. These patterns are remarkably consistent across 45 culturally diverse national samples, suggesting they reflect fundamental features of gendered socialization. Our findings reveal that the cognitive and relational work of tracking family information is unequally distributed by gender from middle childhood. Girls are socialized into kinkeeping roles—maintaining family knowledge, attending to family narratives, performing the invisible labor of knowing—from an early age. Understanding these developmental origins may be essential for efforts to promote gender equality in the division of household labor: if the unequal distribution of relational labor begins in childhood through implicit socialization in everyday family conversations, interventions must address not only adult partnerships but also the parent-child interactions through which gendered priorities are transmitted. Declarations Funding declaration This work received no specific funding. Clinical trial number not applicable. Author Contribution K.E. wrote the paper and performed the analysis. J.L and J.E.L assisted with the literature review and provided critical edits. All authors reviewed the manuscript. Data Availability The raw data are freely available from OECD ( [https://www.oecd.org/en/about/programmes/pisa/pisa-data.html](https:/www.oecd.org/en/about/programmes/pisa/pisa-data.html) ) and IEA ( [https://www.iea.nl/studies/iea/timss](https:/www.iea.nl/studies/iea/timss) ). All effect estimates and standard errors derived and analyzed in this study are available in Supplementary Tables 1 and 2. References Appels, L., De Maeyer, S., & Van Petegem, P. (2024). Re-thinking equity: the need for a multidimensional approach in evaluating educational equity through TIMSS data. Large-scale Assessments in Education , 12 (1), 38. https://doi.org/10.1186/s40536-024-00227-6 Corcoran, M. (1980). Sex differences in measurement error in status attainment models. Sociological Methods & Research , 9 (2), 199–217. https://doi.org/10.1177/004912418000900204 Craig, L., & Mullan, K. (2011). How mothers and fathers share childcare: A cross-national time-use comparison. American Sociological Review , 76 (6), 834–861. https://doi.org/10.1177/0003122411427673 Daminger, A. (2019). The cognitive dimension of household labor. American Sociological Review , 84 (4), 609–633. https://doi.org/10.1177/0003122419859007 Di Leonardo, M. (1987). The female world of cards and holidays: Women, families, and the work of kinship. Signs: Journal of Women in Culture and Society , 12 (3), 440–453. https://doi.org/10.1086/494338 Elias, A., & Brown, A. D. (2022). The role of intergenerational family stories in mental health and wellbeing. Frontiers in Psychology , 13 , 927795. https://doi.org/10.3389/fpsyg.2022.927795 Ensminger, M. E., Forrest, C. B., Riley, A. W., Kang, M., Green, B. F., Starfield, B., & Ryan, S. A. (2000). The validity of measures of socioeconomic status of adolescents. Journal of Adolescent Research , 15 (3), 392–419. https://doi.org/10.1177/0743558400153005 Erickson, R. J. (2005). Why emotion work matters: Sex, gender, and the division of household labor. Journal of Marriage and Family , 67 (2), 337–351. https://doi.org/10.1111/j.0022-2445.2005.00120.x Fivush, R., Bohanek, J. G., & Zaman, W. (2011a). Personal and intergenerational narratives in relation to adolescents' well-being. New Directions for Child and Adolescent Development , 2011 (131), 45–57. https://doi.org/10.1002/cd.288 Fivush, R., Habermas, T., Waters, T. E. A., & Zaman, W. (2011b). The making of autobiographical memory: Intersections of culture, narratives and identity. International Journal of Psychology , 46 (5), 321–345. https://doi.org/10.1080/00207594.2011.596541 Fivush, R., & Kellas, J. K. (2025). Parental and family storytelling across the generations: An interdisciplinary review. Parenting , 25 (2), 103–126. https://doi.org/10.1080/15295192.2025.2450497 Fivush, R., Marin, K., McWilliams, K., & Bohanek, J. G. (2009). Family reminiscing style: Parent gender and emotional focus in relation to child well-being. Journal of Cognition and Development , 10 (3), 210–235. https://doi.org/10.1080/15248370903155866 Grysman, A., & Hudson, J. A. (2013). Gender differences in autobiographical memory: Developmental and methodological considerations. Developmental Review , 33 (3), 239–272. https://doi.org/10.1016/j.dr.2013.07.004 Henrich, J., Heine, S. J., & Norenzayan, A. (2010). The weirdest people in the world? Behavioral and Brain Sciences , 33 (2–3), 61–83. https://doi.org/10.1017/S0140525X0999152X Hornstra, M., & Ivanova, K. (2023). Kinkeeping across families: The central role of mothers and stepmothers in the facilitation of adult intergenerational ties. Sex Roles , 88 (7), 367–382. https://doi.org/10.1007/s11199-023-01352-2 Hovestadt, T., & Schneider, T. (2021). Liefern jugendliche valide informationen zum bildungsstand ihrer eltern in standardisierten erhebungen? Befunde zu schülerinnen und schülern der 9. jahrgangsstufe in Deutschland. Zeitschrift für Erziehungswissenschaft , 24 (3), 715–742. https://doi.org/10.1007/s11618-021-01016-5 Jerrim, J., & Micklewright, J. (2014). Socio-economic gradients in children's cognitive skills: Are cross-country comparisons robust to who reports family background? European Sociological Review , 30 (6), 766–781. https://doi.org/10.1093/esr/jcu072 Kreuter, F., Eckman, S., Maaz, K., & Watermann, R. (2010). Children’s reports of parents’ education level: Does it matter whom you ask and what you ask about? Survey Research Methods , 4 (3), 127–138. https://doi.org/10.18148/srm/2010.v4i3.4283 Looker, E. D. (1989). Accuracy of proxy reports of parental status characteristics. Sociology of Education , 62 (4), 257–276. https://doi.org/10.2307/2112830 Martin, M. O., Mullis, I. V. S., & Hooper, M. (Eds.). (2016). Methods and procedures in TIMSS 2015 . TIMSS & PIRLS International Study Center. OECD (2009). PISA data analysis manual: SPSS (2nd ed.). OECD Publishing. https://doi.org/10.1787/9789264056275-en Parra, A., Oliva, A., & Reina, M. D. C. (2015). Family relationships from adolescence to emerging adulthood: A longitudinal study. Journal of Family Issues , 36 (14), 2002–2020. https://doi.org/10.1177/0192513X13507570 Reese, E., & Fivush, R. (2008). The development of collective remembering. Memory (Hove, England) , 16 (3), 201–212. https://doi.org/10.1080/09658210701806516 Reich-Stiebert, N., Froehlich, L., & Voltmer, J. B. (2023). Gendered mental labor: A systematic literature review on the cognitive dimension of unpaid work within the household and childcare. Sex Roles , 88 (11), 475–494. https://doi.org/10.1007/s11199-023-01362-0 Ridolfo, H., & Maitland, A. (2011). Factors that affect the accuracy of adolescent proxy reporting of parental characteristics: A research note. Journal of Adolescence , 34 (1), 95–103. https://doi.org/10.1016/j.adolescence.2010.01.008 Rosenthal, C. J. (1985). Kinkeeping in the familial division of labor. Journal of Marriage and the Family , 47 (4), 965–974. https://doi.org/10.2307/352340 Strazdins, L., & Broom, D. H. (2004). Acts of love (and work): Gender imbalance in emotional work and women's psychological distress. Journal of Family Issues , 25 (3), 356–378. https://doi.org/10.1177/0192513X03257413 Van Damme, J., & Bellens, K. (2017). Countries strive towards more quality and equity in education: Do they show success or failure? Evidence from TIMSS 2003 and 2011, for Grade 4. Cognitive abilities and educational outcomes: a festschrift in honour of Jan-Eric Gustafsson (pp. 127–148). Springer. https://doi.org/10.1007/978-3-319-43473-5_7 . International Publishing. Wagmiller, R. L. Jr. (2009). A