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Family Background and Occupational Status: An Empirical Study Based on the China Family Panel Studies (CFPS)

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29 April 2026

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02 May 2026

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Abstract
This study examines how family background shapes individual occupational status within a behavioral science framework, using pooled data from the 2018 and 2020 waves of the China Family Panel Studies (CFPS). Grounded in New Human Capital Theory, it further investigates the moderating roles of cognitive and non-cognitive abilities in this relationship. The results indicate that family background exerts a significant and persistent positive effect on both initial and current occupational status, suggesting the enduring influence of intergenerational advantage. Robustness checks using alternative indicators, including father’s occupational status and mother’s education, confirm the stability of the findings. In addition, digital skills, appearance investment, and selected Big Five personality traits—agreeableness, openness, and conscientiousness—significantly strengthen the positive association between family background and occupational outcomes. These findings suggest that, beyond structural advantages, individual behavioral and psychological characteristics play a critical role in enabling individuals to effectively transform family resources into labor market success. Overall, the study provides empirical evidence on how behavioral factors interact with family background to shape occupational inequality in contemporary China.
Keywords: 
family background
;  
occupational status
;  
digital skills
;  
appearance investment
;  
big five personality traits
Subject: 
Business, Economics and Management  -   Human Resources and Organizations

1. Introduction

The family, as the fundamental unit of society, serves as a primary agent of individual socialization (Freeman & Showel, 1953). Family background exerts a direct influence on socioeconomic attainment across generations, largely independent of temporal or institutional constraints. In traditional societies, social status was predominantly determined by familial endowment and class origin, with limited social mobility. Intergenerational transmission meant that access to resources and opportunities was directly inherited, creating largely insurmountable divides between social strata, as seen in historical systems like China’s recommendation-based bureaucracy or medieval European aristocracy. In modern societies, while individual achievement is commonly attributed to a combination of ability, effort, and opportunity, outcomes continue to vary significantly across socioeconomic groups. The intergenerational transmission of both tangible and intangible resources, such as property and cultural knowledge, contributes to persistent disparities in occupational access, earnings, and career achievement (Kilpi-Jakonen, 2024). Even in the absence of innate ability differences and under conditions of well-functioning capital markets, economic inequalities tend to persist across generations (Becker et al., 2018; Lucas, 2001).
In contemporary contexts, as social structures evolved, occupation has become a key mechanism through which social inequality is expressed, and socioeconomic differences among individuals are increasingly reflected in occupational status (Broom et al., 2023; Sakamoto & Wang, 2020). Families continue to play a decisive role in shaping individuals’ career trajectories—a phenomenon could be characterized as “advantage finds its way” (Lucas, 2001). That is, even amid structural changes that may promote equal opportunity, privileged parents are often the first to identify and leverage new opportunities, securing advantageous conditions for their children’s professional success. For instance, the 2016 U.S. Wealth and Income Survey, jointly conducted by the Social Security Administration and the Bureau of Economic Analysis, revealed a direct correlation between family wealth and career success. Similarly, the Federal Reserve’s 2021 Survey of Consumer Finances indicated that household wealth significantly influences access to high-paying occupations, with children from affluent families more likely to enter high-income, high-status professions.
