Evaluating the Impact of the One-Child Policy on Children’s Entrepreneurship
—New Empirical Evidence from CFPS Data

Longlong Duan, Jie Huang^*

School of Economics, Sichuan University, Chengdu, China.

DOI: 10.4236/aasoci.2025.157036                

Abstract

As an important part of China’s basic national policy of family planning, the one-child policy, which has been in force for more than 30 years, has created a large one-child group whose unique growth background and family structure have had a far-reaching and long-lasting impact on China’s economic and social development. However, the existing studies on the intergenerational effects of the one-child policy have mostly focused on education, income, investment, savings, and other issues, but have not examined the issue of entrepreneurship. This paper empirically examines the impact of the one-child policy on children’s entrepreneurial decision-making and its mechanism of action using the cohort double-difference method based on the data specific to the 2010 China Family Tracking Survey (CFPS). The study finds that 1) The one-child policy significantly reduces the probability of children’s entrepreneurship. 2) Mechanism analysis shows that the one-child policy inhibits children’s entrepreneurial decision-making through two main paths: one is to increase children’s education level and strengthen the awareness of risk aversion, which increases the opportunity cost of entrepreneurship; and the other is to inhibit children’s social capital accumulation, which leads to the lack of entrepreneurial information, the ability to acquire resources and the ability to share risks. 3) Further analysis shows that the impact of the one-child policy on children’s entrepreneurial decision-making is geographically heterogeneous between urban and rural areas, as well as between East, Central, and West China. This paper enriches the research in the field of family planning policy effects and provides important policy insights and empirical support for encouraging children to engage in entrepreneurship in the context of the continuity of the childbearing policy.

Keywords

Family Planning, One-Child Policy, Child Entrepreneurship, Cohort Double-Difference Approach

1. Introduction

Entrepreneurship is an important force that promotes knowledge diffusion, accelerates technological innovation, raises employment levels, and stimulates economic growth (Pugsley & Sahin, 2019; Neneh, 2019). In recent years, China has continued to improve the entrepreneurship support policy system to promote the high-quality development of innovation and entrepreneurship. The report of the 20th Party Congress specifically emphasized the importance of “improving the modern enterprise system with Chinese characteristics and promoting entrepreneurship”, and took “supporting the development of small, medium, and micro enterprises” as a major strategic arrangement to promote high-quality economic development. In the new stage of economic development, in the face of the employment pressure brought about by the post-Singapore pneumonia epidemic and the economic slowdown beyond expectations, the Third Plenary Session of the 20th CPC Central Committee further emphasized the need to “optimize the policy environment for entrepreneurship and employment promotion, and to support and regulate the development of new forms of employment”. This series of policy arrangements shows that entrepreneurship and innovation have become an important engine for promoting economic transformation and upgrading and realizing high-quality development, and play an irreplaceable role in maintaining the stability of the employment market and activating the vitality of market development. Therefore, an in-depth examination of the influencing factors of entrepreneurial behavior has important academic and practical value.

As one of China’s basic national policies, the family planning policy has been continuously adjusted and optimized in its historical evolution. Since the founding of New China, the development of China’s family planning policy can be roughly divided into four stages (Qin et al., 2018; Zhang et al., 2023): That is, in the first stage (1949-1953), the State encourages childbearing in order to restore production; in the second stage (1954-1978), the family planning policy is formally put forward, and the State publicizes birth control through various instructions but does not restrict it through In the third stage (1979-2001), family planning was elevated to the status of a law and a basic state policy, with a shift from the previous encouraging policy of “late, sparse, and few” to the mandatory “one-child” birth control; in the fourth stage (2002-present), the population is aging, and the State is encouraging births to resume production. In the fourth stage (2002 to the present), the problem of population ageing became apparent, and the State continued to adjust its fertility policies: from “two children alone” to “two children across the board” to the liberalization of “three children”.

It is particularly noteworthy that in the late 1970s, China began to implement the One-Child Policy, and the concept of “eugenics” was gradually promoted and implemented throughout the country. The long-term implementation of this policy has created a large one-child group, whose unique background and family structure have had a profound and lasting impact on China’s economic and social development. Currently, with the change of time, most of the one-child children have grown up to be the mainstay of the labor market, but the existing research has not yet paid attention to the substantive impact of the one-child policy on children’s entrepreneurship, for the government departments, to clarify the effect of the one-child policy on children’s entrepreneurship in the long term implementation of the one-child policy, on the one hand, can be assessed for the long-term economic and social effects of the one-child policy to provide a micro perspective of the empirical evidence basis, to help policymakers For government departments, clarifying the effect of the one-child policy on children’s entrepreneurship can, on the one hand, provide a micro perspective for evaluating the long-term economic and social effect of the one-child policy, and help policy makers grasp the effect of the policy more comprehensively and accurately, and provide a decision-making reference for optimizing and adjusting the future population policy. On the other hand, it also helps to assess the advantages and difficulties of only-children in entrepreneurial activities, so as to scientifically formulate more targeted entrepreneurial support policies, stimulate the entrepreneurial vitality of this large group, and optimize the entrepreneurial ecology in China.