fixed effects approach to assessing bias in proxy reports. International Journal of Public Opinion Research , 21 (4), 477–505. https://doi.org/10.1093/ijpor/edp035 West, P., Sweeting, H., & Speed, E. (2001). We really do know what you do: A comparison of reports from 11 year olds and their parents in respect of parental economic activity and occupation. Sociology , 35 (2), 539–559. https://doi.org/10.1177/S0038038501000268 Wittrock, J., Kimmel, L., Hunscher, B., & Le, K. T. (2017). Proxy reporting in education surveys: Factors influencing accurate reporting in the 2012 Qatar Education Study. International Journal of Social Research Methodology , 20 (6), 737–748. https://doi.org/10.1080/13645579.2017.1301078 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8541982","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":574822105,"identity":"db1cf7a2-b44a-4948-8333-f65243df5315","order_by":0,"name":"Kimmo Eriksson","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYBACCWYog40HTNkwMBwgUUsaEVrgLIiWw4S1SLZzp274wGCTz8dz/PGHjzvOR/PdSGD+8AGPFmlm3m03ZzCkWbbx9phJzjxzO3fmjQQ2yRl4tMgBtdzmYThswMbPw8bM23Y7dwNQCzMPYS3/gVrYH3/mbTsH0sL8+Q8BhwG1HDBg420wkOZtOwDSwiCN1/vNIL8YJBuw8ZwB+qUtOXfmmYdtkj14tEicP7vtxocKOwP5nnRgiLXZ5fYdTz784Qc+a8DAAIXH2EBQwygYBaNgFIwC/AAARWFLw2FzP2QAAAAASUVORK5CYII=","orcid":"","institution":"Mälardalen University","correspondingAuthor":true,"prefix":"","firstName":"Kimmo","middleName":"","lastName":"Eriksson","suffix":""},{"id":574822106,"identity":"12531c7c-dd46-40df-8b2c-226aacb35248","order_by":1,"name":"Jannika Lindvall","email":"","orcid":"","institution":"Mälardalen University","correspondingAuthor":false,"prefix":"","firstName":"Jannika","middleName":"","lastName":"Lindvall","suffix":""},{"id":574822107,"identity":"665bfe45-bc9d-44cc-a3ff-f180d9497e2c","order_by":2,"name":"Jennifer E. 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Across the lifespan, women disproportionately perform the cognitive and relational labor of maintaining family connections\u0026mdash;remembering birthdays, tracking relatives' lives, and preserving family narratives across generations (Hornstra \u0026amp; Ivanova, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This invisible work, termed \"kinkeeping,\" represents a form of gendered labor that, like housework and childcare, is undervalued and unequally distributed (Daminger, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Erickson, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Although extensive research documents these patterns in adult women, far less attention has been paid to when and how this gendered division of relational labor emerges in development.\u003c/p\u003e \u003cp\u003eUnderstanding the developmental origins of gendered relational labor matters for both theoretical and practical reasons. Theoretically, documenting when girls begin to outperform boys in family knowledge acquisition illuminates the mechanisms through which gender inequality is reproduced across generations. If girls are systematically socialized into tracking family information from childhood, interventions targeting the unequal mental load in adult partnerships may be addressing patterns with deep developmental roots. Practically, children's knowledge of their parents' backgrounds\u0026mdash;including education and country of birth\u0026mdash;serves as a common proxy measure in large-scale educational research, making systematic gender differences in this knowledge consequential for how we understand educational equity (Jerrim \u0026amp; Micklewright, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study investigates two types of gender differences in children's knowledge of parental backgrounds: whether girls know their parents' backgrounds better than boys (reflecting gendered socialization of children), and whether children know their mothers' backgrounds better than their fathers' (reflecting gendered divisions of parental labor). We draw on data from over 200,000 children across 45 national samples and over 160,000 adolescents across 20 national samples to examine whether these patterns represent near-universal features of gender socialization or culture-specific phenomena.\u003c/p\u003e\n\u003ch3\u003eGendered Socialization and Family Knowledge\u003c/h3\u003e\n\u003cp\u003eThe theoretical foundation for expecting girls to have greater family knowledge than boys rests on two interconnected literatures: research on gendered patterns in autobiographical memory and family communication, and feminist scholarship on relational labor and kinkeeping.\u003c/p\u003e \u003cp\u003eFrom a developmental perspective, gender differences in family knowledge should arise from corresponding differences in how children engage with family narratives. A substantial body of research demonstrates that girls and boys differ systematically in their participation in family storytelling and their retention of family information (Elias \u0026amp; Brown, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These differences emerge early in development and reflect both socialization practices and gendered patterns of relational engagement (Fivush \u0026amp; Kellas, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom early childhood, mothers use more elaborative reminiscing styles with daughters than with sons, providing richer contextual detail and encouraging more extended conversations about the past (Fivush et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011b\u003c/span\u003e; Reese \u0026amp; Fivush, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). This differential socialization creates more opportunities for girls to learn and retain information about family history. Moreover, girls generally engage in more frequent and intimate conversations with their parents compared to boys (Parra et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), conversations that provide natural contexts for transmitting information about parents' backgrounds.\u003c/p\u003e \u003cp\u003eGrysman and Hudson (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), in their comprehensive review of gender differences in autobiographical memory, explain the mechanism through which these communication differences translate into lasting memory differences. Through elaborative conversations, mothers teach children which details are worthy of attention and should be encoded in memory. When a mother asks, \"How did that make you feel?\" or \"Who else was there?\", she signals that emotional and relational information constitutes an essential component of the narrative. For girls, who receive more of this elaborative questioning across thousands of conversations throughout childhood, background information about parents becomes integrated as core knowledge. For boys, who receive less elaborative scaffolding around these details, such information may be encoded as peripheral rather than central.\u003c/p\u003e \u003cp\u003eImportantly, this reflects differences in learned priorities rather than cognitive ability. Fivush et al. (2011) demonstrated that adolescent boys could produce highly elaborative narratives when retelling their mothers' stories but used significantly fewer elaborations when narrating their own experiences. This suggests that boys possess the capacity to encode and retrieve family background information but may not view it as sufficiently central to warrant the cognitive effort of retention.