Introduction

The choice of a college major is a critical decision-making behavior that significantly impacts a student’s educational and career trajectory. This decision is increasingly being shaped by global trends in higher education, characterized by rapid expansion, diversification, and a growing emphasis on the knowledge economy. Recent cross-national analyses have demonstrated that while higher education expansion has improved access, it has also led to new forms of inequality in educational opportunities1. These global trends, coupled with localized socioeconomic factors, create a complex decision-making environment in which students’ academic choices are made.

The theoretical foundation for understanding how students make their major choices can be traced to Bourdieu’s seminal work on cultural reproduction. Bourdieu et al.2 argued that educational choices are not merely individual decisions but are deeply influenced by inherited cultural capital and social dispositions. This perspective remains particularly relevant in understanding how family background shapes educational decision-making in contemporary contexts. Recent empirical evidence using counterfactual analysis has further strengthened this theoretical framework, demonstrating the substantial causal effect of cultural capital on educational inequality3.

Globally, higher education has shifted from an elite to a mass system, a trend evident in both developed and developing countries. This shift has been driven by the growing recognition of higher education as a key driver of economic development and individual social mobility. Rivera’s detailed analysis of how educational choices translate into career outcomes highlights the lasting impact of major selection on social stratification4. As information technology and the knowledge economy continue to evolve, the importance of higher education in fostering innovation and competitiveness has become increasingly apparent.

China’s role in the global landscape of higher education is particularly noteworthy for studying decision-making behaviors. As the world’s most populous country and second-largest economy, China’s higher education system has seen remarkable growth and transformation. This growth is part of the country’s broader strategy to transition from a manufacturing-based economy to one driven by innovation and services. The Chinese government has invested heavily in higher education, resulting in a dramatic increase in enrollment rates and the establishment of numerous universities and colleges, with the gross enrollment rate rising from 40% in 2015 to over 57.8% in 2021.

However, this rapid expansion has also led to new challenges, particularly in terms of educational inequality and complex decision-making scenarios. The urban-rural divide in China is a critical factor in this regard. Urban areas, with their concentration of resources and opportunities, stand in stark contrast to the more resource-constrained rural areas. This divide is not just a feature of China’s educational landscape but is reflective of broader socioeconomic inequalities that are prevalent in many parts of the world.

The significance of studying China in this global context lies in its unique socioeconomic and educational dynamics. As the country continues its transition and its higher education system expands, understanding how these global trends intersect with local realities becomes crucial for analyzing decision-making behaviors. The urban-rural divide and the role of family cultural capital in shaping students’ major choices offer a lens through which the broader implications of these global trends on individual behavior can be examined.

By examining the urban and rural differences in college students’ major choice behavior in China, this study contributes to the broader discourse on how social background and cultural capital influence educational decision-making in rapidly developing contexts. Our research not only adds to the theoretical understanding of educational stratification but also provides insights relevant to policy makers seeking to address educational inequalities in developing contexts.

This investigation is particularly timely given the recent emphasis on educational equity in China’s national development strategies. Understanding how different student groups approach major selection can inform policies aimed at reducing educational inequalities while supporting the country’s transition to an innovation-driven economy. Moreover, our findings contribute to the growing body of international literature on the relationship between social background, cultural capital, and educational decision-making in rapidly changing societies.

Literature review

The concept of family cultural capital is pivotal in understanding educational choices. Bourdieu defined cultural capital as a set of cultural knowledge, skills, education, and advantages that a person can use to gain a higher status in society5. This framework has been extensively used to analyze educational outcomes and choices6. Studies have shown that students from families with higher cultural capital are more likely to pursue majors leading to prestigious careers7,8. In terms of urban-rural differences, research indicates significant disparities in the availability and access to resources. Urban students often have greater exposure to diverse educational resources, including advanced courses and extracurricular activities, which can influence their major choices9. Rural students, on the other hand, may face constraints due to limited educational resources and exposure10. Research also highlights the role of parental influence in major choice. Parental educational background and occupation have a notable impact on students’ educational aspirations and decisions11,12. Rural parents, who often have lower levels of educational attainment, might guide their children towards more practical and immediately lucrative fields, as opposed to urban parents who might encourage exploration of a wider array of academic disciplines13. Recent studies have begun exploring the intersection of family cultural capital and urban-rural dynamics in major choice. In summary, the literature suggests a complex interaction between family cultural capital and the urban-rural divide in shaping college students’ major choices. This research aims to further explore these dynamics, offering a more nuanced understanding of how family background and geographical setting intertwine to influence educational trajectories.