For this reason, this paper adopts the cohort DID method to assess the impact of the one-child policy on children’s entrepreneurship, in order to more clearly identify the causal effects of the policy, to enrich the research in related fields, and to provide empirical evidence for the relevant organizations to formulate and adjust their policies. The rest of the paper is organized as follows: the second part is the literature review; the third part is the theoretical mechanism of the impact of the one-child policy on children’s entrepreneurship; the fourth part is the research design; the fifth part is the empirical results; and finally, there are conclusions and policy recommendations.

2. Literature Review

1) Study of the effects of the one-child policy

The current academic research on the effects of the one-child policy covers a wide range of areas, which can be mainly categorized into three dimensions: the demographic dimension, the economic dimension, and the social dimension. In the demographic dimension, it mainly involves population size and structure (Tao & Yang, 2011), fertility behavior (Wang et al., 2016), etc.; in the economic dimension, it mainly involves economic growth (Zhang & Wang, 2014), family income (He & Huang, 2017), consumption (Liu et al., 2016a), savings (Zhou & Yin, 2011), etc.; and the social dimension on unemployment (Ren, 2017), income gap (Wu & Su, 2018), and residents’ happiness (Wang et al., 2013).

In terms of the effects of the one-child policy on offspring, Becker and Lewis (1973) pioneered the Quality-Quantity Trade-off Theory (QQTOT), which suggests that there is a significant substitution effect between quantity and quality of offspring under the constraints of family resources. In the context of family resource constraints, there is a significant substitution effect between the quantity and quality of offspring. Specifically, when the total amount of family resources is constant, an increase in the number of children will lead to a decrease in the average resource input per child, thus generating a resource competition effect among children. Based on this theoretical framework, academics have conducted argumentative studies from multiple dimensions to explore the effects of family planning policies or the number of siblings on the education, income, investment, and savings of the offspring. In the education dimension, Zhong and Dong (2018) use multiple microdata sets to find that the educational resources received by individuals are crowded out by the presence of siblings. Qin et al. (2018) find a significant positive effect of the one-child policy on the level of education received by offspring, but the effect is characterized by significant regional heterogeneity, and only holds significantly in low-income and less credit-developed regions. At the income level, Liu and Wei (2016), based on the 2010 CFPS data, find that family planning improves the education level of the offspring, but weakens the social capital income effect brought by siblings, and the policy does not have a significant impact on the personal income of the offspring under the combined effect. At the investment level, Niu et al. (2020), based on the 2013 CHIP data, find that the smaller the number of siblings, the lower the probability and share of investment in risky assets. At the savings level, Zhou (2014), basing their analysis on 2006 CGSS data, find that the reduction in the average number of siblings in a household due to population policy explains at least one-third of the increase in the total savings rate of urban Chinese households.

2) Study of factors influencing children’s entrepreneurship

In terms of the factors influencing children’s entrepreneurship, existing research generally believes that the choice of entrepreneurial behavior is the result of the combined effect of multiple factors, and through literature combing, it can be found that the current academic community mainly examines the relevant influencing factors from the level of the policy environment, the family background, and personal characteristics. First, from the level of policy environment, economic uncertainty (Yang et al., 2024), institutional environment (Wu & Wang, 2015), and government regulation (Chen, 2015) will have a significant impact on entrepreneurial behavior. Second, from the family background, family structure (Yang et al., 2017), family social network, and mobility constraints (Hu & Zhang, 2014), etc., will affect the entrepreneurial decisions of families. Third, the individual’s gender (Liu et al., 2013), education level (Zhai & Huang, 2020), financial literacy (Yin et al., 2015), and other factors will significantly affect the individual’s entrepreneurial behavior.

In related studies focusing on the factors influencing offspring entrepreneurship, the current academic community mostly focuses on family wealth and structure, social capital, and education level. In terms of family wealth, Ju (2020) used 2018 CFPS data to analyze and found that the more family wealth an individual has, the greater the likelihood of entrepreneurship. In terms of family structure, Zhu and Yang (2018) assessed the impact of old-age support on children’s occupational choices based on the 2012 CGSS data and found that the “burden effect” of old-age support is stronger than the “support effect”, which reduces the probability of children’s entrepreneurship. In terms of social capital, Shittu (2014) showed that family social networks can play a positive role in their children’s entrepreneurial process, helping them to acquire all kinds of information, knowledge, resources, and abilities needed for entrepreneurship. In terms of education level, Zhai and Huang (2020) empirically analyzed based on cross-period CGSS data and found that the longer the individual’s education, the weaker the entrepreneurial willingness, and the inhibitory effect of men’s education on entrepreneurial behavior is weakened compared to women’s.

3) Study on the relationship between the one-child policy and children’s entrepreneurship

Longitudinally, the implementation of the one-child policy in China has led to a significant increase in the proportion of only children. Some studies have begun to explore the one-child group and entrepreneurial activities in the research framework. Although not many, they also have some reference value. For example, some scholars have examined the impact of the experience of only children on their entrepreneurial willingness and found that, compared with non-only children, the only-child group shows stronger self-confidence in employment choices, and their entrepreneurial willingness is more significant (Zhou, 2009), but there are a few scholars who hold the opposite view. For example, Li and Guo (2021) found that they have a significant advantage over non-only children in terms of access to educational resources, and that their higher level of education increases the opportunity cost of entrepreneurship while enhancing employment competitiveness; similarly, there is also a study by Sun et al. (2016), who argued that the probability of entrepreneurship for only children is significantly lower than that of non-only children due to the lack of sibling support and lack of appropriate social networks in the former group. Support and lack of corresponding social network boost (Li & Wu, 2017). Further, Lin et al. (2020), based on data from a survey on the current status of entrepreneurship in rural China, found that the only-child group showed lower entrepreneurial propensity, and that this inhibitory effect was significantly reinforced by parents’ risk-averse tendencies, with mothers’ influence being particularly prominent (Zhang et al., 2023).