The influence of family background on individuals’ socioeconomic status has long been a central theme in social science research (Blau, 1992; Friedman & Laurison, 2019a, 2019b; Nelson & Vallas, 2021; Rivera, 2015; Willekens & Lievens, 2014). Given that occupation not only conveys critical information about an individual’s social standing but is also more readily measurable in empirical studies (Mazumder & Acosta, 2014), this study adopts occupational status as the dependent variable to examine the impact of family background. Occupational status refers to the socio-economic position of an individual’s occupation that holds social recognition and influence, reflecting job quality and capturing the socioeconomic characteristics of occupational groups (Judge et al., 1999). The classic Blau-Duncan Model first established that ascribed factors (including father’s occupational status and education) and achieved factors (including an individual’s own education) strongly predict individual occupational status (Blau & Duncan, 1967). Since then, most research has emphasized education as the primary mechanism of intergenerational status transmission (Le Goff et al., 2023). However, family influence on offspring’s socioeconomic status operates through multiple concurrent pathways. Focusing solely on education as the mechanism of intergenerational transmission may lead to biased estimates of human capital effects.
The New Human Capital Theory proposed by Hanushek extends the traditional framework by emphasizing the significant roles of both cognitive and non-cognitive abilities in determining social and economic outcomes (Hanushek, 2010). Guided by this theory, we propose that individuals’ cognitive and non-cognitive abilities play important roles in the process through which family background influences occupational status. Specifically, we examine three moderating variables: digital skills, appearance investment, and Big Five personality traits. Specifically, digital use serves as an essential component for becoming digitally talented, while digital awareness represents a crucial dimension of digital skills (Scheerder et al., 2017). Physical attractiveness enhances social appeal, interpersonal effectiveness, and professional competence, making appearance investment an increasingly strategic ability in modern workplaces (Deng et al., 2020). Personality traits, particularly the Big Five framework, constitute fundamental factors influencing career achievement (Bühler et al., 2020).
In summary, this study employs pooled cross-sectional data from the 2018 and 2020 waves of the China Family Panel Studies (CFPS) to examine the impact of family background on individual occupational status. Grounded in the New Human Capital Theory, it further investigates the moderating roles of three dimensions of cognitive and non-cognitive abilities: digital skills, appearance investment, and the Big Five personality traits. The empirical analysis comprises four components: baseline regression, moderation effect testing, robustness checks, and heterogeneity analysis. This study offers two primary marginal contributions. First, while existing literature has predominantly examined how macro-level social stratification shapes labor market outcomes such as income inequality (Addae, 2020; Björklund & Jäntti, 2020), this study uses micro-level survey data and focus on how familial resource endowment influences career development within Chinese culture. Second, whereas existing scholarship has primarily emphasized education as the central mechanism in intergenerational status transmission (Borgen, 2015; Heckman et al., 2006; Kilpi-Jakonen, 2024), this study adopts the theoretical framework of New Human Capital to propose that cognitive and non-cognitive abilities (digital skills, appearance investment, and Big Five personality traits) constitute crucial boundary conditions that influence how effectively family resources are translated into occupational advancement.