In the current epoch of higher education expansion in China, the spectrum of educational choices extends beyond mere college attendance, gravitating towards prestigious universities and esteemed majors. This evolution reflects a heightened emphasis on major selection as a pivotal concern for numerous students and their families. Opting for an appropriate major is not solely about fostering personal attributes and skills; it also significantly intersects with future employment prospects and earning potential14. Historical developments, including the early prioritization of heavy industry and the establishment of a household registration system after the founding of the People’s Republic of China, have engendered a distinct bifurcation between urban and rural systems15. This dichotomy manifests in comparative advantages for urban residents in terms of income, educational resources, and access to information. Consequently, urban families increasingly view higher education as a conduit for perpetuating their socio-economic status. In contrast, rural residents typically encounter lower income levels and educational attainments, alongside a more limited array of informational sources and channels for social mobility. For many in rural areas, higher education is perceived as a transformative opportunity, a significant step towards achieving substantial socio-economic advancement and altering their life trajectories. In the milieu of a market economy and a fee-based higher education system, rural families often exhibit heightened sensitivity to the risks associated with college admissions and the personal return on investment in higher education16. The inherent heterogeneity between urban and rural students – in terms of family background, educational aspirations, and varying goals of professional training – leads to disparate admission rates and market returns17. Thus, the urban-rural divide potentially exerts a significant influence on students’ major choices, underlining the need to understand these varied backgrounds in the context of higher education decision-making.

There are many family factors that affect children’s major choices. Factors such as parents’ occupation, income and cultural background will all have an impact on children’s major choices18. At a time when the gap between the rich and the poor has eased and the awareness of social justice has generally increased, the role of family culture in the process of children’s professional selection is becoming more and more obvious19. Specifically, when parents have the same education level, although there may be some differences in occupation and income, their collection and cognition of university majors and employment information will not be too different. However, if there are differences in the education level of parents, the family’s intervention and influence on the children’s major choice will be different20. Rochat and Demeulemeester used Heckman’s three-step method to study 641 freshmen in Belgium. The results show that compared with other students, the probability of their parents with higher education degrees in choosing majors such as sociology, economics and humanities in the long-term system was larger than that of ones choosing economics and social sciences in the short-term school system21. Reimer and Polak performed a grouped logistic regression on German survey data and found that compared with students whose fathers only received compulsory education, students whose fathers had a higher education degree chose medicine and law more majors, not social sciences and humanities, this feature reached a significant level in the four-year data group regression; compared to social sciences and humanities, students whose mothers have a higher education degree are less likely to choose than other students For engineering-related majors, this finding is significant in the regression results of the 1990 and 1999 data22.

Affected by the expansion of the scale of higher education, the proportion of rural students on university campuses whose parents have not received a university education has reached a certain scale23. The classification of university students based on the single dimension of urban-rural differences or whether their parents receive higher education may cover up heterogeneity within the group of college students. In this regard, some scholars have begun to distinguish college students from the two dimensions of urban-rural differences and whether their parents have received higher education. When analyzing the impact of financial aid methods on the academic development of the first generation of college students in rural areas, Bao Wei and Chen Yaxiao subdivided the group of college students into rural first-generation college students, rural second-generation college students, urban first-generation college students, and urban second-generation college students. Generation of college students, namely “rural first-generation”, “rural second-generation”, “urban first-generation”, “urban second-generation”24. In view of this, our research combines urban and rural household registration and family cultural background, and divides college students into four categories, namely “rural first-generation”, “rural second-generation”, “urban first-generation”, and “urban second-generation”, employing propensity index and logit regression model to explore the degree of major choice of four types of college students and the influence mechanism of major choice.

The existing literature has made significant contributions to our understanding of how cultural capital influences educational choices and how urban-rural differences shape educational opportunities. However, several important gaps remain in the current literature. First, most studies of cultural capital and educational choice have been conducted in Western contexts, leaving questions about how these mechanisms operate in different institutional settings, particularly in rapidly developing countries like China. Second, while the urban-rural divide has been widely acknowledged as a crucial factor in educational inequality, few studies have systematically examined how it moderates the relationship between cultural capital and educational decision-making. Third, the methodological approaches used in previous studies often treat cultural capital and geographical location as separate influences rather than investigating their interactive effects on educational choices. Our study addresses these gaps by examining how cultural capital operates differently across urban and rural contexts in China’s unique educational landscape. By analyzing the major choices of four distinct groups of students—rural first-generation, rural second-generation, urban first-generation, and urban second-generation—we provide a more nuanced understanding of how family background and geographical location interact to shape educational decisions. This approach not only contributes to the theoretical understanding of cultural capital’s role in educational stratification but also offers practical insights for addressing educational inequalities in developing contexts.

Data source and model construction

Data source

The data of this research comes from a sample survey of colleges and universities across the country conducted by the Peking University Institute of Education’s Higher Education Reform Project Group since 2014. According to the level of colleges (the first-tier colleges, the second-tier colleges, the third-tier colleges), subject distribution (humanities, social sciences, science, engineering, agriculture, medical, etc.) and gender. The original survey data is re-sampled. The new data generated in the three dimensions of colleges, disciplines and gender is consistent with the 2011 Employment White Paper and China Education Statistics Yearbook (2011 The proportions of related data in) are basically the same (as shown in Table 1).

Table 1 Comparison of sample structure before and after resampling.
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Research method

Major tendency index

The major tendency index is a method adapted from previous studies on educational choice patterns19. This index allows for a clear visualization of the relative propensity of different groups to choose specific majors, controlling for group size differences. It is particularly useful in contexts where there are significant disparities in group sizes, as is the case with urban and rural students in China25.