By combing through the literature, it is not difficult to find that the results of the current research on the effects of the one-child policy, the factors influencing children’s entrepreneurship, and the combination of the one-child policy and children’s entrepreneurship have made some progress. However, existing studies lack the identification of mechanism transmission in assessing the effect of the one-child policy on children’s entrepreneurship, while there is still some disagreement about the effect of the one-child policy on children’s entrepreneurial behavior. Therefore, the marginal contributions of this paper are: first, to focus on the intergenerational entrepreneurial effects of the one-child policy, and to re-identify and validate the theoretical controversy over the entrepreneurial effects of the one-child policy. Second, from the perspective of causal identification, this paper further considers the endogenous robustness issue and adopts the newer cohort DID method to reassess the impact of the one-child policy on children’s entrepreneurship, and identifies the policy causal effect more cleanly. Third, from the perspective of mechanism, this paper takes an alternative approach to test the intrinsic mechanism of the one-child policy on children’s entrepreneurship from the dimensions of education level and social capital, which makes up for the inadequacy of the theoretical black box.

3. Theoretical Analysis and Hypothesis

1) Direct effects of the one-child policy in discouraging children’s entrepreneurship

It has been shown that the one-child policy restricts the number of children in a family, causing the proportion of one-child families to increase (Tsui & Rich, 2002; Sun et al., 2016), and the reduction in the number of children in a family will have a dampening effect on children’s entrepreneurship. At the children’s level, only-children shoulder heavier family responsibilities, rely on themselves in supporting the elderly and raising children, and need to bear the economic and social pressure brought about by entrepreneurial failure, which makes individuals face higher opportunity costs and psychological burden in entrepreneurial decision-making; at the parents’ level, due to the single number of children, parents, based on the psychology of risk avoidance, are more inclined to choose the high stability of the career path for their children, and will be oriented through economic support. At the parents’ level, due to the single number of children, parents are more inclined to choose stable career paths for their children based on risk avoidance psychology, and they will directly intervene in their children’s career choices through economic support orientation and emotional persuasion, reducing their entrepreneurial intentions.

Therefore, this paper proposes hypothesis H1: the one-child policy has a dampening effect on children’s entrepreneurial intentions.

2) Mediating effects of the level of education

According to the Q-Q substitution theory, there is a significant competitive distribution of educational inputs among children within families. A large number of domestic and international studies have found that children who are only tended to have a higher level of education than non-only children. Under the condition that the total amount of family resources is established, the increase in the number of children will lead to a decrease in the per capita resource possession, which in turn will have a dilution effect on the quality of education. This resource allocation mechanism enables only children to receive more concentrated educational inputs, creating an “only child advantage”. In general, individuals with more siblings have lower educational attainment (Zhong & Dong, 2018). As the number of children in a family rises, the educational resources available to individual children show a decreasing trend (Kugler & Kumar, 2017).

In addition, the effect of education level on entrepreneurial decision-making can be transmitted through the ability facilitation effect and the risk inhibition effect. From the perspective of the ability-promoting effect, individuals with a higher education level usually have stronger information gathering, resource integration, and management ability, which will increase the probability of entrepreneurship (Ni & Lu, 2016). From the perspective of the risk inhibition effect, highly educated laborers are usually more risk-averse and are more inclined to choose stable occupations, i.e., the longer the years of education of laborers, the more significant the risk aversion tendency (Chen, 2011). Currently, among the many employment choices, entrepreneurship clearly belongs to the riskier choices (Hmieleski & Corbett, 2006). And the signaling theory¹ further emphasizes the enhancing effect of increased education level on the competitiveness of only children in the formal job market, and this career choice advantage objectively raises the opportunity cost of entrepreneurial behavior, thus inhibiting their entrepreneurial intentions (Li & Guo, 2021). In summary, education level has an overall negative effect on the probability of entrepreneurship, i.e., the risk-inhibiting effect is stronger than the ability-promoting effect.

Therefore, this paper proposes Hypothesis H2: The one-child policy inhibits entrepreneurial decision-making by increasing the level of education of children.

3) Intermediation effects of social capital

According to social capital theory, social capital promotes entrepreneurship through three main aspects: first, opportunity identification, social networks provide information access and learning and communication opportunities, helping entrepreneurs discover and assess market opportunities (Cai et al., 2018); second, financing support, based on the trust of the relationship network such as friends and family, industry contacts, etc., can provide entrepreneurs with financial support, alleviate liquidity constraints, and reduce the cost of financing (Wen et al., 2023); Third, resource integration, social networks can provide in-depth support and heterogeneous resources in terms of technology, talent, etc., which directly affects industry entry and strategic choices.