\u003c/p\u003e \u003cp\u003eFrom a feminist perspective, these developmental patterns reflect broader processes of socialization into gendered forms of labor. Di Leonardo's (1987) foundational analysis of \"kin work\" documented how women perform the invisible labor of maintaining family connections\u0026mdash;sending cards, organizing gatherings, and preserving family knowledge. Rosenthal (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1985\u003c/span\u003e) similarly identified \"kinkeeping\" as a gendered role within families, with women serving as the primary maintainers of family ties and transmitters of family history. This relational labor, like housework and emotional labor, is often invisible, undervalued, and assumed to flow naturally from women's caring orientation rather than being recognized as work requiring time, attention, and cognitive resources (Strazdins \u0026amp; Broom, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDaminger's (2019) more recent analysis of the \"cognitive dimension of household labor\" is particularly relevant to understanding family knowledge as gendered work. She identifies four components of cognitive labor: anticipating needs, identifying options, deciding among options, and monitoring outcomes. Reich-Stiebert et al.'s (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) systematic review of gendered mental labor extends this framework, arguing that the cognitive processes necessary to plan and execute family-related tasks constitute a distinct and underrecognized dimension of domestic work\u0026mdash;one that, when ignored, leads to systematic underestimation of gender inequality. Tracking family information\u0026mdash;knowing parents' educational histories, remembering their origins, maintaining awareness of family narratives\u0026mdash;represents precisely this kind of cognitive work. It requires not passive reception but active inquiry, attentive listening, and intentional retention over time.\u003c/p\u003e \u003cp\u003eCorcoran (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) was the first to hypothesize that these gendered patterns would manifest in girls having more accurate knowledge of their parents' educational background. Subsequent empirical tests have produced mixed results, though this inconsistency is consistent with relatively small effect sizes requiring large samples to detect. Studies in Germany and the United States found that the odds of correctly reporting parental education were 1.2\u0026ndash;1.3 times higher among girls than boys (Kreuter et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ridolfo \u0026amp; Maitland, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)\u0026mdash;modest but statistically significant effects. However, an early review found inconclusive evidence (Looker, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1989\u003c/span\u003e), and a study in Scotland found no gender differences (West et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNotably, statistically significant results have only been observed in Western countries. A similarly sized gender effect in Qatar did not reach statistical significance (Wittrock et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It thus remains possible that gendered patterns in family knowledge are strongly contingent on cultural context\u0026mdash;a critical question given concerns about the generalizability of psychological findings beyond Western, Educated, Industrialized, Rich, and Democratic (WEIRD) populations (Henrich et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBuilding on this theoretical foundation, we extend Corcoran's original hypothesis beyond educational attainment. The reasoning that gendered socialization leads girls to acquire more family knowledge than boys should apply equally to other aspects of parental background that children cannot directly observe. Parents' country of birth represents a prominent example\u0026mdash;information that requires intergenerational communication to be known and that is commonly used as a background variable in research on immigrant families and educational equity (Appels et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Van Damme \u0026amp; Bellens, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). We found no prior research examining gender differences in children's knowledge of parents' country of birth, representing a significant gap.\u003c/p\u003e \u003cp\u003eThus, we propose the Gendered Socialization Hypothesis: \u003cem\u003eReflecting gendered socialization into relational labor and kinkeeping roles, girls have more accurate knowledge of their parents' backgrounds\u0026mdash;including both educational attainment and country of birth\u0026mdash;than boys.\u003c/em\u003e\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Parental Involvement\u003c/h2\u003e \u003cp\u003eWe turn now to a complementary question: do children know their mothers' backgrounds better than their fathers'? This question shifts focus from the gender of the child to the gender of the parent, examining how the gendered division of parental labor shapes what children know.\u003c/p\u003e \u003cp\u003eResearch consistently documents that mothers are more involved than fathers in the transmission of family narratives. Mothers serve as the primary \"family historians,\" contributing more factual and emotional elaborations to family conversations and more frequently telling children stories focused on family relationships (Fivush et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Adolescents accordingly recount more detailed narratives about their mothers compared to their fathers (Fivush et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis pattern reflects broader differences in how mothers and fathers interact with their children. Research on parental time use reveals that mothers spend substantially more time in conversational interaction with children compared to fathers (Craig \u0026amp; Mullan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Whereas fathers' involvement often centers on shared activities and play, mothers' interactions more frequently involve the kind of verbal communication through which background information about education and origins would naturally be transmitted. Mothers often serve as the \"default parent\" for day-to-day conversations about family matters, history, and relationships.\u003c/p\u003e \u003cp\u003eFrom a feminist perspective, mothers' role as family historians represents another dimension of the gendered division of domestic labor. Just as women perform disproportionate shares of housework and childcare, they also perform disproportionate shares of the narrative work that maintains family identity and transmits family knowledge across generations (Hornstra \u0026amp; Ivanova, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This \"matrilineal bias\" in family knowledge transmission means that children of both genders may develop more detailed and accurate knowledge of their mothers' backgrounds compared to their fathers'.\u003c/p\u003e \u003cp\u003eThe empirical literature shows mixed results. Looker's (1989) early review concluded that fathers' educational attainment tended to be reported more accurately than mothers'. Some more recent studies echo this pattern (Kreuter et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Wagmiller, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), whereas others find equal or greater accuracy for mothers' education (Ensminger et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; Hovestadt \u0026amp; Schneider, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Nothing is known regarding children's knowledge of mothers' versus fathers' country of birth.