In order to describe and analyze the major choice preferences of college students of “rural first-generation”, “rural second-generation”, “urban first-generation” and “urban second-generation”, this research constructed the major tendency index of four types of student groups, as shown below:

$${\text{major}}\;{\text{tendency}}\;{\text{index}}_{{{\text{ij}}}} = \frac{{{\text{~student}}\;{\text{group}}\;{\text{i}}{\text{proportion}}\;{\text{in}}\;{\text{Major}}\;{\text{j~}}}}{{{\text{Proportion}}\;{\text{of}}\;{\text{type}}\;{\text{students}}\;{\text{i}}\;{\text{in}}\;{\text{the}}\;{\text{overall}}\;{\text{sample}}}}$$

Among them, i represents the four types of student groups, j represents the students majoring. Taking the “rural first-generation” major tendency index as an example, the major tendency of the j major is the proportion of the “rural first-generation” among the students studying in the j major, divided by the proportion of the “rural first-generation” in the overall sample. Also take the “rural first-generation” college students as an example. If their major tendency index for major j is greater than 1, it means that the “rural first-generation” college students tend to choose this major; if it is equal to 1, it means that the “rural first-generation” college students have no obvious preference for this major in tendency and preference; less than 1, it means that “rural first-generation” college students are not inclined to choose this major.

This method provides an intuitive measure of major choice tendencies, facilitating comparisons across groups and revealing patterns that might be obscured in raw percentage comparisons.

Logit regression model

This study will establish logit regression model to further analyze the impact of the four types of student groups on major choice on the basis of controlling family social, cultural, and economic capital variables. The model is as follows:

$$\log it\left( P \right) = \ln \left( {\frac{P}{{1 - P}}} \right) = \alpha + \beta _{1} \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\rightharpoonup}$}}{X} + \gamma \overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\rightharpoonup}$}}{Z} + \cdots + \varepsilon$$

Among them, the dependent variable is a binary variable of whether to study in a certain major. The core independent variables \(\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\rightharpoonup}$}}{X}\) of this model are three binary variables of the four types of student groups. Taking the “rural first-generation” college students as the benchmark group, they represent “whether they are the rural second-generation college students”, “whether they are the urban first-generation college students”, “whether they are the urban second-generation college students”. In terms of control variables, they include gender (male as a reference variable), college entrance examination scores (based on the year of the college entrance examination, liberal arts and sciences, and the percentile system standardized by the college entrance examination scores), and the types of colleges attended (take the third-tier colleges as the benchmark variable), family economic status, father’s occupation, father’s years of education and other variables.

According to the definition of family society, culture, and economic capital, this research will further establish logit regression models for the four student groups of “rural first-generation”, “rural second-generation”, “urban first-generation”, and “urban second-generation” to test whether the influence of factors on the major choice of the four types of student groups is heterogeneous. The model is as follows:

$$\:{log}it\left({P}_{j:1\:}\right)={ln}\left(\frac{P\left(Y=j|X\right)}{P\left(Y=1|X\right)}\right)=\alpha\:+{\beta\:}_{1}socio+{\beta\:}_{2}{cul}_{}+\cdots\:+{\beta\:}_{k}econ+\gamma\:\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\rightharpoonup}$}}{Z} +\epsilon\:$$

The dependent variables are grouping variables of four types of majors, and the humanities major is the reference variable. The core independent variables are family social capital, cultural capital and economic capital. In addition, the model also introduces control variables such as gender, college entrance examination scores, and college level.

Differences in the major tendency of the four types of student groups

Table 2 presents the major tendency index of the four types of student groups at different school levels in the four major disciplines. In the full sample, the “rural first-generation” and “rural second-generation” college students have less than 1 in humanities and social sciences, while the science and engineering, agricultural and medical majors are both greater than 1. Similar trends are also present in different school levels. However, the “rural first-generation” college students studying in first-tier colleges and = have a science major tendency of less than 1, which is 0.75, and the major tendency to study humanities of the “rural second-generation” college students studying in 211 Project universities and third-tier colleges is greater than 1, 1.03 and 1.15 respectively. In contrast, in the full sample, both the “urban first-generation” and “urban second-generation” college students have a tendency of greater than 1 in the humanities and social sciences, and both the science and engineering and agricultural medical majors have a tendency of less than 1. Similar conclusions can be drawn in different school levels. Only the “urban second-generation” college students studying in the first-tier colleges have a science major tendency greater than 1.

In summary, “rural first-generation” and “rural second-generation” college students are more inclined to choose science and engineering, agricultural and medical majors, while “urban first-generation” and “urban second-generation” college students are more inclined to choose humanities and social sciences. The reason may be that the family incomes of the “rural first-generation” and “rural second-generation” college students are lower, so they prefer to choose science and engineering majors with better employment and income prospects; while the family income of “urban first-generation” and “urban second-generation” college students is higher. So, compared with the “rural first-generation” and “rural second-generation” college students, they may prefer to choose the humanities and social sciences that are more interested and more academically prepared.