Compared with non-only children, only children usually have a lower level of social trust, and their social relationship networks are relatively more limited (Li & Guo, 2021). On the one hand, parents may over-indulge their only child, and this over-indulgence tends to make the only child lack initiative and form self-centered thinking and behavior patterns (Sun et al., 2016), and may lead to a lack of communication and teamwork due to a lack of interaction with siblings at an early age (Minuchin, 1974). These personality traits developed at an early age will continue to play a role in the individual’s growth process, leading to lower social trust, which will eventually have an impact on their employment preferences, social interactions, and attitudinal perceptions in adulthood, limiting their entrepreneurial ability. On the other hand, a social network is an important channel through which to obtain resources. Only-children naturally lack a kind of “social insurance”, i.e., support from siblings, and also have limited access to social network resources (Sun et al., 2016). The relatively simple social network structure of only-children not only hinders their access to diversified entrepreneurial resources, but also makes it difficult to achieve effective diversification of entrepreneurial risk, which largely restricts their entrepreneurial practice.

Therefore, this paper proposes Hypothesis H3: The one-child policy inhibits entrepreneurial decision-making by suppressing children’s social capital accumulation and thus entrepreneurship.

Figure 1 summarizes the theoretical framework of how the one-child policy affects children’s entrepreneurial decisions.

Figure 1. Impact pathway diagram.

4. Research Design

1) Data sources and processing

This paper mainly uses the 2010 China Family Tracking Survey (CFPS) data to conduct the study. This dataset collects information at the individual, household, and community levels, and covers 25 provinces/municipalities/autonomous regions across China, with a target sample size of 16,000 households, including all family members in the sample households. The reason for choosing 2010 survey data is that, compared with other annual survey data, only this year’s data directly asked the sample for specific information on the “number of siblings”, which can directly identify whether the sample is an “only child” or a “non-only child”. The reason for choosing the 2010 survey data is that only this year’s data directly asked the sample for specific information on the “number of siblings”, which can directly identify the sample’s status as “only child” or “non-only child”, and provide data support for this paper to study the impact of the one-child policy on children’s entrepreneurship.

Further, we adopt the following data screening process: first, according to the data provided by the China Labor Statistics Yearbook 2010, the age range of private employers and self-employed workers in China is mainly 20 - 49 years old², and with reference to the study by Zhang et al. (2023), in order to ensure that individuals have entrepreneurial ability, this paper retains samples whose birth years are in the 1960-1990 time period (i.e., those in the 2010 survey); then, samples with missing data on the number of siblings and basic demographic characteristics are excluded. As a result of these processes, 10,969 samples were retained.

2) Modeling

In 1980, the Central Committee of the Communist Party of China (CPC) published an open letter to all members of the Communist Party and the Communist Youth League on controlling the growth of China’s population, calling for “each couple to have only one child”. In the same year, article 12 of the Marriage Law stipulated: “Both husband and wife have the obligation to practise family planning.” Therefore, this paper sets 1980 as the time of the impact of the one-child policy, and individuals born after 1980 will be affected by the policy in terms of their upbringing. This paper further divides the sample into only-child and non-only-child groups by the indicator of “being an only child”. The main reason for this division is that if an individual was an only child at the time of the 2010 survey, it indicates that the constraints of the one-child policy are stronger, while non-only-child status may indicate that the constraints of the policy are weaker, for example, if the implementation of the one-child policy for ethnic minorities is weaker, or if some families still choose to overgrow their children or to steal their children, and so on.

Further, this paper draws on the studies of Duflo (2001), Liu et al. (2016b), and Qin et al. (2018) to utilize the differences in the dimensions of “being an only child or not” and “birth year in 1980 and after” to The cohort double difference model (Cohort DID) is constructed for the cross-section. The specific model is shown below:

$entr{e}_i={\beta }_0+{\beta }_{1}onlychil{d}_i\times cohor{t}_i+{\beta }_2\text{'}Z\text{'}+{\delta }_c+{\lambda }_b+{ϵ}_i$ (1)

where$entr{e}_i$ is whether or not individual i is an entrepreneur; $onlychil{d}_i$ is whether or not individual i is an only child; $cohor{t}_i$ is whether or not individual i’s birth year is 1980 and later; $Z\text{'}$ represents the set of all individual-level control variables;${\delta }_c$ and ${\lambda }_b$ are province and birth cohort fixed effects, respectively; and ${ϵ}_i$ is a randomized perturbation term.

3) Definition of variables and descriptive statistics

1. Explained variables

The core explanatory variable in this paper is individual entrepreneurial decision making ($entr{e}_i$ ), drawing on Li, Zhu, & Fu (2017), based on the questionnaire question, “Which organization do you mainly work for now?”, with the answer of “running your own business” assigned a value of 1, and 0 otherwise. It should be noted that the entrepreneurship defined in this paper includes both self-employment entrepreneurship and corporate entrepreneurship.

2. Explanatory variables

The core explanatory variable consists of the interaction term of two binary variables: 1) whether the individual is an only child ($onlychil{d}_i$ ). This variable is identified by the question “How many siblings do you have?” and is assigned a value of 1 if the answer is “0” and a value of 0 if the answer is “0”. 2) Whether or not the individual was born in 1980 or later ($cohor{t}_i$ ). This variable is identified by the year of birth of the sample in the questionnaire, and is assigned a value of 1 if the year of birth is 1980 or later, and 0 otherwise.

3. Control variables

In terms of control variable selection, we refer to the ideas of Sun et al. (2016) and Zhang et al. (2023) and choose to include gender (gender), marital status (marriage), health status (health), political appearance (party), and ethnicity (ethnicity) as control variables.

