Racing Places: The Relationship Between Racial Composition in State of Residence With Racial Identification Among a Cohort of People with Only One Asian Indian Parent

Racing Places: The Relationship Between Racial Composition in State of Residence With Racial Identification Among a Cohort of People with Only One Asian Indian Parent | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Racing Places: The Relationship Between Racial Composition in State of Residence With Racial Identification Among a Cohort of People with Only One Asian Indian Parent Luna Chandna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7133247/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 rise in the Multiracial population in the United States contributes to the shift from a majority white population to a majority non-white population, commonly referred to as the browning of America. Population projection models often assume that people identified as Multiracial in childhood will continue to identify as Multiracial in adulthood. However, racial self-identification continues to develop over the lifecourse, and is both fluid and context-dependent (Albuja et al. 2018 ; Liebler et al. 2017 ; Root 1998 ; Waters 2000 ). An important contextual factor to consider when studying the racial identification of people with multiracial backgrounds is the racial composition of where they live. Using restricted data from Census 2000 and Census 2020, I link the racial identification responses in childhood and in adulthood for 2,509 individuals who have only one Asian Indian parent and use logit regression models to study the association between their racial identification and the racial composition in their state of residence. I find that the share of White and the share of Asian population in the state of residence are significant predictors of corresponding identifications for people who have only one Asian Indian parent and who are born between 1990 and 2000. This work uniquely contributes to the literature about the understudied association between racial identification and the racial demographics of place, and has implications for policy makers, population scientists, and the projected browning rate of America. racial identification Multiracial racial composition of state Asian Indian Figures Figure 1 Figure 2 Figure 3 Introduction Population projection models assume that an individual’s racial identification remains stable across their lifecourse (U.S. Census Bureau 2023). However, research shows that approximately 6-7% of the general U.S. population do not have stable racial identifications (Liebler et al. 2017). Additional research shows that the largest share of the 6-7% people who demonstrate instability in their racial identification are Multiracial (Anders et al. 2025). Because there are many multiracial groups, studying the multiracial population is problematic (Harris and Sim 2002). However, focusing on a clearly defined multiracial group to study patterned differences between how parents racially identify their young multiracial children and how those children racially identify themselves in early adulthood can help shed light on how to improve population estimates as well as better understand racialized processes of social assimilation and incorporation. This study examines the relationship of racial composition in the state of residence with patterns of racial identification from childhood to adulthood among people with one Asian Indian parent. For the purposes of this study, people with one Asian Indian parent are defined as people who have one Asian Indian parent and one non-Asian Indian parent, and a non-Asian Indian parent is defined as an individual who exclusively identifies as not Asian Indian. Therefore, non-Asian Indian parents encompass those who identify with either another Asian subgroup or another racial group. For example, children whose parents are Asian Indian and White as well as children whose parents are Asian Indian and Chinese are considered to have one Asian Indian parent in this study. People with two Asian Indian parents are not included in this study. All research was conducted in a Federal Statistical Research Data Center. Background Racial Identity and Racial Identification Racial identity is how one thinks of themselves and racial identification is how one publicly describes themselves (Campbell 2007; Owens, Robinson, and Smith-Lovin 2010; Umaña‐Taylor et al. 2014). While people who identify with only one race often report having the same racial identity and racial identification, Multiracial people can have one racial identity and a different racial identification. Multiracial people report checking racial identification boxes that reflect how they are identified by others, which is often different from their racial identity (Khanna 2004). This difference between racial identity and racial identification is important to understand when thinking about how people with multiracial backgrounds are described by their parents and how they describe themselves on surveys like the decennial Census. Because different parts of a Multiracial individual’s ancestry may feel more salient under different circumstances or at different times (Gonlin 2022; Gullickson and Morning 2011; Herman 2004, 2010), Multiracial individuals may choose to self-identify with different race groups on different surveys (Campbell 2007; Norman and Chen 2020). Fluidity of Ethnoracial Identification While adults likely exercise agency in selecting their ethnoracial identification, young children are almost certain to have their ethnoracial identity assigned to them by the adult filling out the survey on their behalf. Liebler (2017) has definitively shown that people’s ethnoracial self-identification is fluid across time and Anders et al. (2025) shows the highest rates of fluidity occur among people with multiracial backgrounds. To date, little is understood about the mechanisms that influence ethnoracial fluidity. For people with multiracial backgrounds, studying change in their parental-assigned racial identification from childhood to their self-selected racial identification in adulthood can shed light on issues related to social inequality, mobility, and assimilation throughout the lifecourse. Interracial marriage is understood to be a marker of decreasing social distance, indicative of improving racial relations (Qian and Lichter 2007, 2011; Bratter and Campbell 2023). However, improving cannot be understood to mean equal, and many scholars articulate the continued use of race as a foundational and organizing principle of U.S. society (Bonilla-Silva 2018; Feagin 2023). Multiracial Identification and Measurement Until the year 2000, the U.S. Census allowed respondents to check only one race box to indicate their racial identification. This meant that people with multiracial backgrounds could select only one of the races with which they identified. Starting in 2000, the Census has allowed respondents to select multiple race boxes to indicate their racial identification. In the 2000 census, 6.8 million people reported identifying with two or more races (Jones and Smith 2001). This figure increased to 9 million in Census 2010 (Jones and Bullock 2012), and to 33.8 million in 2020 (Jones et al. 2021). The decennial 2020 Census reported a 276% increase in the Multiracial population from 2010 to 2020 but the figure in 2020 is incomparable to figures from prior years due to the use of a different coding methodology. Unlike in previous decennial censuses, the Census Bureau recoded individuals in the decennial 2020 census as Multiracial if they checked only one race box and wrote in an origin response that did not match their race. For example, an individual who checked only the White box for race and wrote in Venezuela for origin was recoded as Multiracial because Venezuela was not coded as a White, European origin. Origins are not the same as either race or identity. Confounding origins, race, and identity contributed to an artificial increase in the Multiracial population (Arias et al. 2025; Starr and Pao 2024; Ventura and Flores 2025). Overall, how the race of Multiracial people is counted in the Census has direct implications for the calculation of the rate at which America is browning. Therefore, being able to understand the mechanisms that influence ethnoracial identification to predict how Multiracial people of various ancestral backgrounds will self-identify in the Census is consequential. Scholars who document higher rates of ethnoracial fluidity among people who initially identified as Asian compared to people who initially identified as White or Black call researchers to focus their attention on Asians (Breen 2023; Liebler et al. 2017). Asians and Intermarriage As the Asian population has risen over the last few decades, the topic of Multiracial Asians has increasingly come into focus in the sociological literature (Burke and Kao 2013; Castillo et al. 2020; Cheng 2009; Cheng and Lively 2009; Kim-Breunig and Vittrup 2022; Lopez and Pantoja 2004; Qian 1997, 2004; Xie and Goyette 1997). Among the panethnic groups, Asians have the second highest rate of intermarriage (28%), compared to 7% for Whites, 19% for Blacks, and 58% for American Indians (Livingston and Brown 2017; Wang 2015). Numerous scholars have noted that the emphasis, largely due to data limitations, on panethnic Asians limits our ability to understand the heterogeneity within this broad grouping and have called for the disaggregated study of Asians. A Closer Look at Multiracial Asian Indians In 1980, 15.5% of Asian Indians in the United States reported having a spouse who is not of Indian origin; this figure was 12.1% in 1990, and 20% in 2000. Of intermarried Asian Indians, 86.8% reported having a White spouse in 1980 and 69.3% reported having a White spouse in 1990 (Lee and Fernandez 1998). From 2010 to 2020, among the six largest Asian subgroups, the alone-only and alone-or-in-combination Asian Indian populations experienced the fastest rate of increase (Rico, Jacobs, and Coritz 2023). These changes in population size are shown in Table 1. This rise, together with the fact that the offspring of unions between Asian Indians and non-Asian Indians are understudied, make the children of intermarried Asian Indians a good choice for this study. I contribute to the literature by disaggregating the Multiracial Asian Indian population from the Multiracial Asian population and longitudinally linking individuals with one Asian Indian