3. Data, Measurements, and Methods

3.1. Data

This study employs a pooled cross-sectional design using data from the 2018 and 2020 waves of the China Family Panel Studies (CFPS), a nationwide comprehensive social survey conducted by the Institute of Social Science Survey at Peking University. The CFPS covers approximately 16, 000 households across 25 provinces, municipalities, and autonomous regions in China, collecting longitudinal data at the individual, household, and community levels to capture the dynamics of social, economic, demographic, educational, and health developments in contemporary China. The analysis focuses on the adult population. By merging the individual and family relationship databases, key variables such as father’s education, individual occupational status, and other relevant measures were obtained. Missing values in certain variables (e.g., father’s education) were supplemented using data from the 2016 survey. The study sample is restricted to individuals aged 16 to 70 who are participating in the labor market.

3.2. Measurements

Family background：According to Bourdieu (1986), the three fundamental forms of family background—economic, cultural, and social—are interrelated and can be mutually convertible. Higher educational attainment is generally associated with elevated social status and economic resources (Coleman, 1988), and individuals with different levels of education often experience substantial disparities in family material, cultural, and economic environments (Mehryar & Tashakkori, 1984). Besides, the father’s education years serve not only as a core and stable indicator of family cultural capital but is also closely linked to economic and social capital, making it a reliable measure of the family background (Coleman, 1988; Peng, 2023). We use “father’s highest educational attainment” from the CFPS family database as the measure of family background. father’s educational attainment is relatively stable and publicly observable, making them more reliably reported in surveys and resulting in higher data quality. Father’s education is categorized as follows: 1 = no education, 2 = primary school, 3 = junior high school, 4 = high school/vocational/technical school, 5 = associate degree, and 6 = bachelor’s degree or above.
Occupational status: It was measured using the International Socio-Economic Index (ISEI) (Ganzeboom et al., 1992). The ISEI is constructed based on the average education and income levels associated with specific occupations (Blau & Duncan, 1967). The index ranges from 19 to 88, with higher scores indicating higher occupational status.
Digital skills: Referring to the existing relevant CFPS research on the measurement methods of digital skills (Mou et al., 2021; Cao and Cao, 2024), we used digital use and digital awareness as the measurement indicators of digital skills. Digital use was assessed based on affirmative responses to either “mobile internet use” or “computer internet use, ” coded as 1 if at least one was confirmed and 0 otherwise. Digital awareness was measured using the question on “the importance of the internet as an information channel, ” rated on a 5-point Likert scale from 1 (very unimportant) to 5 (very important).
Appearance investment: It was measured using the survey item “beauty and grooming expenses in the past 12 months” from the personal database. The expenditure amount was log-transformed after adding 1 to handle zero values, with higher scores indicating greater importance placed on physical appearance.
Big Five personality traits: Personality traits were assessed using the Big Five Inventory from the 2018 personal database. Each trait was measured by three items from 1 (strongly disagree) to 5 (strongly agree). All reverse-coded items, such as the agreeableness item “Sometimes being rude or impolite to others, ” were transformed before analysis. Composite scores for each trait were calculated by averaging the scores of its respective items.
Control variables: Gender (0 = female, 1 = male), age (ranging from 16 to 70 years), household registration status (0 = agricultural hukou, 1 = non-agricultural hukou), education (1 = no education, 2 = primary school, 3 = junior high school, 4 = high school/vocational/technical school, 5 = associate degree, 6 = bachelor’s degree or above), marriage (0 = not currently married, 1 = married), and region (0 = non-eastern China, 1 = eastern China).
Table 1 presents the descriptive statistics for all variables included in this research. The results show that the average occupational status (ISEI) of the sample is 34. For the explanatory variable of family background, measured by father’s education, the sample shows a mean value of 2. This indicates that the average parental education in the sample is at the primary school level. The average age of respondents is 44 years, clustering around middle adulthood, though the relatively large standard deviation suggests a wide age range spanning from 16 to 70 years. The gender distribution is relatively balanced, with a mean close to 0.5, though slightly skewed toward male respondents. In terms of household registration (hukou) type, most respondents hold non-agricultural hukou. The average educational attainment among respondents is 3, corresponding to junior high school education or above, which is higher than that of their fathers’ generation. Most respondents are married and reside in non-eastern regions of China.

3.3. Methods

To increase the sample size and ensure the availability of key variables such as father’s education, we constructed a pooled cross-sectional dataset by merging the two survey waves. Specifically, to examine the influence of family background on individual occupational status, we utilized data from the China Family Panel Studies (CFPS) for the years 2018 and 2020. We estimated both a baseline regression model with time-fixed effects and a moderation effect model.
The general specification of the baseline model is as follows:
ISEIit=α0+α1Edu_fit +∑αxControlsitit+φit+εit (1)
To further explore the moderating effects, we extended the baseline model by including a moderating variable and its interaction term with paternal education:
ISEIit=β0+β1Edu_fit+β2Moderateit+β3Edu_fit×Moderateit+∑βxControlsitit+φit+εi(2)
In the model specification, i denotes individual workers and t indicates the survey year. The dependent variable, ISEIit, measures occupational status. The independent variable, Edu_fit, represents father’s education, while Moderateit is the moderating variable. The interaction term between father’s education and moderating variable is included to capture the moderating effect. Controlsitit comprise a set of control variables, φit represents time-fixed effects, and εit denotes the idiosyncratic error term.