4. Descriptive statistics of main variables (Table 1)

Table 1. Full sample descriptive statistics.

Variables	Sample Size	Average Value	(Statistics) Standard Deviation	Minimum Value	Maximum Value
Entre	10,969	0.143	0.350	0	1
Cohort	10,969	0.241	0.428	0	1
Onlychild	10,969	0.101	0.301	0	1
Gender	10,969	0.531	0.499	0	1
Marrige	10,969	0.874	0.331	0	1
Party	10,969	0.076	0.265	0	1
Health	10,969	4.395	0.836	1	5
Ethnic	10,969	0.907	0.291	0	1

5. Empirical Findings

1) Baseline regression

Table 2 reports the results of the regression of equation (1). Column (1) shows the results without control variables and without controlling for province fixed effects and birth cohort fixed effects. Column (2) presents results without control variables but controlling for province fixed effects and birth cohort fixed effects. Column (3) is the result of including control variables but not controlling for province fixed effects and birth cohort fixed effects. Column (4) is the result of including both control variables and controlling for province fixed effects and birth cohort fixed effects. All estimation results show that the coefficients of the core explanatory variables are significantly negative regardless of whether control variables are added and fixed effects are controlled, which indicates that the implementation of the one-child policy significantly reduces the probability of children’s entrepreneurship, and Hypothesis H1 is verified.

Table 2. Impact of the one-child policy on children’s entrepreneurship.

Variables	Entre
	(1)	(2)	(3)	(4)
Onlychild * Cohort	−0.042***	−0.039***	−0.037***	−0.037**
	(0.012)	(0.015)	(0.013)	(0.015)
Gender			0.083***	0.083***
			(0.007)	(0.007)
Marriage			0.051***	0.056***
			(0.010)	(0.011)
Party			−0.090***	−0.087***
			(0.010)	(0.011)
Health			0.016***	0.012***
			(0.004)	(0.004)
Ethnic			0.059***	0.041***
			(0.010)	(0.013)
Constant	0.146***	0.146***	−0.060***	−0.032
	(0.003)	(0.003)	(0.020)	(0.022)
Province fixed effects	N0	Yes	No	Yes
Birth cohort fixed effects	No	Yes	No	Yes
Observed value	10,969	10,969	10,969	10,969
R2	0.001	0.028	0.023	0.047
Adjustment of R2	0.001	0.023	0.023	0.042

Note: ^***, ^**, and ^* represent significance at the 1%, 5% and 10% levels, respectively, with standard errors for clustering to the individual level in parentheses. Same as below.

2) Parallel trend test

The key to the double difference model setting lies in the need to satisfy the parallel trend assumption: if the one-child policy had not been implemented, the probability of entrepreneurship between the treatment and control groups could not be significantly different in trend. Therefore, this paper further utilizes the event study analysis method to carry out the parallel trend test, and the specific model setting is as follows:

$entr{e}_i={\beta }_3+{\displaystyle \sum _{t\ge -4}^{t\le -4}{\alpha }_t}\times onlychil{d}_i\times cohor{t}_{it}+{\beta }_4\text{'}Z\text{'}+{\delta }_c+{\lambda }_b+{ϵ}_i$ (2)

Referring to the idea of Lv et al. (2023), this paper takes the 1 period before the implementation of the policy as the base period, and combines this part of the observations from the 4 years before the implementation of the one-child policy to the 4 years after the implementation of the one-child policy in a combined test. The results of the parallel trend test shown in Figure 2 indicate that the coefficient estimates of the periods before the implementation of the one-child policy are not significant, indicating that there is no significant difference between the treatment group and the control group before the implementation of the one-child policy, which satisfies the parallel trend hypothesis. While there is a time lag in the effect of the early implementation of the one-child policy, probably because people’s concept of childbearing has not been completely changed at the early stage of the implementation of the policy (Tong et al., 2021), after which the estimated coefficients from the 2nd period onwards are significantly negative, and this trend of change initially suggests that the implementation of the one-child policy has a negative impact on the children’s entrepreneurship.

Figure 2. Parallel trend test.

3) Placebo test

This paper draws on the practices of Duflo (2001) and Chen et al. (2021) to conduct two separate temporal placebo tests, as well as one separate spatial placebo test with a mixed placebo test.

The time-placebo test is organized as follows: first, the sample population of the original control group born in 1960-1979 is divided in half, with those born in 1960-1969 as “pre-treatment” and those born in 1970-1979 as “post-treatment”. “The coefficients on the core explanatory variables are not significant, as shown in Column (1) of Table 3. Second, because the sample population born after 1990 was under 20 years old at the time of the 2010 survey, and generally do not have the ability to start their own business, this paper treats those born in 1991-2000 as “post-treatment” and those born in 1980-1990 as “pre-treatment”, as shown in Column (1) of Table 3, the coefficients of the core explanatory variables are not significant. Therefore, the coefficients of the core explanatory variables are still not significant, as shown in Column (2) of Table 3. The results of the time-placebo test indicate that the decline in the probability of children’s entrepreneurship is not influenced by other random factors, proving the robustness of the findings of this paper.

Table 3. Time placebo test.