\u003c/p\u003e \u003cp\u003eWe propose the Mother Involvement Hypothesis: \u003cem\u003eDue to mothers' greater involvement in family storytelling and conversational interaction with children, children have more accurate knowledge of their mother's background\u0026mdash;including both educational attainment and country of birth\u0026mdash;than of their father's background.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCultural Variability and the Present Study\u003c/h3\u003e\n\u003cp\u003eA critical limitation of prior research is its near-exclusive focus on Western contexts. Gender roles, family communication patterns, and the division of parental labor vary substantially across cultures. It remains unknown whether the gendered patterns in family knowledge documented in Western samples represent universal features of gender socialization or culture-specific phenomena.\u003c/p\u003e \u003cp\u003eThis question has theoretical significance for feminist scholarship on relational labor. If gendered patterns in family knowledge are consistent across diverse cultural contexts\u0026mdash;including societies with varying levels of gender equality, different family structures, and distinct cultural values\u0026mdash;this would suggest that the socialization of children into gendered roles may be more resistant to cultural variation than often assumed. Alternatively, substantial cultural heterogeneity would indicate that these patterns are malleable and potentially amenable to intervention.\u003c/p\u003e \u003cp\u003eThe present study addresses these questions using data from two international large-scale assessments: the Trends in International Mathematics and Science Study (TIMSS) and the Programme for International Student Assessment (PISA). Our analysis encompasses 45 national samples for parents' country of birth (TIMSS 2015, with over 200,000 students aged approximately 10 years) and 20 national samples for parents' education (PISA 2006 and 2009, with over 160,000 students aged 15 years).\u003c/p\u003e \u003cp\u003eThis design offers several advantages. First, the unprecedented scale allows detection of effects that may be small in magnitude but systematic in direction\u0026mdash;precisely the pattern suggested by prior mixed findings. Second, the cultural diversity enables rigorous testing of cross-cultural consistency. Third, the inclusion of two age groups (10 and 15 years) allows examination of whether gendered patterns are evident across different developmental periods, though we note that the datasets also differ in the background variable assessed, precluding strong developmental conclusions.\u003c/p\u003e \u003cp\u003eIn select waves of these assessments, comparable questions about parental background were posed to both students and their parents. Under the assumption that parents' own reports are largely accurate, comparing student and parent reports allows estimation of students' knowledge accuracy. To test our hypotheses, we compare knowledge among girls and boys in each national sample, then perform meta-analyses to obtain pooled effect sizes and estimates of cross-cultural heterogeneity.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study only analyzes secondary, publicly available data. Therefore, no ethics approval or consent was required. This study was not preregistered. LLMs (Claude Opus 4.5 and Gemini 2.5 Pro) were used to identify relevant literature in feminist scholarship on relational labor.\u003c/p\u003e\n\u003ch3\u003eStudent Samples\u003c/h3\u003e\n\u003cp\u003eTo obtain representative samples, PISA and TIMSS use different two-stage sampling strategies. PISA first samples schools and then students within these schools. TIMSS first samples schools and then entire classes within these schools. Both assessments provide sampling weights and replicate weights to account for this complex sampling design in analyses, as described in the official reports (Martin et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; OECD, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). PISA samples are generally 15 years old, whereas TIMSS samples are generally 10 years old.\u003c/p\u003e\n\u003ch3\u003eCountries\u003c/h3\u003e\n\u003cp\u003eParent- and student-reported data on parents\u0026rsquo; country of birth from 2015 TIMSS are available from 45 national samples, including Northern Ireland and Hong Kong, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (first three columns). The Belgian sample is only from the Flemish part. Note that there are two Norwegian samples, one from grade 4 and one from grade 5.\u003c/p\u003e \u003cp\u003eParent- and student-reported data on parents\u0026rsquo; education from the 2006 and 2009 waves of PISA are available from 20 national samples, including Hong Kong and Macao, see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (last column).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNational samples used in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eTIMSS data\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003ePISA data\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArmenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,781\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJapan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSweden\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3,602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eChile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,913\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBahrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKazakhstan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTaiwan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4,274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eColombia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3,357\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelgium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKuwait\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6,348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eCroatia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8,963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLithuania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUAE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19,050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5,874\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9,631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMorocco\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6,513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHong Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7,566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCroatia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN. Ireland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHungary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,180\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyprus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway gr 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIceland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2,270\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCzechia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNorway gr 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eItaly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e42,372\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,279\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8,587\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLithuania\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,299\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLuxembourg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2,819\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQatar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMacao\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10,245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeorgia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRussia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNew Zealand\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6,341\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGermany\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaudi Arabia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePanama\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2,995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHong Kong\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSerbia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,943\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePoland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9,811\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHungary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSingapore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6,404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8,666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndonesia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlovakia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5,616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQatar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9,547\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSlovenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2,834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9,605\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIreland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSouth Korea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4,648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4,162\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. The sample sizes in the table are the number of student participants with parent-reported data on parental background. Italy used PISA to study regional differences and therefore used an unusually large sample size.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eStudents\u0026rsquo; Knowledge of Parents\u0026rsquo; Country of Birth (TIMSS Data)\u003c/h2\u003e \u003cp\u003eThe TIMSS questionnaires asked students and parents whether the mother and the father were born in the country. Possible responses are yes and no; students additionally have the response option \u0026lsquo;I don\u0026rsquo;t know.\u0026rsquo; 205,815 students have parent-reported data on the father\u0026rsquo;s country of birth and 205,666 students have parent-reported data on the mother\u0026rsquo;s country of birth. Students\u0026rsquo; knowledge of a parent\u0026rsquo;s country of birth is coded 1 if their answer coincided with the parent-reported answer and 0 if they gave the wrong answer or answered that they don\u0026rsquo;t know. (We exclude missing values, which were slightly more common among boys than girls, 3.0% vs. 2.5%.)\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003eStudents’ Knowledge of Parents’ Educational Level (PISA Data)\u003c/h3\u003e\n\u003cp\u003eThe PISA questionnaires ask for the level of education of each parent using national adaptations of the International Standard Classification of Education scale (OECD, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The parent questionnaire only asks about higher qualifications, whereas the student questionnaire also distinguishes between lower levels of schooling. 162,232 students have parent-reported data on the father\u0026rsquo;s education and 163,740 students have parent-reported data on the mother\u0026rsquo;s education. Following prior research (Jerrim \u0026amp; Micklewright, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), we recode the data from students and parents on the same scale: bachelor's degree or higher (coded 3), shorter tertiary education (2), upper secondary education or post-secondary non-tertiary education (1), and any lower level of schooling than upper secondary education (0). If students\u0026rsquo; and parents\u0026rsquo; data on this scale coincide, students\u0026rsquo; knowledge is coded as 1, and coded as 0 if the student gave another response. (We exclude missing values, which were slightly more common among boys than girls, 3.5% vs. 3.0%.)\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAnalytic Strategy\u003c/h2\u003e \u003cp\u003eGiven the dichotomous nature of the dependent variables (correct vs. incorrect knowledge), we aim to estimate the odds ratio of correct responses based on student gender. An odds ratio greater than 1 means that knowledge is more common among girls than boys. For example, if 90% of girls have correct knowledge but only 88% of boys, the odds of knowledge for girls is 0.90/(1-0.90)\u0026thinsp;=\u0026thinsp;9.00, the odds for boys is 0.88/(1-0.88)\u0026thinsp;=\u0026thinsp;7.33, and the odds ratio is 9.00/7.33\u0026thinsp;=\u0026thinsp;1.23. To estimate the odds ratio we use binary logistic regression of knowledge on gender. This analysis yields a regression coefficient that estimates the log-odds ratio, that is, taking the exponential of the coefficient yields the odds ratio.\u003c/p\u003e \u003cp\u003eAnalyses of each national sample are performed in SPSS using syntax created by the IDB Analyzer (IEA, 2017), which ensures the appropriate application of weights. Thereby, we obtain correct standard errors for the estimated gender effect (log-odds ratio) in each sample. Based on these effect estimates and their standard errors, we perform a meta-analysis in SPSS to obtain a meta-analytic estimate of the gender effect. This analysis also yields an estimate of heterogeneity, that is, the true variance in gender effect across societies, whereby we can estimate the proportion of countries in which the Gendered Socialization Hypothesis holds.\u003c/p\u003e \u003cp\u003eWe also analyze whether students have better knowledge of mothers than fathers. To test this we calculate the McNemar odds ratio (\u003cem\u003eb\u003c/em\u003e/\u003cem\u003ec\u003c/em\u003e) in each sample, where \u003cem\u003eb\u003c/em\u003e is the number of students who knew their mother\u0026rsquo;s background but not their father\u0026rsquo;s, and \u003cem\u003ec\u003c/em\u003e is the number of students who knew their father\u0026rsquo;s background but not their mother\u0026rsquo;s. Thus, greater knowledge of mothers than fathers corresponds to a McNemar odds ratio greater than one (or, equivalently, a McNemar log-odds ratio greater than zero). The standard error in the McNemar log-odds ratio is given by \u0026radic;(1/\u003cem\u003eb\u003c/em\u003e\u0026thinsp;+\u0026thinsp;1/\u003cem\u003ec\u003c/em\u003e). Based on these effect estimates and their standard errors, we perform a meta-analysis of the difference in knowledge of mothers and fathers as above to test the Mother Involvement Hypothesis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe raw data are freely available from OECD (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.oecd.org/en/about/programmes/pisa/pisa-data.html\u003c/span\u003e\u003cspan address=\"https://www.oecd.org/en/about/programmes/pisa/pisa-data.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and IEA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.iea.nl/studies/iea/timss\u003c/span\u003e\u003cspan address=\"https://www.iea.nl/studies/iea/timss\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). All effect estimates and standard errors derived and analyzed in this study are available in Supplementary Tables\u0026nbsp;1 and 2.