4. Results

4.1. Preliminary Analysis

Table 2 presents the baseline regression results examining the impact of family background on individual occupational status across Models 1 to 3. Model 1 includes only control variables, with results indicating that some individual characteristics (age, gender, hukou, education, marriage and region) significantly influence occupational status. Specifically, in terms of occupational status, males generally achieve lower status than females, individuals with non-agricultural hukou outperform those with agricultural hukou, and residents in eastern China attain higher status than those in non-eastern regions. All these effects are statistically significant at p < 0.001. Regarding age, older individuals show a decline in occupational status. Model 2 introduces family background into the regression, and Model 3 includes all variables while also controlling for time-fixed effects. The regression coefficients for family background are positive across all models, indicating that family background contributes positively to individual occupational status. In Model 3, the coefficient for father’s education is 0.473, suggesting that a one-unit increase in father’s education is associated with a 0.473-unit increase in the individual occupational status. The model explains 41.82% of the variance in occupational status. These results provide support for Hypothesis H1.
Moreover, we use the initial occupational status as a supplementary analysis. Initial occupation refers to an individual’s first job upon entering the labor market and initial occupational status serves as a variable measuring the position and quality of that first position. Research suggests that family background, relatively unconstrained by temporal and institutional changes, not only significantly influences current occupational status but also exerts a notable impact on the status of their initial job (Jäntti & Jenkins, 2024). Therefore, we introduce initial occupational status to examine whether family background similarly affects individuals’ first position in the labor market. In Models 4 to 6 of Table 2, initial occupational status is used as the dependent variable. The results show that the regression coefficients for the impact of family background on initial occupational status remain positive and statistically significant (p < 0.001). These findings indicate that family background consistently exerts a positive influence on individuals’ career development, regardless of whether the outcome measured is their current occupational status or their initial occupational status. This discovery further validates the importance of family background in the process of individual career development. Its impact on occupational status is persistent, exhibiting significant effects from the very beginning of an individual’s entry into the labor market.

4.1. Robustness Test

We conducted robustness test using a variable substitution approach. For the independent variable, we replaced the original measure of family background—father’s education—with two alternative indicators: mother’s education and father’s occupational status. The dependent variables included both initial and current occupational status. We first replaced father’s education with mother’s education as an alternative indicator of family background. The education of parents constitutes fundamental components of family capital. It is particularly relevant given mothers’ typically central role in individuals’ upbringing and education. Table 3 presents the regression results. Models 1 to Model 3 examine its effect on current occupational status (N = 26, 427). The results remain statistically significant (p < 0.001), further supporting the robustness of our main findings. Specifically, a one-unit increase in mother’s education corresponds to a 0.493-unit rise in individuals’ occupational status, accounting for 41.69% of the variance in occupational status. Model 4 to Model 6 analyze the relationship between mother’s education and initial occupational status (N = 20, 502). The significant coefficients (p < 0.001) confirm that the positive effect of family background persists when examining career entry positions.
Furthermore, we employed father’s occupational status as an alternative measure to father’s education. Occupational status serves as a crucial indicator of family capital, particularly reflecting a family’s social capital and network resources (Bourdieu, 1986)). Higher educational attainment typically enables fathers to secure more advantageous occupational positions, making this variable a substitute for family background. Due to the data availability, this analysis utilizes only the 2020 CFPS data, with father’s occupation measured by the survey item: “What was your father’s specific occupation when you were 14 years old?” As presented in Table 4, Models 1 and 2 examine the impact of father’s occupational status on individual occupational status (N = 1, 990). The results demonstrate a statistically significant positive relationship (p < 0.001). Models 3 and 4 use initial occupational status as the dependent variable (N = 1, 576), with findings similarly indicating a significant effect (p < 0.001).
These consistent results confirm the robustness of the relationship between family background and occupational attainment, which enhances the reliability of the research hypothesis. Whether using mother’s education or father’s occupational status as the measure of family background, family background consistently demonstrates a significant positive impact on individual occupational status, and this holds true for initial occupational status as well.