Table 2 Differences in the major tendency of the four types of student groups.
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Table 3 presents a comprehensive overview of our study participants. The sample consists of a balanced gender distribution with 50.4% female and 49.6% male students. The majority of respondents are underclassmen, with 33.0% freshmen and 27.5% sophomores. Regarding family background, a significant portion (46.0%) comes from rural areas, while the rest are distributed across various urban settings. In terms of parental education, the largest group (38.5%) has fathers with junior high school education, followed by those with high school education (25.0%). The majority of fathers (71.6%) work in basic-level occupations. Family income is predominantly low (60.1%), with fewer middle (28.8%) and high-income (11.1%) families. Lastly, more than half of the students (57.6%) were admitted to their first-choice institution. This diverse sample provides a comprehensive representation of Chinese college students across various demographic and socioeconomic dimensions.

Table 3 Demographic and socioeconomic characteristics of the Sample.
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The influencing mechanism of the major choice of four types of students

Factors influencing the choice of majors of four types of students

This article uses four types of student groups as independent variables and four major types as binary dependent variables to establish a binary logit regression model. The results are shown in Table 4. Under the control of other factors, compared with the “rural first-generation” college students, the “rural second-generation”, “urban first-generation” and “urban second-generation” college students have a higher probability of choosing humanities majors, with 1.317 times, 1.260 times and 1.324 times that of “rural first-generation” college students respectively. “urban second-generation” college students have a higher probability of choosing science majors, which is 1.323 times that of “rural first-generation” college students; “urban first-generation” and “urban second-generation” college students have a lower probability of choosing engineering, agricultural and medical majors, with 0.817 times and 0.689 times that of “rural first-generation” college students respectively; there is no significant difference in the probability of choosing social science majors among the four groups of students.

The college entrance examination results have a significant positive impact on humanities and social sciences, and a significant negative impact on science and engineering, agricultural and medical majors. The college entrance examination scores are increased by one point, and the probability of choosing humanities and social sciences is 1.016 times and 1.006 times the original respectively. The probability of science and engineering, agricultural and medical majors is 0.993 times and 0.998 times the previous ones. Compared with the third-tier colleges, the probability of choosing humanities and social science majors in the first-tier colleges and 211 colleges is significantly lower, and the probability of choosing science and engineering and agricultural medical majors is significantly increased; the probability of choosing humanities and social science majors in the first-tier colleges is 0.414 times and 0.643 times than that of general college, the probability of choosing engineering and agricultural medicine majors is 2.389 times that of ordinary colleges; 211 colleges and universities choose humanities and social sciences is 0.586 times and 0.739 times that of ordinary colleges and universities, choosing science and engineering and agricultural medicine majors the probability of is 1.188 times and 1.696 times that of ordinary colleges and universities.

Compared with disadvantaged types of occupations, children whose fathers are political elites have a higher probability of choosing humanities and social sciences, which are 1.280 times and 1.266 times higher than those of disadvantaged occupations. The probability of choosing science and engineering, agricultural and medical majors is lower. 0.710 times and 0.758 times the disadvantaged type; compared with the disadvantaged type, children whose father’s occupation is an economic elite type have a lower probability of choosing a science major, which is 0.713 times that of the disadvantaged type; compared with the disadvantaged type, the father’s occupation is Children of the general type have a higher probability of choosing humanities and social sciences, which are 1.114 times and 1.143 times that of the disadvantaged types of occupations, and the probability of choosing science and engineering, agricultural and medical majors is lower, which is 0.733 times and 0.916 times of the disadvantaged types of occupations.

The father’s years of education have a significant negative impact on the choice of social science majors, and a significant positive impact on the choice of engineering and agricultural medical majors; for every year the father’s education years increase, the probability of choosing social science majors is 0.988 times higher. It is 1.025 times the previous. Compared with low-income families, low-income families have a significantly lower probability of choosing science majors, which is 0.793 times that of low-income families; compared to low-income families, middle-income families have a significantly higher probability of choosing social science majors, which are low-income families The probability of choosing a science major is significantly lower than that of low-income families, which is 0.801 times that of low-income families. Compared with low-income families, the probability of middle- and high-income families choosing social science majors is significantly higher, which is 1.366 times that of low-income families. The probability of the medical profession is significantly lower, 0.792 times and 0.841 times that of low-income families, respectively. Compared with low-income families, the probability of high-income families choosing social sciences is significantly higher, which is 1.751 times that of low-income families. The probability of major is significantly lower, 0.696 times that of low-income families.