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eWe begin with descriptive statistics of the two datasets. In the average sample in the TIMSS dataset, 84.4% of fathers and 82.9% of mothers were born in the country. In the average sample in the PISA dataset, 34.7% of fathers and 35.9% of mothers had no upper secondary education, 37.1% vs. 36.8% had upper secondary education, 10.1% vs. 11.0% had shorter tertiary education, and 18.1% vs. 16.3% had bachelor's degree or higher (according to parent reports).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows for girls and boys the mean (SD) proportions of correct answers about their parents' backgrounds across the various national samples. Strikingly, answers are consistently more correct among girls than boys and more correct for mothers than fathers. The standard deviations indicate moderate variability across national samples, with knowledge of parents' education showing somewhat greater cross-national variation than knowledge of country of birth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStudents' knowledge of parents' backgrounds by gender.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry of Birth\u003c/p\u003e \u003cp\u003e% Correct (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003cp\u003e% Correct (SD)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFather\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.1 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67.6 (10.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.9 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.8 (10.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGirls\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e90.3 (6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70.3 (10.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBoys\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e88.8 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e64.7 (11.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e Entries are mean percentages across national samples with standard deviations in parentheses. Correct knowledge means the student's response matched the parent's self-report. Country of birth data are 45 national samples from TIMSS 2015; education data are 20 national samples from PISA 2006 and 2009. Percentages in each sample are reported in Supplementary Tables\u0026nbsp;1 and 2.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eTesting the Gendered Socialization Hypothesis\u003c/h2\u003e \u003cp\u003eTo test the differences between girls and boys in the accuracy of their knowledge of their parents\u0026rsquo; backgrounds, we ran logistic regressions of accuracy on gender in every sample. See Supplementary Tables\u0026nbsp;1 and 2 for log-odds ratio estimates with standard errors for each national sample. Meta-analytic estimates of the odds ratio for the gender effect of knowledge for each background variable of each parent are reported in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (corresponding to the percentage point differences shown in Tables\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Note that the estimated effects of gender on knowledge of parents\u0026rsquo; education are in line with the odds ratios of 1.2\u0026ndash;1.3 obtained in prior studies in the United States and Germany (Kreuter et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ridolfo \u0026amp; Maitland, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The estimated effects of gender on knowledge of parents\u0026rsquo; country of birth are slightly smaller. However, the confidence intervals have considerable overlap.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeta-analytic estimates of odds ratios and heterogeneity for the gender difference in knowledge of parents\u0026rsquo; country of birth and education.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCountry of birth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMother\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFather\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMother\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFather\u0026rsquo;s\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-odds ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18 [0.13, 0.23]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.13 [0.08, 0.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28 [0.21, 0.34]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19 [0.15, 0.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.20 [1.14, 1.26]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.14 [1.08, 1.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.32 [1.24, 1.40]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.21 [1.16, 1.27]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eτ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProp. OR\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e96%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e99%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote. 95% confidence intervals are given within brackets. Country of birth data are from 45 national samples from TIMSS 2015; education data are from 20 national samples from PISA 2006 and 2009. The last two rows are measures of heterogeneity: τ\u003csup\u003e2\u003c/sup\u003e is a measure of the variance of the true effect sizes across studies, while \u0026ldquo;Prop. OR\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026rdquo; stands for the estimated percentage of nations in which the true odds ratio is greater than one.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe heterogeneity measure τ\u003csup\u003e2\u003c/sup\u003e estimates the true variance in the log-odds ratio across nations. We can use this measure to estimate how common it is that the true log-odds ratio is positive (i.e., the odds ratio is greater than 1). Assuming the true log-odds ratio is normally distributed across nations, the proportion of nations where it is positive is given by \u0026#120601;(\u003cem\u003eM\u003c/em\u003e/ τ), where \u003cem\u003eM\u003c/em\u003e is the meta-analytic log-odds ratio and \u0026#120601; is the cumulative distribution function for the standard normal distribution. The last row of Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the results of using this formula, indicating that the vast majority of countries exhibit true gender differences in the expected direction. In sum, we conclude that the Gendered Socialization Hypothesis holds across two different parental background variables and that it is highly consistent across cultural contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eTesting the Mother Involvement Hypothesis\u003c/h2\u003e \u003cp\u003eIn each sample, we also calculated the McNemar odds ratios and log-odds ratios for knowledge of mother\u0026rsquo;s vs. father\u0026rsquo;s background. See Supplementary Tables\u0026nbsp;1 and 2 for log-odds ratio estimates with standard errors for each national sample. A meta-analysis of the results obtained in different samples yields the results reported in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The meta-analytic odds ratios are 1.40 for country of birth and 1.13 for education, both significantly larger than 1, indicating that students had more knowledge of their mother than their father. From the heterogeneity estimate, we estimate that this finding holds in the vast majority of countries, 87% for country of birth and 77% for education. We conclude that the Mother Involvement Hypothesis was supported for both parental background variables and that its cross-cultural robustness is high.