parent using restricted data from the 2000 Census, the 2020 Census, and the NUMIDENT to study changes in patterns of their ethnoracial self-identification. Racial Composition in State of Residence The salience of race and ethnicity vary across states. People in different states have different purposes for identifying with a race. In Texas, some people of Latino origin choose to identify as white, regardless of skin color, as a strategy to avoid discrimination, while other people of Latino origin choose to identify as Other as a way to give voice to anti-racist politics (Dowling 2014). In California, politics have been intertwined with race and ethnicity in ways that have created anti- or pro-immigrant sentiments in different times (Cain, Citrin, and Wong 2000). For example, attitudes towards immigrants, both legal and illegal, have been shaped by public policies that either provide or deny services based on immigrant status . Discrimination, politics, and legal issues at the state level may influence how people think about themselves. An under-studied geographic scale for investigating ethnoracial identification, the state is a worthwhile level of geography to study for Asian Indians because more than half of the Asian Indian population in the U.S. resides in one of five states (California, Texas, New Jersey, New York, and Illinois), and Asian Indians are the largest Asian subgroup in only one county in the country (Cook county, IL) (Rico, Hahn, and Spence 2023). The state level is meaningful because people who live in a county with a small number of their group can still attain access to ethnic social networks and other group members who live within driving distance. I posit that Multiracial people with an Asian Indian parent could self-identify or be identified by their parents in ways that vary with state-level racial composition. I test three hypotheses: Hypothesis 1 : The share of Multiracial people in the state of residence has a significant relationship with being identified in childhood, or self-identifying in adulthood, as Multiracial. Hypothesis 2 : The share of Asian people in the state of residence has a significant relationship with being identified in childhood, or self-identifying in adulthood, as Asian. Hypothesis 3 : The share of White people in the state of residence has a significant relationship with being identified in childhood, or self-identifying in adulthood, as White. Data and Methods The data sources I use are the decennial 2000 Census, decennial 2020 Census, and the NUMIDENT. To create a single record for each individual, I use protected identification keys (PIKs) to link individuals across these datasets. In this section, I provide descriptions of each dataset, PIKS, and my methods. Decennial Census The U.S. Census Bureau is required by law to enumerate the population every decade. These decennial censuses include population and housing data, but the data collection is not identical across decades. The questions asking about race and ethnicity on the 2000 and 2020 forms are shown in Figures 1(a) and 1(b), respectively. In both Census 2000 and Census 2020, there was a checkbox for Asian Indian as well as space for a write-in response. I combine the respondents who marked the Asian Indian checkbox with the respondents who were recoded to Asian Indian based on their write-in response, which included responses like “Gujarat.” I use the detailed codes for race, relationship to householder, household type, and parent of own children to correctly identify the sample and its characteristics from 2000 to 2020. NUMIDENT The Social Security Administration has a master file, referred to as the numerical identification system or NUMIDENT, of everyone who has applied for a social security number since 1936. As such, the NUMIDENT contains information such as full name, sex, place and date of birth, and date of death if applicable (National Archives 2018) for everyone who has ever had legal authorization to work in the U.S. Between 2001 and 2015, more than 50% of all H1-B visas were awarded to Asian Indian nationals (Ruiz 2018). This limits the known bias created by linking to the NUMIDENT (Taylor, Stuart, and Bailey 2016) because it is more likely than not that the Asian Indian parent of individuals in my study appear in the NUMIDENT. Protected Identification Key (PIK) The Census Bureau collects information from the decennial censuses, conducts quality control checks, and then replaces sensitive personally identifying information with a Protected Identification Key (PIK). The Census Bureau links PIKs to individual observations via the Person Identification Validation System (PVS), which uses probabilistic matching to assign a unique identifier for each person (Layne, Wagner, and Rothhaas 2014). For censuses and major surveys conducted since 2000, more than 90% of cases have PIKs assigned (Massey and O’Hara 2014) and nearly all assigned PIKs have been shown to be accurate (Alexander and Genadek 2023). Measuring ethnoracial identity is challenging because of its multiple dimensions (Roth 2016), which are frequently inconsistent among groups such as Latinos, Native Americans, Asians, Middle Easterners, Multiracial populations, and even some White Americans and Black Americans (Campbell and Troyer 2007; Golash-Boza and Darity 2008; Harris and Sim 2000; Hitlin, Brown, and Elder 2006; Rockquemore and Brunsma 2002; Roth 2010; Saperstein 2006; Vargas and Stainback 2016). The type of survey and question format can affect which dimension gets measured, influencing the fluid nature of Multiracial identification (Campbell 2007). For example, a question format with multiple check boxes and “check all that apply” could encourage multiple responses whereas not including “check all that apply” could encourage only one response. To help address these challenges, I use ethnoracial identification questions from two different decennial Censuses for a few reasons. First, the information collected by the Census Bureau is useful for study because it captures the largest and most common ethnoracial identification categories in the United States. Second, the decennial surveys are administered to the full count of the population, which is important for measuring changes in small groups that are often missed in survey samples. Third, the question format is identical for all respondents. Fourth, because each decennial Census has the same kinds of public information campaigns and pressure to respond, it is likely that people have the same conception of the legal responsibility to respond. While there is no guarantee, and different data collection instruments cannot reliably be understood to capture the same dimension every time, choosing to use two different decennial Censuses maximizes the likelihood that the same dimension of race is being measured in both datasets. Sample Creation I start with the decennial 2000 Census data and use the household identification number and relationship to householder to link family members together. Due to data limitations in the way 2000 data were collected and structured, it is not possible to reliably link children to unmarried parents or same-sex parents. Furthermore, only the races of both parents and their biological children who live in the same household are recorded, making it possible to confirm the mixed racial backgrounds of only biological, and not adopted, children who are living with both parents (Harris, Perlman, and Waters 2002). Therefore, I only use households of different-sex married partners and their biological children because these are the only children for whom their race and both of their parents’ races are recorded. Because ethnic and racial identity development are understood to occur during adolescence (French et al. 2006; Phinney 1989; Umaña‐Taylor et al. 2014), I am interested in selecting children who are pre-adolescent so that I can capture how they were identified prior to developing their own independent ethnic identity, and then compare that identification to their own self-identification as adults. Adolescence is often associated with the onset of puberty. It is generally understood that, regardless of time or place, the age of onset of puberty occurs earlier for girls than for boys. The average age of menarche for girls born between 1990 and 1999 was 12.1 +/- 1.6 years (Wang et al. 2024). Based on this, I restrict my sample to households with children ages 10 and under in 2000, i.e., born between 1990 and 2000, to ensure my sample consists of pre-adolescents. To make the birthyear cut, I use the PIK to link my sample to the NUMIDENT and then use the birthyear of the children as reported in the NUMIDENT to refine my sample to biological families with children born between 1990 and 2000. Then, I further filter my sample of households by the ethnoracial identification of the householder and spouse, keeping only the households with one (and only one) Asian Indian parent.Because the share of individuals in any Hispanic category is very small and supplemental tests showed that excluding them did not change the substantive results, I combine them with the Non-Hispanic share. I then link the children by their PIK in decennial Census 2000 (when they are ages 0-10 years) to their PIK in Census 2020 (when they are ages 20-30 years) and to the NUMIDENT. The size of my linked sample is 2,509. I also create a sample of 1,622 linked individuals who have one Asian Indian parent and one White parent. The 1,622 individuals are a subset of the larger sample. It should be noted that linking creates some limitations. First, children have lower PIK rates than adults and due to time lags between the birth of a child and when the parents file for the child’s social security number, those aged 0-9 years of age are less likely than other age groups to be in both Census 2000 and the NUMIDENT (Mulrow, Pramanik, and Fontes 2011; Mulry and Petroni 2003; Rastogi et al. 2012). Second, in Census 2010, PIK rates vary by racial group and range from about 75% for Some Other Race alone to about 92% for White alone; for Multiracial the PIK rate is about 90% and for Asian alone it is about 88% (Rastogi et al. 2012) and these figures are comparable in Census 2000 (Mulry 2007). Third, PIK assignments have been shown to over-represent the non-migrant population, those with proficiency in English, citizens, other population sub-groups that are more likely to appear in federal government administrative records, and those who reside in the Midwest (Bond et al. 2014; Layne et al. 2014; Rastogi et al. 2012). Even though linking to the NUMIDENT limits the linked sample to only those immigrants who have at some point during their time in the U.S. had legal authorization to be in the U.S., doing so allows the largest and most representative longitudinal sample that is currently feasible. Dependent Variables I use the responses to the race question to create two dependent variables (DV) for my models. The first DV is racial identification in childhood (as reported on decennial census 2000), when the individuals in my sample were ages 0-10 years old. The second DV is racial self-identity in adulthood (as reported on decennial census 2020), when the individuals in my sample were ages 20-30 years old. To make the two DVs as comparable as possible, I construct them to consist of the same set of mutually exclusive racial response categories. The share of individuals in my sample with an identification that is not White, Asian, or Multiracial is quite small. Therefore, I create a category called Other and combine the individuals who were identified as Black, American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or Some Other Race in it. The DVs have binary outcomes (0=no, 1=yes) for each of the five mutually exclusive racial categories: White, Asian, Asian Indian, Multiracial, and Other. I define White as people who were identified as only White, and Asian as people who were only identified as Asian, Chinese, Filipino, Japanese, Korean, Vietnamese, and/or Other Asian, but not as Asian Indian. I define Asian Indian as people who were exclusively identified as Asian Indian. I define Multiracial as people who were identified as two or more races. Independent Variables I create a state-level variable that contains the percentage of people who identify as White in each state in decennial 2000 Census and decennial 2020 Census, including Washington D.C. and Puerto Rico as “states.” From decennial 2000 Census, I assign the state of residence to individuals in my sample when they are between the ages of 0 and 10 years, and from 2020 Census, I assign the state of residence when individuals in my sample are between the ages of 20 and 30 years. I repeat these steps for the percentage of people who identify as Asian in each state in 2000 and 2020, and for the percentage of people who identify as Multiracial in each state in 2000 and 2020. These variables are Percentage of White in State of Residence, Percentage of Asian in State of Residence, and Percentage of Multiracial in State of Residence. Control Variables I control for sex (0=male, 1=female) and income , as reported in 2000. I compare sex as reported on the decennial census 2000 to the sex reported in the NUMIDENT and find no differences in my sample. I use parental income information from the 2000 longform to capture the context of the childhood household of individuals in my sample. I observe two negative values for income in my sample and replace them with the median of all reported positive income values in the sample. Then I use the natural log transformation of income to normalize its distribution and yield a better fit in the regression models. These variables, along with the summary statistics for each sample, are presented in Tables 2(a) and 2(b). Logit Regression Model Specifications I specify a logit regression model where the dependent variable is the identification of the child as Multiracial (0=no, 1=yes) in 2000 and the independent variable is a continuous measure of the share of the population that is Multiracial in the child’s state of residence in 2000 to test whether children in the sample who live in states with a high presence of Multiracial people are more likely to be identified by their parents as Multiracial. I specify a second logit regression model where the dependent variable is self-identifying as Multiracial (0=no, 1=yes) in 2020 and the independent variable is continuous for the share of the population that is Multiracial in the adult child’s state of residence in 2020 to test whether adults in the sample who live in states with a high presence of Multiracial people more likely to self-identify as Multiracial. I specify similar models to test whether sample respondents who live in states with a high presence of Asian people are more likely to be identified or self-identify as Asian, and as White if they live in states with a high presence of White people. In every model, I control for sex (0=male, 1=female) and log income. RESULTS State Share of Multiracial and Multiracial Identification Both the childhood and adulthood models show that whether the respondent is identified or self-identifies as Multiracial is positively but not significantly related to the percentage of Multiracial people residing in the same state as the respondent. While sex is not a significant predictor in either model, income has a positive and significant relationship with being identified as Multiracial in both models. As income increases, the likelihood of parents identifying their children as Multiracial significantly increases and the likelihood of self-identifying as Multiracial in adulthood significantly increases. However, net of controls, people with one Asian Indian parent who live in places with a high presence of other Multiracial people are not significantly more likely to be identified by their parents in childhood or to self-identify as Multiracial in adulthood. These results are presented in Table 3. Based on these results, I reject my hypothesis that the share of Multiracial people in the state of residence has a significant relationship with being identified in childhood, or self-identifying in adulthood, as Multiracial. State Share of Asian and Asian Identification Both the childhood and adulthood models show a positive and significant relationship with being identified or self-identifying as Asian and the percentage of the population that is Asian in the state of residence. This means that as the state-level Asian population increases, there is an associated increase in the likelihood that the respondent is identified or self-identifies as Asian. In addition, both models show a negative and significant relationship of income with identification or self-identifying as Asian. This means that as income increases, the likelihood of identifying the respondent as Asian significantly decreases. Lastly, in both models, the relationship of sex with identification or self-identifying as Asian is negative but not significant. The results of both models are presented in Table 4. Based on these results, I fail to reject my hypothesis that the share of Asian people in the state of residence has a significant relationship with being identified in childhood, or self-identifying in adulthood, as Asian. Since the coefficients are in logits, which can be difficult to interpret, I used the coefficients to calculate the predicted probabilities. For males with the mean income of $65,000, if the populationin the state of residence is 1% Asian, then the predicted probability that a child is identified as Asian is 36% and that an adult self-identifies as Asian is 23%. Alternatively, if the population in the state of residence is 50% Asian, then the predicted probability that a child is identified as Asian is 81% and that an adult self-identifies as Asian is 90%, all else being equal. These results are shown in Figure 2. State Share of White and White Identification For these models, I limited the sample to the subset of 1,622 individuals who have one Asian Indian parent and one White parent. In both models, the percentage of White people residing in the same state as the respondent shares a positive and significant relationship with being identified or self-identifying as White. This means that as the state-level White population increases, there is an associated increase in the likelihood of being identified or self-identifying as White. The relationship is stronger and more significant in adulthood than in childhood. In addition, both models show a negative and significant relationship for income with identifying the respondent as White. This means that as income increases, the likelihood of being identified or self-identifying as White decreases. Lastly, while the relationship of being female with being identified as White is negative and not significant in childhood, it is negative and significant in adulthood. This means that adult females are less likely than adult males to identify as White. The results are summarized in Table 5. Based on these results, I fail to reject my hypothesis that the share of White people in the state of residence has a significant relationship with being identified in childhood, or self-identifying in adulthood, as White. Once again, I calculate predicted probabilities to better understand these results. The predicted probability that a child is identified as White in a state with a 50% White population is 61.2%, all else being equal. The predicted probability of a child being identified as White in a state with a 90% White population is 73.1%, all else being equal. Said another way, a 40-percentage point increase in the White population increases the odds of a child being identified as White by about 12 percent. In adulthood, the predicted probability of self-identifying as White in a state with a 50% White population is 67.6%, all else being equal. For adults in a state with a 90% White population, the predicted probability of self-identifying as White is 79.4%, all else being equal. These results are shown in Figure 3. Finally, because income is a significant predictor in all the models, I calculated the predicted probabilities across the income spread for identification as Multiracial, Asian, and White. I centered the share of the state population on the mean for each racial category. The probability of being identified or self-identifying as Multiracial increases with income while the probability of being identified or self-identifying as either Asian or White decreases with income. These results are included in Appendix A. Discussion and Conclusion Understanding patterned differences between parental- and self- identification can shed light on the racialized processes of social assimilation and incorporation, as well as the otherwise unseen downward intergenerational, social, and geographic mobility, that Multiracial people face. For people with Multiracial backgrounds, downward mobility can be understood through the lens of social