Variables	Entre
	(1)	(2)
Onlychild * Cohort	0.001	0.001
	(0.024)	(0.052)
Gender	0.083***	0.087***
	(0.008)	(0.012)
Marriage	0.040**	0.070***
	(0.016)	(0.014)
Party	−0.088***	−0.073***
	(0.012)	(0.025)
Health	0.013***	0.006
	(0.004)	(0.011)
Ethnic	0.024	0.086***
	(0.016)	(0.025)
Constant	−0.005	−0.059
	(0.026)	(0.057)
Province fixed effects	Yes	Yes
Birth cohort fixed effects	Yes	Yes
Observed value	8323	2901
R2	0.048	0.061
Adjustment of R2	0.042	0.047

The idea of the spatial placebo test is as follows: construct fictitious experimental and control groups by random sampling, and then generate fictitious treatment variables and their corresponding interaction terms. In order to enhance the reliability of the placebo test, the above procedure is repeated 500 times. On a theoretical level, if the baseline regression results are indeed caused by the one-child policy, the estimated coefficients of the fictitious interaction terms should not be significantly different from zero. As shown in Figure 3, in the 500 placebo tests, the estimated coefficients are concentrated around the value of 0 and show the characteristics of normal distribution, the true estimates are at the edge of the kernel density function, and the p-value of most of the estimated coefficients is greater than 0.1. The above results show that the benchmark regression results are not caused by the randomness factor.

Figure 3. Spatial placebo test.

Figure 4. Mixed placebo test.

The idea of the mixed placebo test is as follows: A new control group and a new treatment group are constructed by randomly setting the year of birth of the sample and “whether it is an only child” 500 times for the placebo test. The results are shown in Figure 4. In the 500 placebo tests, the estimated coefficients are centrally distributed around the 0 value and show normal distribution characteristics, and the true estimates are at the edge of the kernel density function, while the P value of the majority of the estimated coefficients is greater than 0.1, which is further verified to conclude that the results of the baseline regression are not due to the randomness factor.

4) Robustness tests

1. Shorten the sample window interval

The benchmark regressions in this paper use individuals born in 1960-1990, and here we draw on the practice of Li and Bo (2023) of adjusting the number of sample periods by successively raising the lower limit of the sample window interval by 5 and 10 years, i.e., successively regressing on samples from 1965-1990 and 1970-1990, respectively. Compared with the research design that only uses a single time window, different degrees of shortening of the sample time span can not only reduce the measurement error brought about by a specific sample period, but also effectively exclude other possible policy disturbances during the long-term policy window period. As shown in Columns (1) and (2) of Table 4, the coefficients of the core explanatory variables are still significantly negative, indicating that the one-child policy has a dampening effect on children’s entrepreneurship.

2. Exclusion of other competing assumptions

Next, this paper will exclude samples of self-employment entrepreneurship, samples from provinces with lenient policy implementation, and samples of ethnic minorities, respectively, to enhance the robustness of the conclusions.

At the level of entrepreneurship types, entrepreneurship can be divided into self-employment entrepreneurship and corporate entrepreneurship. In China, self-employment entrepreneurship is often a forced choice for making a living. Therefore, based on the entrepreneurial samples, this paper further excludes self-employment entrepreneurship samples according to the questionnaire question “To which type of institution does your current main job belong,” and conducts regression analysis on samples of corporate entrepreneurship alone. The results are shown in Column (3) of Table 4, indicating that the one-child policy has a significant suppression effect on corporate entrepreneurship.

At the provincial level, the strictness of the implementation of the one-child policy varies from province to province. Referencing the research of Guo et al. (2003) and Li & Lin (2023), who divided regions into three categories by calculating the policy fertility rate³ of each province to reflect the “implementation intensity of family planning policies”, this paper re-conducts regression after excluding samples from the third-category regions⁴ with relatively lenient policies. The results are shown in Column (4) of Table 4, and the suppression effect of the one-child policy on children’s entrepreneurship is still significant.

At the ethnic group level, the one-child policy is more strictly enforced among the Han group, while the State has a relatively liberal family planning policy for ethnic minorities. Different regions have formulated different fertility policies based on factors such as the demographic situation and level of economic development of the local ethnic minorities, with childbearing restrictions often being more lenient—ethnic minority families were generally allowed to have two or more children (Tong et al., 2021). Therefore, this paper excludes the minority samples and regresses the Han sample alone, and the results, as shown in Column (5) of Table 4, show that the one-child policy has a significant entrepreneurial suppression effect on the Han sample.

3. Propensity score matching test (PSM-DID)

This paper uses propensity score matching to further mitigate potential endogeneity problems. The specific approach is as follows: the only-child sample is set as the treatment group, the non-only-child sample is set as the control group, the control variables of the model used in this paper are used as the covariates for matching, the samples are matched period by period with 1:1 nearest-neighbor matching, and the matched data of each period are then merged vertically into a single dataset, which is the dataset required for generating the subsequent regression. The matching results are shown in Table 5, with the standard deviations of the covariates less than 10%, and all t-tests failing to reject the original hypothesis that there are no systematic differences between the treatment and control groups. The regression results are shown in Column (6) of Table 4, and the coefficients on the core explanatory variables are significantly negative, indicating that the conclusions of this paper remain robust.