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMeta-analytic estimates of McNemar odds ratios for the difference in knowledge of mothers\u0026rsquo; and fathers\u0026rsquo; background (country of birth and education) and their heterogeneity.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCountry of birth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog-odds ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.34 [0.25, 0.43]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.12 [0.05, 0.20]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeterogeneity (τ\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOdds ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.40 [1.28, 1.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.13 [1.05, 1.22]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProp. OR\u0026thinsp;\u0026gt;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNote. 95% confidence intervals are given within brackets. Country of birth data are from 45 national samples from TIMSS 2015; education data are from 20 national samples from PISA 2006 and 2009. The last two rows are measures of heterogeneity: τ\u003csup\u003e2\u003c/sup\u003e is a measure of the variance of the true effect sizes across studies, while \u0026ldquo;Prop. OR\u0026thinsp;\u0026gt;\u0026thinsp;1\u0026rdquo; stands for the estimated percentage of nations in which the true odds ratio is greater than one.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study investigated gender differences in children's knowledge of their parents' backgrounds from a feminist perspective on relational labor and gendered socialization. Drawing on data from over 360,000 children and adolescents across 45 culturally diverse national samples, our findings provide robust support for two complementary hypotheses. Girls demonstrated significantly more accurate knowledge of both parents' backgrounds compared to boys, and children exhibited greater knowledge of their mothers' backgrounds compared to their fathers'. The absolute magnitude of these differences is modest (typically a few percentage points in accuracy), which is unsurprising given that TIMSS and PISA rely on these proxy reports precisely because most children report accurately. However, the consistency of the pattern across 45 culturally diverse samples indicates it reflects systematic socialization rather than random variation, making even modest differences theoretically meaningful, suggesting they reflect fundamental features of how gender organizes family life and the transmission of family knowledge.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMethodological Contributions\u003c/h2\u003e \u003cp\u003eOur methodological approach differs substantially from prior developmental research on gendered family knowledge. Research documenting elaborative reminiscing and narrative transmission has necessarily relied on intensive coding methods with samples typically ranging from 30\u0026ndash;65 participants within single cultural contexts (Fivush et al., 2011; Grysman \u0026amp; Hudson, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While this research provides invaluable mechanistic insight into \u003cem\u003ehow\u003c/em\u003e mothers differentially scaffold family knowledge with daughters versus sons, the small scale and cultural specificity left three critical questions unanswered: (1) Do these socialization differences translate into measurable differences in what children actually know? (2) Are such patterns culturally universal or limited to WEIRD populations? and (3) What is the population-level magnitude of gender differences?\u003c/p\u003e \u003cp\u003eOur study addresses these questions through a complementary approach: assessing factual accuracy of parental background knowledge across over 360,000 children in 45 nations. This design reveals that the gendered patterns documented in intensive narrative studies manifest as systematic differences in children's knowledge\u0026mdash;with girls showing 14\u0026ndash;32% higher odds of accurate knowledge depending on the domain\u0026mdash;and that these differences show remarkable cross-cultural consistency. The convergence between narrative studies revealing mechanisms and our large-scale findings establishing generalizability provides compelling evidence that gendered socialization into family knowledge represents a robust cross-cultural phenomenon rather than an artifact of small Western samples.\u003c/p\u003e \u003cp\u003eMoreover, our approach offers a novel behavioral indicator of differential parental involvement. Rather than relying solely on self-reported communication patterns or observational coding of family interactions, children's relative accuracy about mothers versus fathers provides an unobtrusive measure of whose background information is more thoroughly transmitted\u0026mdash;with our findings suggesting mothers remain the primary family historians even in contemporary diverse societies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe Early Roots of Relational Labor\u003c/h2\u003e \u003cp\u003eThe theoretical significance of these findings lies in what they reveal about the developmental origins of gendered relational labor. Acquiring and retaining family knowledge is not passive; it requires active inquiry, attentive listening during family conversations, and intentional retention over time. That girls consistently perform this cognitive work more thoroughly than boys suggests they are being prepared for the disproportionate relational and cognitive labor documented in adult women's lives (Daminger, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eImportantly, the gender differences we observe likely reflect differences in socialized priorities rather than cognitive capacity. Grysman and Hudson (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) note that boys demonstrate the ability to produce elaborative, detail-rich narratives when the context signals that such detail is important. Fivush et al. (2011) similarly found that adolescent boys could produce highly elaborative narratives when retelling their mothers' stories but used significantly fewer elaborations in their own accounts. Boys' lower accuracy in reporting parents' backgrounds thus likely reflects not an inability to learn this information but rather that they did not encode it as central or worthy of retention. Through elaborative conversations emphasizing relational and background information, girls learn to treat parents' biographical details as fundamental knowledge; boys, receiving less scaffolding around such details, encode them as peripheral.\u003c/p\u003e \u003cp\u003eThis distinction has implications for intervention. If the gender gap in family knowledge reflected cognitive differences, it might be less amenable to change. But if it reflects socialized priorities, then different socialization practices\u0026mdash;including more elaborative reminiscing with sons\u0026mdash;could potentially reduce gender differences in relational labor from an early age.