networks and the resources made available by social networks (Campbell 2009). Families generate and share resources that can be in the form of social, cultural, and economic capital. However, some people hold stereotypes against specific racial groups and refuse to share their resources equitably with Multiracial family members, contributing to inequity across racial groups (Bratter and Campbell 2023; Franco et al. 2020; Song 2017). In the context of this study, a Multiracial child who was identified as White in childhood but who self-identifies as Asian in adulthood could speak to racialized social processes that reinforce the boundaries of White privilege. Race is an organizing principle of U.S. society, and so one’s ethnoracial identity holds meaning for many economic, social, and political outcomes. Like other identities, ethnoracial identity can be experienced differently in adulthood compared to what parents ascribed in childhood. Removed by a generation, the children of interracial unions likely experience racialized social interactions in the U.S. in ways that differ from their single-race parents.Therefore, how parents identify their children’s race is likely informed by the interactions and experiences they encounter in their own lives. Similarly, how the children of interracial unions self-identify their race in adulthood is likely based on their own social interactions and racial experiences. Hence, racial identification can reflect different contexts, times, and spaces. This study shows that people who have only one Asian Indian parent are significantly more likely to self-identify as Asian or Asian Indian if they live in a state with a larger Asian population and that people who have one Asian Indian parent and one White parent are significantly more likely to self-identify as White if they live in a state with a larger White population. Additionally, this study shows that racial identification for this segment of the Multiracial population can change from childhood to adulthood. The findings of this study underscore the importance of including contextual factors, such as the racial composition in the place of residence, in population projection models. Differences in how parents identify their young children and how those children identify themselves in adulthood mean that the enumeration by race of the population is not fixed by birth cohort. The assumption of stability in racial identification from childhood to adulthood among this population could result in an underestimate of the projected brown population if these adults move to a state with more Asian people and an overestimate if they move to a state with more White people. Unmasking patterned relationships between geographical racial context and individual racial identification can be consequential for the way we conceptualize effective strategies to mitigate social inequality. Program development and funding decisions aimed at mitigating social inequality, which are closely related to the enumeration by race of the population, need to be adjusted accordingly. Furthermore, unmasking the instability of racial identities over the young lifecourse for Multiracial people who have one Asian Indian parent in relation to the racial composition in their state of residence raises a very important question about whether people are moving to places that match their identity or whether their identity is influenced by the place in which they live. Suggested future directions include modeling other Multiracial populations using this methodology and adjusting population projection models accordingly. In addition, studying lower levels of geography, e.g. county, could reveal additional and interesting patterns. Qualitative work can focus on extending Kohli and coauthor’s (2023) work to investigate whether people are relieving themselves of pressure by avoiding some identifications and choosing other identifications, and on exploring the processes Multiracial people use to navigate the racially undergirded social, political, and economic boundaries that result in distributional and relational inequality (Carter 2024; Thye, Kalkhoff, and Lawler 2025). Limitations Focused on a subset of the Multiracial population, the results of this study are not generalizable to all Multiracial populations. Second, although linking to the NUMIDENT limits my sample to the native-born and a subset of immigrants, it is likely that the population of interest for my study is represented in the NUMIDENT, and NUMIDENT data remain the best available data for linking. Third, survey response data carries concern about the check boxes that are presented, e.g. American Indian versus Asian Indian, and the different coding of those responses in different years. Lastly, unlike the American Community Survey (ACS), the decennial Census does not contain an indicator of who filled out the survey. It is possible that the individuals in my sample did not personally fill out the decennial 2020 Census. However, to test the potential bias this creates, Anders and co-authors linked the ACS and Census responses for ~22.9 million people and found the results for single person households are largely the same as the results for all households (Anders et al. 2025). While this provides some assurance for my study, fluidity could be the result of someone else filling out the survey and this remains an important area for future work. Declarations STATEMENT of DISCLOSURE This article includes research conducted in a Federal Statistical Research Data Center (FSRDC). The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data used to produce this product (Data Management System [DMS] number P-7522857, Disclosure Review Board [DRB] approval number CBDRB-FY25-0101). This material is based on work supported by the National Science Foundation under Grant Number 2148889. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation or the U.S. Census Bureau. References Albuja, A. F., Diana, T., Sanchez, & Gaither, S. E. (2018). Fluid Racial Presentation: Perceptions of Contextual ‘Passing’ among Biracial People. Journal of Experimental Social Psychology , 77 , 132–142. https://doi:10.1016/j.jesp.2018.04.010 Alexander, J., and Katie Genadek (2023). 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Supplementary Files Tables.docx APPENDIXA.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-7133247","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492641808,"identity":"95e6d5a9-235a-419b-991c-f51f1b508086","order_by":0,"name":"Luna Chandna","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACPgYeIMnGwMAPFWBsIKSFDaZFso1kLQbHiNbCfvbg54KyO/LG95ufbuZhsJHdcICQFp68ZOkZ554ZbjvGZnabhyHNmLAWCR4Dad62w4zbjjGAtBxOJEaL8W+gFvvNbezfgFr+E6XFDGRL4gY2HpAtB4jQwpNjZs1z7nDyjGM5ZTfnGCQbzySkhZ/9jPFtnrLDtv3Nx7fdeFNhJ9tHSAsaMCBN+SgYBaNgFIwCHAAAssM+WnYvV+sAAAAASUVORK5CYII=","orcid":"","institution":"Texas A\u0026M University","correspondingAuthor":true,"prefix":"","firstName":"Luna","middleName":"","lastName":"Chandna","suffix":""}],"badges":[],"createdAt":"2025-07-15 18:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7133247/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7133247/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":91432717,"identity":"9025b682-b000-4077-aa48-5ba065cf0793","added_by":"auto","created_at":"2025-09-16 12:32:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":283468,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(a)\u003c/strong\u003e Decennial 2000 Race Question\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(b)\u003c/strong\u003e Decennial 2020 Race Question\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7133247/v1/467e18b3a13167fd3d9fd05c.png"},{"id":91432719,"identity":"11dbbafd-8393-45b6-bc19-c28ec732ff29","added_by":"auto","created_at":"2025-09-16 12:32:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":25232,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Probability of Asian Identification by % Asian in State\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7133247/v1/229705fcc7862e404541af87.png"},{"id":91433739,"identity":"429540df-5070-48eb-8d29-21888cfc4076","added_by":"auto","created_at":"2025-09-16 12:40:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":13039,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Probability of White Identification by % White in State\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7133247/v1/9966e5e513b9815422a17089.png"},{"id":91435020,"identity":"e80b17dd-14be-4b31-9f07-b5a40a5f787b","added_by":"auto","created_at":"2025-09-16 12:56:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1014347,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7133247/v1/c16280dc-1aef-43bd-a83b-a445f4c7bedf.pdf"},{"id":91434154,"identity":"4a609524-82e7-4abe-93d8-e49dadd16b6a","added_by":"auto","created_at":"2025-09-16 12:48:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":516323,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.docx","url":"https://assets-eu.researchsquare.com/files/rs-7133247/v1/2684da28fd715f944be85ad2.docx"},{"id":91432718,"identity":"3534a564-d934-4192-9fb0-ef18c381b128","added_by":"auto","created_at":"2025-09-16 12:32:25","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":28263,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIXA.docx","url":"https://assets-eu.researchsquare.com/files/rs-7133247/v1/5e22f930765b99b9d3f7b68b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eRacing Places: The Relationship Between Racial Composition in State of Residence With Racial Identification Among a Cohort of People with Only One Asian Indian Parent\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePopulation projection models assume that an individual’s racial identification remains stable across their lifecourse (U.S. Census Bureau 2023). However, research shows that approximately 6-7% of the general U.S. population do not have stable racial identifications (Liebler et al. 2017). Additional research shows that the largest share of the 6-7% people who demonstrate instability in their racial identification are Multiracial (Anders et al. 2025). Because there are many multiracial groups, studying \u003cem\u003ethe\u003c/em\u003e multiracial population is problematic (Harris and Sim 2002). However, focusing on a clearly defined multiracial group to study patterned differences between how parents racially identify their young multiracial children and how those children racially identify themselves in early adulthood can help shed light on how to improve population estimates as well as better understand racialized processes of social assimilation and incorporation.