4. Introduction of other databases

The data used in the benchmark regression of this paper comes from the China Family Tracking Survey (CFPS) database of Peking University, while the China Household Finance Survey (CHFS) database of Southwestern University of Finance and Economics is likewise a well-recognized national micro-survey database. Therefore, this paper adopts the 2011 CHFS data and constructs the model following the same empirical design idea, and the regression results are shown in Column (7) of Table 4, where the coefficients of the core explanatory variables are significantly negative, which further proves the robustness of the conclusions.

Table 4. Robustness test.

Variables	Entre
	(1)	(2)	(3)	(4)	(5)	(6)	(7)
Onlychild *cohort	−0.039**	−0.035**	−0.014**	−0.035**	−0.046***	−0.034**	−0.097***
	(0.015)	(0.015)	(0.006)	(0.015)	(0.015)	(0.016)	(0.010)
Gender	0.088***	0.092***	0.012***	0.084***	0.087***	0.089***	0.035***
	(0.008)	(0.009)	(0.003)	(0.007)	(0.007)	(0.008)	(0.004)
Marriage	0.063***	0.073***	0.003	0.057***	0.058***	0.078***	0.012
	(0.012)	(0.012)	(0.005)	(0.011)	(0.012)	(0.015)	(0.007)
Party	−0.092***	−0.102***	−0.009**	−0.092***	−0.096***	−0.117***	−0.092***
	(0.012)	(0.014)	(0.005)	(0.011)	(0.011)	(0.016)	(0.005)
Health	0.014***	0.013**	0.002	0.012***	0.012***	0.006	0.015***
	(0.004)	(0.006)	(0.002)	(0.004)	(0.004)	(0.007)	(0.003)
Ethnic	0.056***	0.064***	0.005	0.041***		0.032	0.030*
	(0.015)	(0.017)	(0.004)	(0.015)		(0.025)	(0.013)
Constant	−0.054**	−0.065**	−0.002	−0.029	0.007	−0.010	0.053***
	(0.026)	(0.032)	(0.009)	(0.023)	(0.021)	(0.042)	(0.008)
Province fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Birth cohort fixed effects	Yes	Yes	Yes	Yes	Yes	Yes	Yes
Observed value	8942	6513	9578	10,644	9944	8543	7374
R2	0.050	0.057	0.013	0.046	0.050	0.048	0.056
Adjustment of R2	0.044	0.049	0.007	0.041	0.045	0.042	0.048

Table 5. Balance test.

Variables	Brochure	Average value		Standard error (%)	Absolute reduction in standard error (%)	T-test
		Process group	Control group			T-value	P-value
Gender	Unmatched	0.617	0.523	19.0	98.6	5.75	0.000
	Match	0.617	0.615	0.3		0.06	0.951
Marriage	Unmatched	0.675	0.919	−63.6	97.1	−24.56	0.000
	Match	0.675	0.668	1.9		0.36	0.722
Party	Unmatched	0.062	0.034	13.1	59.7	4.51	0.000
	Match	0.062	0.073	−5.3		−1.05	0.295
Health	Unmatched	4.610	4.564	7.8	98.5	2.43	0.015
	Match	4.610	4.611	−0.1		−0.03	0.979
Ethnic	Unmatched	0.963	0.976	−7.6	92.6	−2.54	0.011
	Match	0.963	0.962	0.6		0.12	0.905

5) Mechanism testing

Through the above empirical results, this paper finds that the one-child policy has an inhibitory effect on children’s entrepreneurial decisions. According to the analysis of the previous theoretical mechanism, the one-child policy has an impact on children’s entrepreneurship through two major mechanisms: education level and social capital. Since the mediation effect model cannot solve the problem of endogeneity of the regression of the explanatory variables on the mediating variables, this paper refers to the study of Jiang (2022), focuses on the causal link between the explanatory variables and the mediating variables, and tests the hypothesis H2 and hypothesis H3.

1. Mechanisms for the level of education

This paper constructs the variable of education level (educ) through the sample “highest education”, and Column (2) in Table 6 shows the regression results of the core explanatory variables and education level after adding control variables. The coefficients of the core explanatory variables are significantly positive, indicating that the one-child policy promotes the enhancement of children’s education level, which conforms to the Q-Q substitution theory, and has been in line with the Q-Q substitution theory since the introduction of the one-child policy in China in 1980. Since the introduction of the one-child policy in China in 1980, the proportion of single-child family structure has risen significantly, and this reduction in family size has made it possible to centralize the investment of educational resources, which in turn promotes the overall improvement of the education level of the offspring. And according to Zhai and Huang (2020), the level of education is negatively correlated with entrepreneurial intentions. On the one hand, education enhances an individual’s cognitive ability to recognize entrepreneurial risk, making him or her more rational to avoid uncertainty; on the other hand, the advantage of education expands the space of employment choices, and the existence of high-quality workplace opportunities raises the opportunity cost of entrepreneurship, which prompts the highly educated group to prefer occupational stability to entrepreneurial risk-taking. In summary, it shows that the implementation of the one-child policy has increased the education level of children, thus inhibiting their entrepreneurial decisions, and hypothesis H2 is verified.