Table 4 Factors influencing the choice of Majors of four types of students.
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Factors influencing major choices of four types of students in the first-tier colleges

The hierarchy of Chinese colleges and universities is obvious, and the characteristics of college students’ major choices at different levels may be different, and the influence of family background on their children’s major choices may be different from the overall situation. Therefore, this article selects the first-tier colleges as the representatives of China’s high-level universities, and analyzes the first-tier colleges separately to explore the influence of the family background of different types of student groups in China’s high-level universities on major choices. It can be seen from Table 5 that, under the control of other factors, Compared with the “rural first-generation”, the probability of “rural second-generation” college students choosing social science majors is significantly lower which is 0.506 times that of the “rural first-generation” college students. The probability of choosing engineering, agriculture and medical majors is significantly higher, 2.246 times that of the “rural first-generation” college students. The probability of the “urban first-generation” college students choosing a science major is significantly higher, which is 1.742 times that of the “rural first-generation” college students. For humanities majors, there is no significant difference in the probability of major selection among the four groups of students. The college entrance examination scores are similar to the overall. For every point increase in the college entrance examination scores, the probability of choosing humanities and social sciences will be reduced to 0.978 times and 0.965 times. The probability of choosing science and engineering, agriculture and medical majors will increase to 1.034 times and 1.043 times.

Compared with students whose parents are professionally disadvantaged groups, students, whose fathers are political elites and cultural elites, have a significantly higher probability of choosing social science majors, which are 1.716 times and 1.645 times that of professionally disadvantaged groups. The probability of choosing engineering, agricultural and medical majors significantly lower, 0.590 times and 0.602 times the occupational disadvantaged groups respectively. Compared with students whose parents are disadvantaged groups, students whose father’s occupation is the general group have a significantly higher probability of choosing social science majors, which is 1.383 times that of the disadvantaged group. The father’s years of education had no significant influence on the choice of major. Compared with students from low-income families, students from middle-income families have a significantly higher probability of choosing social sciences, which is 1.602 times that of students from low-income families, and the probability of choosing science majors is significantly lower, which is 0.593 times that of low-income families; Students from upper middle income families have a significantly higher probability of choosing social sciences, which is 1.665 times that of low-income families; students from high-income families have a significantly higher probability of choosing social sciences, which is 2.713 times that of low-income families. The probability of choosing a science major is significant lower, 0.301 times that of low-income families.

Table 5 Factors affecting major choices of four types of students in the first-tier colleges.
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Differences in influencing factors of major choices among four types of students

In order to further understand the differences in the factors affecting the choice of majors of the four types of student groups, this paper conducts a sample regression for different types of college students, and uses the “humanities major as the benchmark” to establish logit regression models to study the factors affecting their major selection.

Factors influencing the choice of majors of “rural first-generation” student group

Table 6 shows the regression results of the influencing factors of the “rural first-generation” college students’ major choices. (1) Gender has a significant influence on professional selection. Compared with girls, boys are more inclined to choose social science, science, and engineering, agricultural and medical majors, especially engineering agricultural and medical majors. Boys are 1.538 times, 2.023 times and 4.951 times more likely to choose social sciences, sciences, and engineering and agricultural medicine than girls. (2) The higher the college entrance examination score, the higher the probability of “rural first-generation” college students choosing social sciences, sciences, and engineering, agricultural and medical majors compared with humanities majors. For every point increase in the college entrance examination score, the probability of “rural first-generation” college students choosing social sciences, sciences, and engineering, agricultural and medical majors is 0.980, 0.957, and 0.964 times the original. (3) At the university level, the probability of “rural first-generation” college students studying in the first-tier and second-tier colleges in choosing science and engineering, agricultural and medical majors have increased significantly. Specifically, the probability of “rural first-generation” college students in the first-tier colleges choosing science and engineering and agricultural medicine majors is 1.840 times and 4.031 times that of ordinary colleges and universities, and the probability of “rural first-generation” college students studying in 211 colleges and universities choosing science and engineering, agricultural and medical majors It is 1.963 times and 2.000 times that of ordinary colleges and universities. (4) In terms of family social capital, the probability that children whose father’s occupation belongs to the economic elite type chooses science majors is significantly lower, which is 0.495 times that of the father’s occupation belonging to the disadvantaged group; the probability of children whose father’s occupation belongs to the cultural elite type chooses social science majors significant increase is 1.542 times that of the father’s occupation belonging to the disadvantaged group; the probability of children with the father’s occupation belonging to the general type of choosing science and engineering, agricultural and medical professions is significantly increased, which is 1.350 times and 1.245 times that of the father’s occupation belonging to the disadvantaged group. (5) In terms of family economic capital, the probability of choosing social sciences among the “rural first -generation” college students from high income and upper-middle income families is significantly reduced, which is 0.545 and 0.565 times that of low-income families, respectively.