\u003c/p\u003e\n\u003cp\u003eThis study examines the relationship of racial composition in the state of residence with patterns of racial identification from childhood to adulthood among people with one Asian Indian parent. For the purposes of this study, people with one Asian Indian parent are defined as people who have one Asian Indian parent and one non-Asian Indian parent, and a non-Asian Indian parent is defined as an individual who exclusively identifies as \u003cem\u003enot\u003c/em\u003e Asian Indian. Therefore, non-Asian Indian parents encompass those who identify with either another Asian subgroup or another racial group. For example, children whose parents are Asian Indian and White as well as children whose parents are Asian Indian and Chinese are considered to have one Asian Indian parent in this study. People with \u003cem\u003etwo\u003c/em\u003e Asian Indian parents are \u003cem\u003enot\u003c/em\u003e included in this study. \u0026nbsp;All research was conducted in a Federal Statistical Research Data Center.\u003c/p\u003e"},{"header":" Background","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eRacial Identity and Racial Identification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRacial identity is how one thinks of themselves and racial identification is how one publicly describes themselves (Campbell 2007; Owens, Robinson, and Smith-Lovin 2010; Uma\u0026ntilde;a‐Taylor et al. 2014). While people who identify with only one race often report having the same racial identity and racial identification, Multiracial people can have one racial identity and a different racial identification. Multiracial people report checking racial identification boxes that reflect how they are identified by others, which is often different from their racial identity (Khanna 2004). This difference between racial identity and racial identification is important to understand when thinking about how people with multiracial backgrounds are described by their parents and how they describe themselves on surveys like the decennial Census. Because different parts of a Multiracial individual\u0026rsquo;s ancestry may feel more salient under different circumstances or at different times (Gonlin 2022; Gullickson and Morning 2011; Herman 2004, 2010), Multiracial individuals may choose to self-identify with different race groups on different surveys (Campbell 2007; Norman and Chen 2020).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFluidity of Ethnoracial Identification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWhile adults likely exercise agency in selecting their ethnoracial identification, young children are almost certain to have their ethnoracial identity assigned to them by the adult filling out the survey on their behalf. Liebler (2017) has definitively shown that people\u0026rsquo;s ethnoracial self-identification is fluid across time and Anders et al. (2025) shows the highest rates of fluidity occur among people with multiracial backgrounds. To date, little is understood about the mechanisms that influence ethnoracial fluidity.\u003c/p\u003e\n\u003cp\u003eFor people with multiracial backgrounds, studying change in their parental-assigned racial identification from childhood to their self-selected racial identification in adulthood can shed light on issues related to social inequality, mobility, and assimilation throughout the lifecourse. Interracial marriage is understood to be a marker of decreasing social distance, indicative of improving racial relations (Qian and Lichter 2007, 2011; Bratter and Campbell 2023). However, improving cannot be understood to mean equal, and many scholars articulate the continued use of race as a foundational and organizing principle of U.S. society (Bonilla-Silva 2018; Feagin 2023).\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc191479801\"\u003e\u003cstrong\u003e\u003cem\u003eMultiracial Identification and Measurement\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUntil the year 2000, the U.S. Census allowed respondents to check only one race box to indicate their racial identification. This meant that people with multiracial backgrounds could select only one of the races with which they identified. Starting in 2000, the Census has allowed respondents to select multiple race boxes to indicate their racial identification. In the 2000 census, 6.8 million people reported identifying with two or more races (Jones and Smith 2001). This figure increased to 9 million in Census 2010 (Jones and Bullock 2012), and to 33.8 million in 2020 (Jones et al. 2021).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe decennial 2020 Census reported a 276% increase in the Multiracial population from 2010 to 2020 but the figure in 2020 is incomparable to figures from prior years due to the use of a different coding methodology. Unlike in previous decennial censuses, the Census Bureau recoded individuals in the decennial 2020 census as Multiracial if they checked only one race box and wrote in an origin response that did not match their race. \u0026nbsp;For example, an individual who checked only the White box for race and wrote in Venezuela for origin was recoded as Multiracial because Venezuela was not coded as a White, European origin. Origins are not the same as either race or identity. Confounding origins, race, and identity contributed to an artificial increase in the Multiracial population (Arias et al. 2025; Starr and Pao 2024; Ventura and Flores 2025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, how the race of Multiracial people is counted in the Census has direct implications for the calculation of the rate at which America is browning. Therefore, being able to understand the mechanisms that influence ethnoracial identification to predict how Multiracial people of various ancestral backgrounds will self-identify in the Census is consequential. Scholars who document higher rates of ethnoracial fluidity among people who initially identified as Asian compared to people who initially identified as White or Black call researchers to focus their attention on Asians (Breen 2023; Liebler et al. 2017).\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc191479803\"\u003e\u003cstrong\u003e\u003cem\u003eAsians and Intermarriage\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs the Asian population has risen over the last few decades, the topic of Multiracial Asians has increasingly come into focus in the sociological literature (Burke and Kao 2013; Castillo et al. 2020; Cheng 2009; Cheng and Lively 2009; Kim-Breunig and Vittrup 2022; Lopez and Pantoja 2004; Qian 1997, 2004; Xie and Goyette 1997). Among the panethnic groups, Asians have the second highest rate of intermarriage (28%), compared to 7% for Whites, 19% for Blacks, and 58% for American Indians (Livingston and Brown 2017; Wang 2015). Numerous scholars have noted that the emphasis, largely due to data limitations, on panethnic Asians limits our ability to understand the heterogeneity within this broad grouping and have called for the disaggregated study of Asians.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eA Closer Look at Multiracial Asian Indians\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 1980, 15.5% of Asian Indians in the United States reported having a spouse who is not of Indian origin; this figure was 12.1% in 1990, and 20% in 2000. Of intermarried Asian Indians, 86.8% reported having a White spouse in 1980 and 69.3% reported having a White spouse in 1990 (Lee and Fernandez 1998). From 2010 to 2020, among the six largest Asian subgroups, the alone-only and alone-or-in-combination Asian Indian populations experienced the fastest rate of increase (Rico, Jacobs, and Coritz 2023). These changes in population size are shown in Table 1. This rise, together with the fact that the offspring of unions between Asian Indians and non-Asian Indians are understudied, make the children of intermarried Asian Indians a good choice for this study. I contribute to the literature by disaggregating the Multiracial Asian Indian population from the Multiracial Asian population and longitudinally linking individuals with one Asian Indian parent using restricted data from the 2000 Census, the 2020 Census, and the NUMIDENT to study changes in patterns of their ethnoracial self-identification. \u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc191479806\"\u003e\u003cstrong\u003e\u003cem\u003eRacial Composition in State of Residence\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe salience of race and ethnicity vary across states. People in different states have different purposes for identifying with a race. In Texas, some people of Latino origin choose to identify as white, regardless of skin color, as a strategy to avoid discrimination, while other people of Latino origin choose to identify as Other as a way to give voice to anti-racist politics (Dowling 2014). In California, politics have been intertwined with race and ethnicity in ways that have created anti- or pro-immigrant sentiments in different times (Cain, Citrin, and Wong 2000). For example, attitudes towards immigrants, both legal and illegal, have been shaped by public policies that either provide or deny services based on immigrant status\u003cem\u003e.\u0026nbsp;\u003c/em\u003eDiscrimination, politics, and legal issues at the state level may influence how people think about themselves. An under-studied geographic scale for investigating ethnoracial identification, the state is a worthwhile level of geography to study for Asian Indians because more than half of the Asian Indian population in the U.S. resides in one of five states (California, Texas, New Jersey, New York, and Illinois), and Asian Indians are the largest Asian subgroup in only one county in the country (Cook county, IL) (Rico, Hahn, and Spence 2023). The state level is meaningful because people who live in a county with a small number of their group can still attain access to ethnic social networks and other group members who live within driving distance. I posit that Multiracial people with an Asian Indian parent could self-identify or be identified by their parents in ways that vary with state-level racial composition. I test three hypotheses:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHypothesis 1\u003c/em\u003e\u003c/strong\u003e: The share of Multiracial people in the state of residence has a significant\u0026nbsp;\u003c/p\u003e\n\u003cp\u003erelationship with being identified in childhood, or self-identifying in adulthood, as\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMultiracial.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHypothesis 2\u003c/em\u003e\u003c/strong\u003e: The share of Asian people in the state of residence has a significant relationship\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ewith being identified in childhood, or self-identifying in adulthood, as Asian.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHypothesis 3\u003c/em\u003e\u003c/strong\u003e: The share of White people in the state of residence has a significant relationship\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ewith being identified in childhood, or self-identifying in adulthood, as White.\u003c/p\u003e"},{"header":"Data and Methods","content":"\u003cp\u003eThe data sources I use are the decennial 2000 Census, decennial 2020 Census, and the NUMIDENT. To create a single record for each individual, I use protected identification keys (PIKs) to link individuals across these datasets. In this section, I provide descriptions of each dataset, PIKS, and my methods.\u003c/p\u003e\n\u003cp id=\"_Toc191479812\"\u003e\u003cstrong\u003e\u003cem\u003eDecennial Census\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe U.S. Census Bureau is required by law to enumerate the population every decade. These decennial censuses include population and housing data, but the data collection is not identical across decades. The questions asking about race and ethnicity on the 2000 and 2020 forms are shown in Figures 1(a) and 1(b), respectively. In both Census 2000 and Census 2020, there was a checkbox for Asian Indian as well as space for a write-in response. I combine the respondents who marked the Asian Indian checkbox with the respondents who were recoded to Asian Indian based on their write-in response, which included responses like \u0026ldquo;Gujarat.\u0026rdquo; I use the detailed codes for race, relationship to householder, household type, and parent of own children to correctly identify the sample and its characteristics from 2000 to 2020.\u003c/p\u003e\n\u003cp id=\"_Toc191479814\"\u003e\u003cstrong\u003e\u003cem\u003eNUMIDENT\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The Social Security Administration has a master file, referred to as the numerical identification system or NUMIDENT, of everyone who has applied for a social security number since 1936. As such, the NUMIDENT contains information such as full name, sex, place and date of birth, and date of death if applicable (National Archives 2018) for everyone who has ever had legal authorization to work in the U.S. Between 2001 and 2015, more than 50% of all H1-B visas were awarded to Asian Indian nationals (Ruiz 2018). This limits the known bias created by linking to the NUMIDENT (Taylor, Stuart, and Bailey 2016) because it is more likely than not that the Asian Indian parent of individuals in my study appear in the NUMIDENT.\u003c/p\u003e\n\u003cp id=\"_Toc191479815\"\u003e\u003cstrong\u003e\u003cem\u003eProtected Identification Key (PIK)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;The Census Bureau collects information from the decennial censuses, conducts quality control checks, and then replaces sensitive personally identifying information with a Protected Identification Key (PIK). The Census Bureau links PIKs to individual observations via the Person Identification Validation System (PVS), which uses probabilistic matching to assign a unique identifier for each person (Layne, Wagner, and Rothhaas 2014). For censuses and major surveys conducted since 2000, more than 90% of cases have PIKs assigned (Massey and O\u0026rsquo;Hara 2014) and nearly all assigned PIKs have been shown to be accurate (Alexander and Genadek 2023).\u003c/p\u003e\n\u003cp\u003eMeasuring ethnoracial identity is challenging because of its multiple dimensions (Roth 2016), which are frequently inconsistent among groups such as Latinos, Native Americans, Asians, Middle Easterners, Multiracial populations, and even some White Americans and Black Americans (Campbell and Troyer 2007; Golash-Boza and Darity 2008; Harris and Sim 2000; Hitlin, Brown, and Elder 2006; Rockquemore and Brunsma 2002; Roth 2010; Saperstein 2006; Vargas and Stainback 2016). The type of survey and question format can affect which dimension gets measured, influencing the fluid nature of Multiracial identification (Campbell 2007). For example, a question format with multiple check boxes and \u0026ldquo;check all that apply\u0026rdquo; could encourage multiple responses whereas not including \u0026ldquo;check all that apply\u0026rdquo; could encourage only one response.\u003c/p\u003e\n\u003cp\u003eTo help address these challenges, I use ethnoracial identification questions from two different decennial Censuses for a few reasons. First, the information collected by the Census Bureau is useful for study because it captures the largest and most common ethnoracial identification categories in the United States. Second, the decennial surveys are administered to the full count of the population, which is important for measuring changes in small groups that are often missed in survey samples. Third, the question format is identical for all respondents. Fourth, because each decennial Census has the same kinds of public information campaigns and pressure to respond, it is likely that people have the same conception of the legal responsibility to respond. While there is no guarantee, and different data collection instruments cannot reliably be understood to capture the same dimension every time, choosing to use two different decennial Censuses maximizes the likelihood that the same dimension of race is being measured in both datasets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eSample Creation\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI start with the decennial 2000 Census data and use the household identification number and relationship to householder to link family members together. Due to data limitations in the way 2000 data were collected and structured, it is not possible to reliably link children to unmarried parents or same-sex parents. Furthermore, only the races of both parents and their biological children who live in the same household are recorded, making it possible to confirm the mixed racial backgrounds of only biological, and not adopted, children who are living with both parents (Harris, Perlman, and Waters 2002). Therefore, I only use households of different-sex married partners and their biological children because these are the only children for whom their race and both of their parents\u0026rsquo; races are recorded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBecause ethnic and racial identity development are understood to occur during adolescence (French et al. 2006; Phinney 1989; Uma\u0026ntilde;a‐Taylor et al. 2014), I am interested in selecting children who are \u003cem\u003epre-adolescent\u0026nbsp;\u003c/em\u003eso that I can capture how they were identified prior to developing their own independent ethnic identity, and then compare that identification to their own self-identification as adults. Adolescence is often associated with the onset of puberty. It is generally understood that, regardless of time or place, the age of onset of puberty occurs earlier for girls than for boys. The average age of menarche for girls born between 1990 and 1999 was 12.1 +/- 1.6 years (Wang et al. 2024). Based on this, I restrict my sample to households with \u003cem\u003echildren ages 10 and under\u003c/em\u003e in 2000, i.e., born between 1990 and 2000, to ensure my sample consists of pre-adolescents.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo make the birthyear cut, I use the PIK to link my sample to the NUMIDENT and then use the birthyear of the children as reported in the NUMIDENT to refine my sample to biological families with children born between 1990 and 2000. Then, I further filter my sample of households by the ethnoracial identification of the householder and spouse, keeping only the households with one (and only one) Asian Indian parent.Because the share of individuals in any Hispanic category is very small and supplemental tests showed that excluding them did not change the substantive results, I combine them with the Non-Hispanic share.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eI then link the children by their PIK in decennial Census 2000 (when they are ages 0-10 years) to their PIK in Census 2020 (when they are ages 20-30 years) and to the NUMIDENT. The size of my linked sample is 2,509. I also create a sample of 1,622 linked individuals who have one Asian Indian parent and one White parent. The 1,622 individuals are a subset of the larger sample.\u003c/p\u003e\n\u003cp\u003eIt should be noted that linking creates some limitations. First, children have lower PIK rates than adults and due to time lags between the birth of a child and when the parents file for the child\u0026rsquo;s social security number, those aged 0-9 years of age are less likely than other age groups to be in both Census 2000 and the NUMIDENT (Mulrow, Pramanik, and Fontes 2011; Mulry and Petroni 2003; Rastogi et al. 2012). Second, in Census 2010, PIK rates vary by racial group and range from about 75% for Some Other Race alone to about 92% for White alone; for Multiracial the PIK rate is about 90% and for Asian alone it is about 88% (Rastogi et al. 2012) and these figures are comparable in Census 2000 (Mulry 2007). Third, PIK assignments have been shown to over-represent the non-migrant population, those with proficiency in English, citizens, other population sub-groups that are more likely to appear in federal government administrative records, and those who reside in the Midwest (Bond et al. 2014; Layne et al. 2014; Rastogi et al. 2012). Even though linking to the NUMIDENT limits the linked sample to only those immigrants who have at some point during their time in the U.S. had legal authorization to be in the U.S., doing so allows the largest and most representative longitudinal sample that is currently feasible.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc191479819\"\u003e\u003cstrong\u003e\u003cem\u003eDependent Variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI use the responses to the race question to create two dependent variables (DV) for my models. The first DV is \u003cem\u003eracial identification in childhood\u003c/em\u003e (as reported on decennial census 2000),\u0026nbsp;when the individuals in my sample were ages 0-10 years old.