4.2. Moderating Effect Analysis

Table 5 and Table 6 present the regression results testing the moderating effects on the relationship between family background and occupational status. In Table 5, Model 1 displays the baseline regression incorporating control variables and time-fixed effects. Model 2 to model 4 introduce the moderating effects of digital skills (digital use and digital awareness), and appearance investment, respectively. The results reveal that the interaction terms between family background and these three moderators are all positive and statistically significant (β = 0.304, p < 0.001; β = 1.082, p < 0.001; β = 0.125, p < 0.001), indicating significant positive moderating effects. Specifically, individuals with the ability to use digital tools, higher awareness of the importance of digital information, and greater investment in personal appearance are better able to leverage their family background to achieve higher occupational status. These findings support hypotheses H2a, H2b, and H3.
Table 6 presents the moderating effects of the Big Five personality traits (Model 5 to model 8). The results show significant positive interactions between family background and agreeableness (β = 0.225, p < 0.05), openness (β = 0.253, p < 0.001), and conscientiousness (β = 0.517, p < 0.01). This indicates that higher levels of these personality traits strengthen the positive influence of family background on occupational status. Consequently, hypotheses H4a, H4b, and H4d are supported. The other hypotheses H4c and H4d, however, are not corroborated by the findings: neither extraversion nor neuroticism demonstrated a significant moderating effect on the relationship between family background and occupational status.

5. Summary and Discussion

Using pooled data from the 2018 and 2020 waves of the China Family Panel Studies (CFPS), this study examines the impact of family background on individual occupational status and explores the moderating roles of cognitive and non-cognitive abilities within a behavioral framework. The results show that family background exerts a significant and persistent positive effect on both initial and current occupational status, highlighting the enduring nature of intergenerational inequality. Individuals from more advantaged families are more likely to obtain higher-status occupations at labor market entry and maintain these advantages over time, suggesting that early structural advantages continue to shape long-term career trajectories (Becker et al., 2018).
The findings provide important insights into the behavioral mechanisms underlying the transmission of advantage. Consistent with the New Human Capital Theory, cognitive and non-cognitive abilities significantly moderate the relationship between family background and occupational outcomes. This indicates that the translation of family resources into labor market success is not automatic but depends on individuals’ behavioral capacities, including how they perceive opportunities, make decisions, and mobilize available resources. In this sense, family background provides the “potential, ” while individual abilities determine the efficiency with which this potential is realized (Zhang, 2020). Specifically, digital skills, appearance investment, and key personality traits (agreeableness, openness, and conscientiousness) significantly strengthen the positive association between family background and occupational status. Digital skills enhance individuals’ ability to access information, adapt to technological changes, and expand career opportunities, thereby facilitating the effective utilization of family resources. Appearance investment, as a form of strategic self-presentation, improves perceived professionalism and social evaluation in workplace interactions, contributing to greater career recognition (Deng et al., 2020). Meanwhile, personality traits shape behavioral tendencies that are closely linked to labor market success: agreeableness facilitates cooperation and social support, openness enhances adaptability and innovation, and conscientiousness promotes self-discipline and goal-oriented behavior. Together, these factors illustrate that behavioral and psychological characteristics play a crucial role in amplifying the advantages associated with family background.
In contrast, the moderating effects of extraversion and neuroticism are not statistically significant. This finding suggests that not all personality traits function equally in the process of advantage transmission. Extraversion, while beneficial for social interaction, may not necessarily translate into sustained career advancement, particularly in contexts where structured skills and professional qualifications are more important than social expressiveness. Neuroticism, characterized by emotional instability and stress sensitivity, may exert a generalized negative influence on career outcomes rather than specifically interacting with family background.
Overall, this study demonstrates that occupational inequality is shaped not only by inherited family resources but also by individuals’ behavioral capacities to utilize these resources effectively. These findings highlight the importance of incorporating cognitive and non-cognitive dimensions into the analysis of social mobility and suggest that policies aimed at promoting equal opportunity should not only address structural disparities but also foster the development of individual capabilities. In the context of contemporary China, this dual perspective provides a more comprehensive understanding of how family background and individual behavior jointly shape career outcomes.