Table 6 “Regression results of factors influencing major selection of “rural first-generation” students.
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The influencing factors of the major choice of the “rural second-generation”

The regression results of the factors influencing the choice of majors of the “rural second-generation” are shown in Table 7. (1) The influence of gender is similar to that of the “rural first-generation”. The number of boys who are likely to choose social sciences, sciences, and engineering, agricultural and medical are 2.096 times, 2.627 times and 5.798 times more than the number of girls who do the same respectively. (2) The impact of college entrance examination results is different from that of “rural first-generation” college students. The improvement of college entrance examination scores significantly reduced the probability of “rural second-generation” college students choosing the major of engineering, agricultural and medial. For each increase in the scores of the college entrance examination, the probability of the “rural second-generation” college students choosing the major of engineering, agricultural and medical is 0.985 times the original. (3) The probability of “rural second-generation” college students studying in the first-tier colleges in choosing social sciences, sciences, and engineering, agricultural and medical majors has increased significantly, which is 2.821 times, 4.464 times and 9.876 times that of ordinary colleges and universities respectively; while those studying in 211 colleges and universities There is no significant difference in the major choices of the “rural second-generation”. (4) Compared with the disadvantaged types of occupations, the probability that the “rural second-generation” college students whose fathers’ occupations belong to the political elite type choosing the engineering, agricultural and medical majors is significantly lower, which is 0.412 times that of the father who belongs to the disadvantaged type; The probability of “rural second-generation” college students choosing social science majors was significantly reduced to 0.405 times that of fathers belonging to the disadvantaged category. (5) Relative to low-income families, the probability of choosing social sciences and engineering, agricultural and medical for the “rural second-generation” of other income families is significantly lower. Specifically, the probability of choosing a social science major among high-income, upper middle-income, middle-income, and lower middle-income college students from “rural second-generation” college students is reduced to 0.618, 0.806, 0.303, and 0.735 times that of low-income families. The probability of “rural second-generation” college students from high-income, upper middle-income, middle-income, and low-income families choosing the major of engineering, agricultural and medical is reduced to 0.193 times, 0.150 times, 0.997 times, and 0.074 times that of low-income families.

Table 7 Regression results of factors influencing major selection of the “rural second-generation”.
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The influencing factors of major choice of “urban first-generation”

The regression results of the influencing factors of the “urban first-generation” student group’s major choice (Table 8) show that (1) the influence of gender is similar to that of “rural first-generation” and “rural second-generation”. Boys are more inclined to choose social sciences, sciences, and engineering, agricultural and medical than girls. The probability that boys choose social science, science and engineering, agricultural and medical is 1.701 times, 2.441 times and 5.171 times that of girls. (2) The increase in college entrance examination scores has significantly reduced the probability of “urban first-generation” college students choosing social science, science, and engineering, agricultural and medical majors. For each increase in the scores of the college entrance examination, the probability of “urban first-generation” college students choosing social sciences, sciences, and engineering, agricultural and medical majors is 0.989, 0.976, and 0.970 times the original. (3) In terms of the level of colleges and universities, the probability of “urban first-generation” college students studying in the first-tier and second-tier colleges choosing social science, science, and engineering, agricultural and medical majors have increased significantly. The amount of “Urban first-generation” college students studying in the first-tier colleges are 1.386 times, 2.686 times, and 4.168 times more likely to choose social science, science and engineering, agricultural and medical majors than those who study in ordinary colleges respectively. The. The probability of “urban first-generation” college students, who choose science and engineering and agricultural medicine as their majors and study in 211 colleges and universities, is 1.793 times and 1.804 times that of students in ordinary colleges and universities. (4) The probability of “urban first-generation” college students whose father’s occupation belongs to the political elite type choosing science majors is significantly reduced to 0.485 times that of their father’s occupation belonging to the disadvantaged type; “urban first-generation” college students whose father’s occupation belongs to the ordinary type choose science majors, the probability of being significantly increased is 1.316 times that of the father’s occupation being a disadvantaged type. (6) The probability of “urban first-generation” college students from high-income and upper middle-income families choosing social science majors is significantly reduced to 0.653 times and 0.642 times that of students from low-income families.

Table 8 Regression results of factors influencing major selection of the “urban first-generation” student group.
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The influencing factors of major choice of “urban second-generation” student group

Table 9 presents the regression results of the factors influencing the major choice of the “urban second-generation” student group. (1) The influence of gender is the same as that of “rural first-generation”, “rural second-generation” and “urban first-generation” college students, boys are more inclined to choose social sciences, sciences, and engineering, agricultural and medical majors than girls do. Boys are 1.765 times, 3.538 times and 5.517 times more likely to choose social sciences, sciences, and engineering agricultural and medical majors than girls do respectively. (2) Compared with humanities majors, the improvement of college entrance examination scores will significantly reduce the probability of “urban second-generation” college students choosing social sciences, sciences, and engineering, agricultural and medical majors. Each time the college entrance examination score increases by one point, the probability of “urban second-generation” college students choosing social sciences, sciences, and engineering, agriculture and medical decreases to 0.990, 0.979, and 0.966 times the original. (3) The probability of “urban second-generation” college students studying in the first-tier and second-tier colleges in choosing social sciences, sciences, and engineering, agricultural and medical majors have increased significantly. The probability of “urban second-generation” college students studying in the first-tier colleges choosing social science, science, and engineering, agriculture and medical majors is 1.456, 2.252, and 4.032 times that of students in ordinary colleges and universities. “Urban second-generation” college students in 211 colleges and universities choose social science, science and engineering, agriculture and medical majors is 1.348 times, 1.943 times and 2.494 times that of ordinary colleges and universities. (4) The probability of choosing science and engineering, agricultural and medical majors for college students whose father’s occupation is a political elite type is significantly reduced, which is reduced to 0.598 and 0.669 times that of students whose father’s occupation belongs to a disadvantaged group. (5) Family income does not have a significant impact on the choice of majors of the “urban second-generation” college students.