\u0026nbsp;The second DV is \u003cem\u003eracial self-identity in adulthood\u003c/em\u003e (as reported on decennial census 2020), when the individuals in my sample were ages 20-30 years old. To make the two DVs as comparable as possible, I construct them to consist of the same set of mutually exclusive racial response categories. The share of individuals in my sample with an identification that is not White, Asian, or Multiracial is quite small. Therefore, I create a category called Other and combine the individuals who were identified as Black, American Indian or Alaska Native, Native Hawaiian or Pacific Islander, or Some Other Race in it.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe DVs have binary outcomes (0=no, 1=yes) for each of the five mutually exclusive racial categories: White, Asian, Asian Indian, Multiracial, and Other. I define White as people who were identified as only White, and Asian as people who were only identified as Asian, Chinese, Filipino, Japanese, Korean, Vietnamese, and/or Other Asian, but \u003cem\u003enot\u003c/em\u003e as Asian Indian. I define Asian Indian as people who were exclusively identified as Asian Indian. I define Multiracial as people who were identified as two or more races.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc191479820\"\u003e\u003cstrong\u003e\u003cem\u003eIndependent Variables\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI create a state-level variable that contains the percentage of people who identify as White in each state in decennial 2000 Census and decennial 2020 Census, including Washington D.C. and Puerto Rico as \u0026ldquo;states.\u0026rdquo; From decennial 2000 Census, I assign the state of residence to individuals in my sample when they are between the ages of 0 and 10 years, and from 2020 Census, I assign the state of residence when individuals in my sample are between the ages of 20 and 30 years. I repeat these steps for the percentage of people who identify as Asian in each state in 2000 and 2020, and for the percentage of people who identify as Multiracial in each state in 2000 and 2020. These variables are\u0026nbsp;\u003cem\u003ePercentage of White in State of Residence, Percentage of Asian in State of Residence,\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePercentage of Multiracial in State of Residence.\u003c/em\u003e\u003c/p\u003e\n\u003cp id=\"_Toc191479821\"\u003e\u003cstrong\u003e\u003cem\u003eControl Variables\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI control for \u003cem\u003esex\u0026nbsp;\u003c/em\u003e(0=male, 1=female) and \u003cem\u003eincome\u003c/em\u003e, as reported in 2000. I compare sex as reported on the decennial census 2000 to the sex reported in the NUMIDENT and find no differences in my sample. I use parental income information from the 2000 longform to capture the context of the childhood household of individuals in my sample.\u0026nbsp;I observe two negative values for income in my sample and replace them with the median of all reported positive income values in the sample. Then I use the natural log transformation of income to normalize its distribution and yield a better fit in the regression models. These variables, along with the summary statistics for each sample, are presented in Tables 2(a) and 2(b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eLogit Regression Model Specifications\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI specify a logit regression model where the dependent variable is the identification of the child as Multiracial (0=no, 1=yes) in 2000 and the independent variable is a continuous measure of the share of the population that is Multiracial in the child\u0026rsquo;s state of residence in 2000 to test whether children in the sample who live in states with a high presence of Multiracial people are more likely to be identified by their parents as Multiracial. I specify a second logit regression model where the dependent variable is self-identifying as Multiracial (0=no, 1=yes) in 2020 and the independent variable is continuous for the share of the population that is Multiracial in the adult child\u0026rsquo;s state of residence in 2020 to test whether adults in the sample who live in states with a high presence of Multiracial people more likely to self-identify as Multiracial. I specify similar models to test whether sample respondents who live in states with a high presence of Asian people are more likely to be identified or self-identify as Asian, and as White if they live in states with a high presence of White people. In every model, I control for sex (0=male, 1=female) and log income.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eState Share of Multiracial\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;and Multiracial Identification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth the childhood and adulthood models show that whether the respondent is identified or self-identifies as Multiracial is positively but not significantly related to the percentage of Multiracial people residing in the same state as the respondent. While sex is not a significant predictor in either model, income has a positive and significant relationship with being identified as Multiracial in both models. As income increases, the likelihood of parents identifying their children as Multiracial significantly increases and the likelihood of self-identifying as Multiracial in adulthood significantly increases. However, net of controls, people with one Asian Indian parent who live in places with a high presence of other Multiracial people are not significantly more likely to be identified by their parents in childhood or to self-identify as Multiracial in adulthood. These results are presented in Table 3. Based on these results, I reject my hypothesis that the share of Multiracial people in the state of residence has a significant relationship with being identified in childhood, or self-identifying in adulthood, as Multiracial.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc191479827\"\u003e\u003cstrong\u003e\u003cem\u003eState Share of Asian\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;and Asian Identification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBoth the childhood and adulthood models show a positive and significant relationship with being identified or self-identifying as Asian and the percentage of the population that is Asian in the state of residence. This means that as the state-level Asian population increases, there is an associated increase in the likelihood that the respondent is identified or self-identifies as Asian. In addition, both models show a negative and significant relationship of income with identification or self-identifying as Asian. This means that as income increases, the likelihood of identifying the respondent as Asian significantly decreases. Lastly, in both models, the relationship of sex with identification or self-identifying as Asian is negative but not significant. The results of both models are presented in Table 4. Based on these results, I fail to reject my hypothesis that the share of Asian people in the state of residence has a significant relationship with being identified in childhood, or self-identifying in adulthood, as Asian.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSince the coefficients are in logits, which can be difficult to interpret, I used the coefficients to calculate the predicted probabilities. For males with the mean income of $65,000, if the populationin the state of residence is 1% Asian, then the predicted probability that a child is identified as Asian is 36% and that an adult self-identifies as Asian is 23%. Alternatively, if the population in the state of residence is 50% Asian, then the predicted probability that a child is identified as Asian is 81% and that an adult self-identifies as Asian is 90%, all else being equal. These results are shown in Figure 2.\u0026nbsp;\u003c/p\u003e\n\u003cp id=\"_Toc191479828\"\u003e\u003cstrong\u003e\u003cem\u003eState Share of White\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;and White Identification\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor these models, I limited the sample to the subset of 1,622 individuals who have one Asian Indian parent and one White parent.\u0026nbsp;In both models, the percentage of White people residing in the same state as the respondent shares a positive and significant relationship with being identified or self-identifying as White. This means that as the state-level White population increases, there is an associated increase in the likelihood of being identified or self-identifying as White. The relationship is stronger and more significant in adulthood than in childhood. In addition, both models show a negative and significant relationship for income with identifying the respondent as White. This means that as income increases, the likelihood of being identified or self-identifying as White decreases. Lastly, while the relationship of being female with being identified as White is negative and not significant in childhood, it is negative and significant in adulthood. This means that adult females are less likely than adult males to identify as White. The results are summarized in Table 5. Based on these results, I fail to reject my hypothesis that the share of White people in the state of residence has a significant relationship with being identified in childhood, or self-identifying in adulthood, as White.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOnce again, I calculate predicted probabilities to better understand these results. The predicted probability that a child is identified as White in a state with a 50% White population is 61.2%, all else being equal. The predicted probability of a child being identified as White in a state with a 90% White population is 73.1%, all else being equal. Said another way, a 40-percentage point increase in the White population increases the odds of a child being identified as White by about 12 percent. In adulthood, the predicted probability of self-identifying as White in a state with a 50% White population is 67.6%, all else being equal. For adults in a state with a 90% White population, the predicted probability of self-identifying as White is 79.4%, all else being equal. These results are shown in Figure 3. Finally, because income is a significant predictor in all the models, I calculated the predicted probabilities across the income spread for identification as Multiracial, Asian, and White. I centered the share of the state population on the mean for each racial category. The probability of being identified or self-identifying as Multiracial increases with income while the probability of being identified or self-identifying as either Asian or White decreases with income. These results are included in Appendix A.