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eMothers as Family Historians\u003c/h2\u003e \u003cp\u003eOur findings also supported the Mother Involvement Hypothesis. Children were significantly more likely to know their mothers' backgrounds than their fathers' across two important domains: country of birth and education. This pattern aligns with research documenting mothers' more prominent role in family narrative transmission (Fivush et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) and their greater time in conversational interaction with children (Craig \u0026amp; Mullan, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eCross-Cultural Patterns\u003c/h2\u003e \u003cp\u003ePerhaps the most striking finding is the remarkable consistency of both gendered patterns across different national samples from all inhabited continents, representing diverse cultural values regarding gender equality, individualism-collectivism, and family structure. Our analyses suggested that the Mother Involvement Hypothesis holds in the vast majority of countries (77\u0026ndash;87%). The Gendered Socialization Hypothesis held even more universally (89\u0026ndash;99%). For feminist scholarship, this near-universality suggests that gendered socialization into family knowledge may be deeply entrenched and resistant to change through cultural shifts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eImplications for Research Using Proxy Reports\u003c/h2\u003e \u003cp\u003eThe patterns we document also have methodological consequences for research on gender differences using children\u0026rsquo;s proxy reports of parental background. Without accounting for systematic gender differences in accuracy, they risk misattributing effects. Observed differences in the effects of parental background on girls and boys, or in the effects of maternal versus paternal education, when based on student reports, may partially reflect differences in children's knowledge rather than true differential effects.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eLimitations and Future Directions\u003c/h2\u003e \u003cp\u003eWhile we interpret our findings through the lens of feminist theory on relational labor, it is important to note that we do not directly test the proposed mechanisms. Future research employing observational methods could more directly examine the mechanisms underlying these patterns. An important direction for such work involves examining whether these patterns vary by race, ethnicity, social class, or family structure within national contexts, as gendered socialization may intersect with other social positions. We also acknowledge that although we use parents' own reports as the accuracy criterion, their reports may not always be perfectly accurate; however, any measurement error in parental reports is expected to attenuate rather than inflate our estimates.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides compelling evidence that girls know their parents' backgrounds better than boys, and that children know their mothers' backgrounds better than their fathers'. These patterns are remarkably consistent across 45 culturally diverse national samples, suggesting they reflect fundamental features of gendered socialization.\u003c/p\u003e \u003cp\u003eOur findings reveal that the cognitive and relational work of tracking family information is unequally distributed by gender from middle childhood. Girls are socialized into kinkeeping roles\u0026mdash;maintaining family knowledge, attending to family narratives, performing the invisible labor of knowing\u0026mdash;from an early age. Understanding these developmental origins may be essential for efforts to promote gender equality in the division of household labor: if the unequal distribution of relational labor begins in childhood through implicit socialization in everyday family conversations, interventions must address not only adult partnerships but also the parent-child interactions through which gendered priorities are transmitted.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eFunding declaration\u003c/strong\u003e \u003cp\u003eThis work received no specific funding.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003enot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eK.E. wrote the paper and performed the analysis. J.L and J.E.L assisted with the literature review and provided critical edits. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data are freely available from OECD ( [https://www.oecd.org/en/about/programmes/pisa/pisa-data.html](https:/www.oecd.org/en/about/programmes/pisa/pisa-data.html) ) and IEA ( [https://www.iea.nl/studies/iea/timss](https:/www.iea.nl/studies/iea/timss) ). All effect estimates and standard errors derived and analyzed in this study are available in Supplementary Tables 1 and 2.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAppels, L., De Maeyer, S., \u0026amp; Van Petegem, P. (2024). 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Proxy reporting in education surveys: Factors influencing accurate reporting in the 2012 Qatar Education Study. \u003cem\u003eInternational Journal of Social Research Methodology\u003c/em\u003e, \u003cem\u003e20\u003c/em\u003e(6), 737\u0026ndash;748. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/13645579.2017.1301078\u003c/span\u003e\u003cspan address=\"10.1080/13645579.2017.1301078\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"relational labor, kinkeeping, gender socialization, family knowledge, cross-cultural, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-8541982/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8541982/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe preservation and transmission of family knowledge is gendered work. Drawing on feminist frameworks of kinkeeping and relational labor, this study investigates how gendered socialization shapes children's knowledge of their parents' educational attainment and country of birth. We tested two hypotheses: (1) the Gendered Socialization Hypothesis, positing that girls\u0026mdash;socialized into relational labor roles from an early age\u0026mdash;have more accurate knowledge of parental backgrounds than boys, and (2) the Mother Involvement Hypothesis, suggesting that mothers' greater role as family historians results in children having more accurate knowledge of their mother's than their father's background. Using data from over 200,000 children across 45 national samples from TIMSS, and over 160,000 adolescents across 20 national samples from PISA, we conducted meta-analyses of logistic regression results. Both hypotheses received robust support. Girls demonstrated significantly higher odds of accurate knowledge than boys regarding both parents' education and country of birth (ORs\u0026thinsp;=\u0026thinsp;1.14\u0026ndash;1.32). Children across diverse cultures exhibited greater knowledge of their mothers' backgrounds compared to their fathers' (ORs\u0026thinsp;=\u0026thinsp;1.13\u0026ndash;1.40). Both patterns showed remarkable cross-cultural consistency, with the Gendered Socialization Hypothesis supported in an estimated 89\u0026ndash;99% of countries and the Mother Involvement Hypothesis in 77\u0026ndash;87%. These findings reveal that the cognitive and relational work of tracking family information is unequally distributed by gender from middle childhood, with implications for understanding how gendered divisions of labor are reproduced across generations.\u003c/p\u003e","manuscriptTitle":"Socialized into Knowing: Gender Differences in Children's Knowledge of Parental Backgrounds as Early Relational Labor Across 45 Nations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 12:50:15","doi":"10.21203/rs.3.rs-8541982/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6fdd2a6b-fb43-47a0-9aae-74d7eeaecb8a","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-07T15:27:09+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-07T15:41:04+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 12:50:15","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8541982","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8541982","identity":"rs-8541982","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}