Table 9 Regression results of factors influencing major selection of the “urban second-generation” student group.
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Conclusion

This study delves into the influence of varying urban and rural backgrounds on college students’ major choices, utilizing a major orientation index and a multiple logit regression model, yielding several principal conclusions.

Firstly, the study identifies distinct differences in major selections among students from diverse backgrounds. Typically, urban students show a propensity for humanities and social sciences, whereas their rural counterparts are inclined towards science, engineering, agriculture, and medical fields. Regarding cultural capital, students from more educated families, or “second-generation” students, display a preference for humanities and social sciences. They have easier access to active and effective assistance in gathering information about professions, leading them to choose majors like humanities and sciences that align with personal interests and thorough academic preparation. On the other hand, “first-generation” students, with less accumulated family cultural capital, are influenced more by practical concerns such as employment prospects and income, leading them to opt for applied fields like social sciences, engineering, agriculture, and medicine.

Secondly, the study observes that in top-tier colleges, the likelihood of all student groups, except “rural first-generation” students, choosing science, engineering, agriculture, and medical fields is significantly higher, while their preference for humanities and social sciences is lower. This trend is partly attributed to the historical emphasis on science and engineering faculties since 1952. Even comprehensive universities among these top-tier institutions tend to allocate more resources to science and engineering, both for enhancing research and enrollment. Consequently, parents with higher education levels, being more aware of resource distribution within universities, are likelier to encourage their children to pursue science and engineering majors in these prestigious institutions.

Finally, the study highlights the unique characteristics affecting major choices across four types of student groups. Gender consistently impacts all groups, with boys more inclined towards majors in social sciences, science, engineering, agriculture, and medicine compared to girls. The effect of college entrance examination results varies across the groups. Improved scores notably decrease the likelihood of “rural first-generation,” “urban first-generation,” and “urban second-generation” students choosing majors in social science, science, engineering, agriculture, and medicine; and reduce the chances for “rural second-generation” students in selecting engineering, agriculture, and medical fields. The prestige of the university exerts a similar influence on the major choices of “rural first-generation,” “urban first-generation,” and “urban second-generation” students, but affects “rural second-generation” students differently. Specifically, students from the first three categories are more inclined towards majors in social sciences, sciences, and engineering, agriculture, and medicine at top-tier and second-tier universities. For “rural second-generation” students, attending top-tier universities significantly increases their likelihood of choosing these majors.

The study also explores how social capital influences the major choices of these student groups differently. For instance, “rural second-generation” students with fathers in political elite positions are less likely to choose majors in engineering, agriculture, and medicine. Similarly, “urban first-generation” students with fathers in political elite positions are less inclined to science majors, and “urban second-generation” students in similar circumstances show a reduced likelihood of choosing science, engineering, agriculture, and medical fields. In contrast, “rural first-generation” students with fathers in economic elite positions show a decreased probability of choosing science majors; the probability for “urban second-generation” students in similar conditions to choose social science majors is significantly higher. The study also notes no significant difference in the choices of “rural second-generation” and “urban first-generation” students from economically elite backgrounds.

Furthermore, the impact of economic capital on major choices also varies among these groups. Compared to students from low-income families, “rural first-generation” students from high-income and upper-middle-income families are significantly less likely to choose social sciences. The probability for “rural second-generation” students from all income levels, except low-income, to choose social sciences and majors in engineering, agriculture, and medicine is notably lower. Similarly, “urban first-generation” students from higher income backgrounds show a reduced likelihood of opting for social sciences, whereas the major choices of “urban second-generation” students do not significantly differ across different economic backgrounds.

Discussion

As an important manifestation of the horizontal dimension of higher education stratification, university majors play an important role in the intergenerational transmission of classes. On the one hand, restricted major choice is an important manifestation of class inequality in education; on the other hand, professional qualifications, as an institutional design for social exclusion by the dominant class, plays an important role in the intergenerational transmission of class status. The empirical results of this study show that there are obvious urban-rural differences in the major choices of Chinese college students. Urban students are more likely to choose humanities and social science majors, while rural students are more likely to choose science, engineering, agricultural and medical majors. Even considering the impact of gender differences and college entrance examination scores, family background, especially family cultural capital, is still an important factor influencing differences in the major choices of urban and rural children. This study found that the “rural second-generation” and “urban second-generation” college students with higher family cultural capital are more inclined to choose humanities and science majors, while college students of the “rural first-generation” and “urban first-generation” with lower family cultural capital prefer to choose social science and engineering, agricultural and medical majors.