6. Practical Implications

Based on the analysis and research above, we can conclude that family background has a significant positive influence on an individual occupational status, and that cognitive and non-cognitive abilities play a moderating role in this relationship. These findings offer several important implications from three perspectives: the individual, the family, and policymakers.
First, at the individual level, it is essential for individuals to consciously develop and enhance their cognitive and non-cognitive abilities (Kröger et al., 2024). This includes improving digital literacy, cultivating positive personality traits (agreeableness, openness and conscientiousness), and investing in personal image management. Such internal capacities strengthen professional competitiveness, optimize the utility of family resources in occupational advancement, and serve as a partial buffer against the disadvantages of lower-status family backgrounds.
Second, from the family perspective, families especially those with fewer resources advantages should be encouraged to invest not only in normal educational resources, but also in the development of their children’s soft skills and broader personal competencies. Cultivation of digital skills, personal traits and self-management abilities can provide individuals with a more robust foundation for navigating future career challenges. In addition, creating a supportive family environment that values learning, adaptability, and self-development can help maximize the long-term impact of family inputs.
Finally, these findings also offer implications for policymakers and educators. Interventions aimed at reducing employment inequality should not only address disparities in family resources, but also focus on enhancing individuals’ capacity to make use of those resources effectively. On the one hand, taking measures that can help individuals compensate for disadvantages stemming from limited family backgrounds. For example, increasing investment in education in economically underdeveloped areas can improve access to quality educational resources for disadvantaged groups. This includes building schools, providing modern teaching equipment, and offering financial aid and scholarships (Tzannatos, 1999). On the other hand, programs that provide career preparation and support the development of soft skills can serve as effective tools for narrowing opportunity gaps and promoting more equitable occupational mobility across different social groups.

7. Limitations and Directions for Further Research

This study has several limitations that should be acknowledged. First, the mechanisms through which family background influences employment outcomes remain underexplored. The research does not provide a comprehensive examination of how family background affects occupational status, leaving potential pathways inadequately tested. Future studies could employ methods such as surveys to investigate these mechanisms in greater depth, particularly how family background shapes individuals’ behavioral patterns and cognitive frameworks, thereby influencing their career trajectories. For instance, existing research indicates that individuals from lower social classes are more susceptible to impostor syndrome, a psychological pattern in which they doubt their accomplishments and fear being exposed as frauds, leading to a lack of confidence during job searches and potentially poorer employment outcomes (Phelan, 2024). This phenomenon is also observed from the employer’s perspective. For example, hiring managers may perceive candidates from working-class backgrounds as exhibiting less disjoint agency, such as lower levels of confidence and decisiveness in simulated interviews, making them appear less hirable (Sharps & Anderson, 2021).
Second, the study primarily relies on the father’s educational attainment as the measure of family background, overlooking maternal variables. In particular, the independent influence of the mother’s education and occupational status on children’s educational and career development has received limited attention, having been examined only in robustness checks. This omission does not imply that the mother’s role is unimportant. Rather, it reflects the conventional approach in much of the existing literature, which often treats the father’s socioeconomic status as a more representative indicator of overall family background. Commonly used paternal variables include employment status, workplace characteristics, occupation type, and educational attainment, which are considered key factors influencing offspring’s occupational status. Future research should compare the distinct effects of maternal and paternal background on children’s career outcomes, thereby offering deeper insights into parental differences in intergenerational mobility.
Third, the study lacks exploration of situational-level moderating variables. While cognitive and non-cognitive abilities are identified as positive moderators at the individual level, situational factors that may alter the relationship between family background and career outcomes remain unexamined. Future research could investigate such moderators. For instance, parental marital status may influence how effectively family resources promote career development. Recent evidence suggests that children of highly educated parents may experience unique disadvantages in human capital accumulation when parental divorce occurs, as the disruption can outweigh the benefits of self-efficacy and interrupt the intergenerational transmission of human capital advantages (Andric et al., 2024). Job type represents another potential moderator. Individuals from lower social strata are often perceived as more communal, demonstrating traits such as warmth, kindness, and helpfulness that facilitate social bonding. Conversely, those from higher strata are typically viewed as more agentic, exhibiting independence and assertiveness that support task accomplishment. Consequently, individuals from less advantaged backgrounds might achieve better outcomes in communal occupations where their perceived social strengths are valued, while those from more privileged backgrounds may excel in autonomous professions that reward their perceived competence and agency.