\u003c/p\u003e"},{"header":"Discussion and Conclusion","content":"\u003cp\u003eUnderstanding patterned differences between parental- and self- identification can shed light on the racialized processes of social assimilation and incorporation, as well as the otherwise unseen downward intergenerational, social, and geographic mobility, that Multiracial people face. For people with Multiracial backgrounds, downward mobility can be understood through the lens of social networks and the resources made available by social networks (Campbell 2009). Families generate and share resources that can be in the form of social, cultural, and economic capital. However, some people hold stereotypes against specific racial groups and refuse to share their resources equitably with Multiracial family members, contributing to inequity across racial groups (Bratter and Campbell 2023; Franco et al. 2020; Song 2017). In the context of this study, a Multiracial child who was identified as White in childhood but who self-identifies as Asian in adulthood could speak to racialized social processes that reinforce the boundaries of White privilege.\u003c/p\u003e\n\u003cp\u003eRace is an organizing principle of U.S. society, and so one\u0026rsquo;s ethnoracial identity holds meaning for many economic, social, and political outcomes. Like other identities, ethnoracial identity can be experienced differently in adulthood compared to what parents ascribed in childhood. Removed by a generation, the children of interracial unions likely experience racialized social interactions in the U.S. in ways that differ from their single-race parents.Therefore, how parents identify their children\u0026rsquo;s race is likely informed by the interactions and experiences they encounter in their own lives. Similarly, how the children of interracial unions self-identify their race in adulthood is likely based on their own social interactions and racial experiences. Hence, racial identification can reflect different contexts, times, and spaces. This study shows that people who have only one Asian Indian parent are significantly more likely to self-identify as Asian or Asian Indian if they live in a state with a larger Asian population and that people who have one Asian Indian parent and one White parent are significantly more likely to self-identify as White if they live in a state with a larger White population. Additionally, this study shows that racial identification for this segment of the Multiracial population can change from childhood to adulthood.\u003c/p\u003e\n\u003cp\u003eThe findings of this study underscore the importance of including contextual factors, such as the racial composition in the place of residence, in population projection models. Differences in how parents identify their young children and how those children identify themselves in adulthood mean that the enumeration by race of the population is not fixed by birth cohort. The assumption of stability in racial identification from childhood to adulthood among this population could result in an \u003cem\u003eunderestimate\u003c/em\u003e of the projected brown population if these adults move to a state with more Asian people and an \u003cem\u003eoverestimate\u003c/em\u003e if they move to a state with more White people.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eUnmasking patterned relationships between geographical racial context and individual racial identification can be consequential for the way we conceptualize effective strategies to mitigate social inequality. Program development and funding decisions aimed at mitigating social inequality, which are closely related to the enumeration by race of the population, need to be adjusted accordingly. Furthermore, unmasking the instability of racial identities over the young lifecourse for Multiracial people who have one Asian Indian parent in relation to the racial composition in their state of residence raises a very important question about whether people are \u003cem\u003emoving to\u003c/em\u003e places that match their identity or whether their identity is \u003cem\u003einfluenced by\u003c/em\u003e the place in which they live. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSuggested future directions include modeling other Multiracial populations using this methodology and adjusting population projection models accordingly. In addition, studying lower levels of geography, e.g. county, could reveal additional and interesting patterns. Qualitative work can focus on extending Kohli and coauthor\u0026rsquo;s (2023) work to investigate whether people are relieving themselves of pressure by avoiding some identifications and choosing other identifications, and on exploring the processes Multiracial people use to navigate the racially undergirded social, political, and economic boundaries that result in distributional and relational inequality (Carter 2024; Thye, Kalkhoff, and Lawler 2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFocused on a subset of the Multiracial population, the results of this study are not generalizable to \u003cem\u003eall\u003c/em\u003e Multiracial populations. Second, although linking to the NUMIDENT limits my sample to the native-born and a subset of immigrants, it is likely that the population of interest for my study is represented in the NUMIDENT, and NUMIDENT data remain the best available data for linking. Third, survey response data carries concern about the check boxes that are presented, e.g. American Indian versus Asian Indian, and the different coding of those responses in different years. Lastly, unlike the American Community Survey (ACS), the decennial Census does not contain an indicator of who filled out the survey. It is possible that the individuals in my sample did not personally fill out the decennial 2020 Census. However, to test the potential bias this creates, Anders and co-authors linked the ACS and Census responses for ~22.9 million people and found the results for single person households are largely the same as the results for all households (Anders et al. 2025). While this provides some assurance for my study, fluidity could be the result of someone else filling out the survey and this remains an important area for future work.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eSTATEMENT of DISCLOSURE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article includes research conducted in a Federal Statistical Research Data Center (FSRDC). The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data used to produce this product (Data Management System [DMS] number P-7522857, Disclosure Review Board [DRB] approval number CBDRB-FY25-0101). This material is based on work supported by the National Science Foundation under Grant Number 2148889. 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The Racial Identification of Biracial Children with One Asian Parent: Evidence from the 1990 Census. \u003cem\u003eSocial Forces\u003c/em\u003e, \u003cem\u003e76\u003c/em\u003e(2), 547. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/sf/76.2.547\u003c/span\u003e\u003cspan address=\"10.1093/sf/76.2.547\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 5 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"racial identification, Multiracial, racial composition of state, Asian Indian","lastPublishedDoi":"10.21203/rs.3.rs-7133247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7133247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rise in the Multiracial population in the United States contributes to the shift from a majority white population to a majority non-white population, commonly referred to as the browning of America. Population projection models often assume that people identified as Multiracial in childhood will continue to identify as Multiracial in adulthood. However, racial self-identification continues to develop over the lifecourse, and is both fluid and context-dependent (Albuja et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Liebler et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Root \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Waters \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). An important contextual factor to consider when studying the racial identification of people with multiracial backgrounds is the racial composition of where they live. Using restricted data from Census 2000 and Census 2020, I link the racial identification responses in childhood and in adulthood for 2,509 individuals who have only one Asian Indian parent and use logit regression models to study the association between their racial identification and the racial composition in their state of residence. I find that the share of White and the share of Asian population in the state of residence are significant predictors of corresponding identifications for people who have only one Asian Indian parent and who are born between 1990 and 2000. This work uniquely contributes to the literature about the understudied association between racial identification and the racial demographics of place, and has implications for policy makers, population scientists, and the projected browning rate of America.\u003c/p\u003e","manuscriptTitle":"Racing Places: The Relationship Between Racial Composition in State of Residence With Racial Identification Among a Cohort of People with Only One Asian Indian Parent","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-16 12:32:20","doi":"10.21203/rs.3.rs-7133247/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":"848e6a8f-2978-4423-a898-a9ad28159647","owner":[],"postedDate":"September 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T17:23:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-16 12:32:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7133247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7133247","identity":"rs-7133247","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}