The ability of families in different social classes to master information resources will also affect their children’s major choices. The reasons for the difference between the urban and rural areas of Chinese college students’ major choices may be due to two aspects. On the one hand, the difference in urban and rural areas and the difference in cultural capital caused information asymmetry, that is, the “rural first-generation”, “rural second-generation”, “urban first-generation” and “urban second-generation” of different family backgrounds. The difference in obtaining major information between the first-generation and the second-generation college students has led to the difference in major selection results. Parents and students with better family backgrounds have a wider experience, wider access to information, a clearer understanding of majors, and more planning for future career development, so they are more proactive when choosing majors. Parents and children with poor family backgrounds have relatively single sources of information, and their contact surface is narrow, which affects their major understanding and grasp of future plans. On the other hand, the “soft power” of family cultural capital on children is also an important influencing factor. Family cultural capital often indirectly interferes with children’s major choices through long-term effects on children’s thinking, habits, interests and risk preferences. For families with high cultural capital, parents will use various resources to nurture their children, in a subtle atmosphere, establish their children’s specific hobbies, and improve related professional capabilities. This long-term, invisible, and structural effect on children has a far-reaching influence on the choice of profession and future development. However, for families with low cultural capital, the parents lack major understanding, and the children can get less effective guidance, which leads to stronger limitations in major choice and future career development. It can be said that the gap in family cultural capital is also one of the important reasons for the differentiation of urban and rural students’ major choices.

Our findings on the influence of cultural capital on major choice in China share similarities with studies in other countries, but also reveal unique patterns. For instance, similar to our results, research in the United States19 and France26 found that students from high cultural capital backgrounds are more likely to choose humanities and social sciences. However, our finding that high cultural capital students in top-tier Chinese universities prefer STEM fields differs from these Western studies, highlighting the importance of considering specific national contexts in understanding educational choices.

Existing research conclusions generally believe that family economic capital and social capital are the main reasons for the difference between the urban and rural areas of Chinese college students’ major choices. The results of this study show that the difference in family cultural capital is also an important factor that leads to the differentiation of urban and rural students’ major choices. The main reasons are as follows:

First, the difference in family cultural capital of urban and rural college students is more “significant”. With the advancement of society and the improvement of the level of marketization, the difference in family economic capital and social capital of urban and rural college students has decreased. However, the increase in economic income or social status of individuals or groups does not mean that their cultural level can be improved to a corresponding degree. On the contrary, basic education in rural areas has been relatively weak for a long time, and in order to pursue economic income or social status, rural groups are more likely to give up the opportunity to enter the labor market prematurely, so that their income level and professional status have improved, but the level of education is still quite different from that of urban groups.

Second, the influence path of family cultural capital is relatively “concealed”. Differences in cultural capital can lead to unequal access to education, which also plays an important role in professional selection. Family cultural capital often affects children’s major choices through long-term influences, such as indirectly interfering with their final choice results by influencing children’s personal interests, academic advantages and self-efficacy. With the strengthening of social supervision, it is more difficult for family resources to directly convert into support for children’s education, but the “hidden” role of family cultural capital on children’s major choices may become an important way to influence the major choices of urban and rural college students.

Based on the above research results, it can be found that after the expansion of the scale of colleges and universities, the proportion of “rural first-generation” college students is 54.4%, which has become the vast majority of the source of colleges and universities in my country. Their family background is relatively weak, and they need full attention and strong support from all sectors of society. As the current major volunteer guidance and consulting work in my country’s colleges and universities is relatively lagging, the suggestions of parents and family members still play an important role in the process of children’s professional selection. The “rural first-generation” college students with relatively disadvantaged family backgrounds, especially family cultural capital, lack of higher education experience from their parents. On the one hand, they are unable to obtain the corresponding cultural influence and professional support; on the other hand, it is difficult to obtain effective professional information and suggestionin the process of professional selection. The advice resulted in their major choices considering fewer personal interests and more employment-oriented, which largely limited their major choice space. In order to better promote the fairness of higher education, we should pay more attention to this kind of “concealed” intergenerational inheritance to bridge the “hidden” cultural capital gap between urban and rural children.

We acknowledge that China’s higher education landscape has undergone significant changes since our data collection in 2014. Several important developments may affect the interpretation of our findings. The gross enrollment rate in higher education increased from 37.5% in 2014 to over 57.8% in 2021, representing an even broader participation across social groups. This expansion might have further complicated the relationship between cultural capital and major choice, as more first-generation college students from both urban and rural backgrounds enter universities. Meanwhile, the structural changes in China’s economy and labor market, particularly the rapid development of technology sectors and expansion of service sectors, have created new patterns of employment prospects for college graduates. The COVID-19 pandemic since 2020 has significantly impacted both the education system and labor market, with the unemployment rate among college graduates reaching 19.3% for ages 16–24 in 2023. Additionally, recent policy initiatives promoting rural revitalization and regional development might have altered the opportunity structure for rural students. While our findings remain valuable for understanding the fundamental mechanisms of how cultural capital and urban-rural differences influence major choice, future research should examine how these recent developments have potentially modified the relationship between family background and educational decision-making in China’s changing social context.