Author Contributions

Conceptualization, M.W.; methodology, Y.L.; software, Y.L.; validation, Y.Z.; formal analysis, Y.L.; writing—original draft preparation, Y.L.; writing—review and editing, Z.W & M.W. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 72072014.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
Variable Mean SD Min Max
Family background 2.048 1.085 1 6
Occupational status 34.614 14.892 19 88
Digital use 0.624 0.484 0 1
Digital awareness 3.292 1.593 1 5
Appearance investment 5.233 2.437 0 11.00
Agreeableness 3.815 0.595 1 5
Openness 3.194 0.841 1 5
Extraversion 3.336 0.698 1 5
Conscientiousness 3.865 0.628 1 5
Neuroticism 2.960 0.724 1 5
Age 44.147 13.003 16 70
Gender 0.573 0.495 0 1
Hukou 0.229 0.420 0 1
Education 3.034 1.448 0 1
Marriage 0.803 0.398 0 1
Region 0.406 0.491 0 1
Note. N=27306. Descriptive statistics based on cross-sections from the survey-years 2018 and 2020.
Table 2. Regression results of family background on occupational status.
Table 2. Regression results of family background on occupational status.
ISEI Initial ISEI
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Family background 0.468***
(6.44)		 0.473***
(6.50)		 0.371***
(4.75)		 0.370***
(4.74)
Age -0.144*** (-21.60) -0.138***
(-20.30)		 -0.136***
(-19.93)		 -0.099***
(-13.76)		 -0.092***
(-12.53)		 -0.092***
(-12.55)
Gender -1.164***
(-8.30)		 -1.122***
(-8.01)		 -1.135***
(-8.10)		 -2.308***
(-15.46)		 -2.286***
(-15.31)		 -2.284***
(-15.30)
Hukou 5.064***
(27.72)		 4.915***
(26.67)		 4.902***
(26.61)		 4.370***
(22.18)		 4.253***
(21.43)		 4.255***
(21.43)
Education 5.131***
(83.29)		 5.010***
(79.14)		 5.030***
(79.26)		 4.886***
(74.05)		 4.807***
(70.69)		 4.803***
(70.41)
Marriage -0.310
(-1.57)		 -0.354
(-1.79)		 -0.338
(-1.71)		 -0.688**
(-3.10)		 -0.700***
(-3.16)		 -0.701***
(-3.17)
Region 1.0100***
(7.75)		 1.067***
(7.53)		 1.059***
(7.48)		 0.310*
(2.05)		 0.279
(1.84)		 0.280
(1.85)
Constant
 25***
(62.40)		 23.929***
(56.00)		 24.016***
(56.15)		 23.976***
(54.18)		 23.213***
(49.22)		 23.180***
(49.00)
Year-fixed No No Yes Yes Yes Yes
Observations 27306 27306 27306 21208 21208 21208
R2 0.1125 0.4178 0.4182 0.102 0.398 0.398
Note. * p<0.05, ** p<0.01, ***p<0.001. T-statistics are reported in parentheses.
Table 3. Robustness test using mother’s education.
Table 3. Robustness test using mother’s education.
ISEI Initial ISEI
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Family background
(Mother’s education)		 0.493***
(5.53)		 0.493***
(5.54)		 0.326***
(3.33)		 0.326***
(3.33)
Age -0.146***
(-21.39)		 -0.138***
(-19.70)		 -0.136***
(-19.29)		 -0.097***
(-13.26)		 -0.092***
(-12.21)		 -0.092***
(-12.24)
Gender -1.16***
(-8.09)		 -1.110***
(-7.73)		 -1.127***
(-7.85)		 -2.327***
(-15.25)		 -2.291***
(-14.98)		 -2.288***
(-14.96)
Hukou 5.055***
(27.18)		 4.881***
(25.89)		 4.867***
(25.82)		 4.290***
(21.40)		 4.184***
(20.62)		 4.186***
(20.63)
Education 5.140***
(81.94)		 5.055***
(78.33)		 5.077***
(78.47)		 4.920***
(73.04)		 4.863***
(69.99)		 4.858***
(69.71)
Marriage -0.289
(-1.43)		 -0.274
(-1.36)		 -0.256
(-1.27)		 -0.642**
(-2.84)		 -0.627**
(-2.78)		 -0.629**
(-2.79)
Region 1.129 ***
(7.80)		 1.086***
(7.49)		 1.078***
(7.44)		 0.327*
(2.12)		 0.302
(1.95)		 0.303
(1.96)
Constant 24.778 ***
(60.63)		 23.898***
(54.52)		 -0.631***
(-4.43)		 23.833***
(52.97)		 23.243***
(48.08)		 0.135***
(0.89)
Year-fixed No No Yes No No Yes
Observations 26427 26427 26427 20502 20502 20502
R2 0.4158 0.4165 0.4169 0.3969 0.3973 0.3973
Note. * p<0.05, ** p<0.01, ***p<0.001. T-statistics are reported in parentheses.
Table 4. Robustness test using father’s occupational status.
Table 4. Robustness test using father’s occupational status.
ISEI Initial ISEI
Model 1 Model 2 Model 3 Model 4
Family background
(Father’s occupational status)		 0.065***
(3.26)		 0.068**
(3.09)
Age -0.082**
(-3.17)		 -0.090***
(-3.46)		 -0.093***
(-3.48)		 -0.105***
(-3.69)
Gender -2.896***
(-5.47)		 -2.863***
(-5.42)		 -2.828***
(-5.03)		 -2.351***
(-3.94)
Hukou 2.500***
(3.76)		 2.075**
(3.07) 		3.205***
(4.58)		 2.265**
(3.00)
Education 6.197***
(27.91)		 6.063***
(26.92)		 5.217***
(22.02)		 5.989***
(23.40)
Marriage 1.374*
(2.08)		 1.465*
(2.22) 		0.516
(0.75)		 1.456*
(1.98)
Region 1.564**
(2.93)		 1.462***
(2.74)		 0.815
(1.46)		 1.461*
(2.46)
Constant 18.256***
(13.01)		 17.103***
(11.85)		 22.075***
(14.80)		 17.493***
(10.73)
Observations 1990 1990 1576 1576
R2 0.4276 0.4306 0.3904 0.4240
Note. * p<0.05, ** p<0.01, ***p<0.001. T-statistics are reported in parentheses.
Table 5. Moderating effects of digital skills and appearance investment.
Table 5. Moderating effects of digital skills and appearance investment.
Variables ISEI
Model 1 Model 2 Model 3 Model 4
Family background 0.473***
(6.50)		 0.310***
(0.074)		 0.335***
(0.074)		 0.382***
(0.073)
Digital use 2.772***
(0.186)
Family background×
Digital use		 1.082***
(0.152)
Digital awareness
 0.811***
(0.053)
Family background×
Digital awareness
 0.304***
(0.044)
Appearance investment 0.489***
(0.030)
Family background×
Appearance investment		 0.125***
(0.027)
Control variables Yes Yes Yes Yes
Year-fixed Yes Yes Yes Yes
Observations 27306 27306 27306 27306
R2 0.418 0.422 0.423 0.423
Note. * p<0.05, ** p<0.01, ***p<0.001. T-statistics are reported in parentheses.
Table 6. Moderating effects of Big Five personality traits.
Table 6. Moderating effects of Big Five personality traits.
Variables ISEI
Model 5 Model 6 Model 7 Model 8 Model 9
Family background 0.468***
(0.073)		 0.445***
(0.073)		 0.476***
(0.073)		 0.483***
(0.073)		 0.464***
(0.07)
Agreeableness 0.300
(0.116)
Family background×
Agreeableness 		0.225**
(0.106)
Openness 0.694***
(0.083)
Family background×
Openness 		0.253***
(0.076)
Extraversion
 0.328***
(0.098)
Family background×
Extraversion		 0.052
(0.090)
Conscientiousness 0.517***
(0.112)
Family background×
Conscientiousness 		0.213*
(0.101)
Neuroticism
 -0.409***
(0.097)
Family background×
Neuroticism		 -0.044
(0.087)
Control variables Yes Yes Yes Yes Yes
Year-fixed Yes Yes Yes Yes Yes
Observations 27306 27306 27306 27306 27306
R2 0.418 0.419 0.418 0.418 0.418
Note. * p<0.05, ** p<0.01, ***p<0.001. T-statistics are reported in parentheses.
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