2. Social capital mechanisms

In Chinese society, the principle of “social reciprocity” dictates that when families or individuals encounter difficulties or host major events (such as weddings, funerals, or children’s school enrollment ceremonies), members of their social networks typically offer monetary gifts as support. The number of gifts given does not involve specific amounts but represents a ritual of interpersonal obligation, generally independent of income levels. And in general, the more gifts an individual gives out, the more friends and relatives he or she has, and the larger the size of his or her social network, which further reflects the more social capital he or she has accumulated. Therefore, in this paper, “the number of gifts given in the previous year” is used as a proxy variable for social capital (soc), and is logarithmized. Column (4) of Table 6 shows the regression results of the core explanatory variables and social capital after adding control variables. The coefficient of the core explanatory variables is significantly negative, indicating that the one-child policy has a negative impact on children’s social capital accumulation. The reason for this result is that social capital can promote individual entrepreneurship by providing resources such as information and knowledge, financial support, talent and technology. The lack of social capital, on the other hand, implies narrower channels for entrepreneurial information acquisition and learning exchange, stronger mobility constraints, and greater difficulty in introducing talents and technologies, thus inhibiting individual entrepreneurship. In summary, the implementation of the one-child policy inhibits the accumulation of children’s social capital, thus inhibiting individual entrepreneurial decision-making, and hypothesis H3 can be verified.

Table 6. Impact mechanism test.

Variables	Educ		Soc
	(1)	(2)	(3)	(4)
Onlychild * Cohort	2.099***	1.674***	−0.206***	−0.182***
	(0.162)	(0.160)	(0.049)	(0.049)
Gender		0.565***		−0.004
		(0.076)		(0.021)
Marriage		−0.773***		0.313***
		(0.140)		(0.039)
Party		4.740***		0.304***
		(0.127)		(0.038)
Health		0.458***		0.040***
		(0.048)		(0.013)
Ethnic		1.714***		0.334***
		(0.167)		(0.044)
Constant	8.159***	4.632***	2.484***	1.709***
	(0.041)	(0.294)	(0.011)	(0.076)
Province fixed effects	Yes	Yes	Yes	Yes
Birth cohort fixed effects	Yes	Yes	Yes	Yes
Observed value	10,969	10,969	10,969	10,969
R2	0.204	0.303	0.123	0.139
Adjustment of R2	0.200	0.299	0.118	0.134

6) Heterogeneity analysis

1. Urban-rural heterogeneity

In this paper, the interaction term between the dummy variable for urban and rural areas (urban) and the one-child policy (onlychild * cohort) is introduced into the baseline model as the core explanatory variable (onlychild * cohort * urban), and the results are as shown in Column (1) of Table 7: the coefficient of the core explanatory variable is significantly negative. This indicates that there is a difference in the impact of the one-child policy on children’s entrepreneurial decisions in urban and rural areas. Specifically, the one-child policy has a stronger inhibitory effect on the entrepreneurial decision-making of children in urban areas than in rural areas. The possible explanation for this phenomenon is that since 1982, while the State has continued to strictly implement the one-child policy in urban areas, it has gradually relaxed the restrictions on the birth policy in rural areas, allowing one-woman households in rural areas of some provinces, autonomous regions and municipalities to give birth to two children, so children in rural areas are weakerly affected by the one-child policy compared with children in urban areas, and thus the policy has a weaker effect of inhibition on entrepreneurial decision-making than children in towns and cities. Therefore, children in rural areas are less affected by the one-child policy than children in urban areas, and the policy has a weaker effect on their entrepreneurship than children in urban areas.

Table 7. Heterogeneity analysis.

Variables	Urban and rural areas	Eastern part	Central and Western region
	(1)	(2)	(3)
Onlychild * cohort	0.015	−0.016	−0.056**
	(0.030)	(0.020)	(0.024)
Onlychild * cohort * urban	−0.087***
	(0.033)
Urban	0.084***
	(0.008)
Gender	0.084***	0.097***	0.071***
	(0.007)	(0.010)	(0.009)
Marriage	0.059***	0.045***	0.063***
	(0.011)	(0.017)	(0.014)
Party	−0.106***	−0.099***	−0.078***
	(0.011)	(0.016)	(0.014)
Health	0.009**	0.016**	0.010**
	(0.004)	(0.007)	(0.004)
Ethnic	0.032**	−0.025	0.062***
	(0.013)	(0.029)	(0.015)
Constant	−0.052**	0.030	−0.049*
	(0.022)	(0.043)	(0.026)
Test for difference in coefficients between groups		0.085*
Province fixed effects	Yes	Yes	Yes
Birth cohort fixed effects	Yes	Yes	Yes
Observed value	10,969	4767	6202
R2	0.058	0.054	0.045
Adjustment of R2	0.053	0.045	0.037

Note: Component coefficient difference P-values were calculated based on the bootstrap’s Fisher’s Combined Test with 1000 repetitive samples.

2. Analysis of regional heterogeneity

Table 8. Stepwise regression for eastern region samples.

Variables	Entre
	(1)	(2)	(3)	(4)	(5)	(6)
Onlychild * Cohort	−0.014	−0.025	−0.018	−0.017	−0.017	−0.016
	(0.020)	(0.020)	(0.020)	(0.020)	(0.020)	(0.020)
Gender		0.093***	0.094***	0.099***	0.096***	0.097***
		(0.010)	(0.010)	(0.010)	(0.010)	(0.010)
Marrige			0.049***	0.046***	0.045***	0.045***
			(0.017)	(0.017)	(0.017)	(0.017)
Party				−0.098***	−0.099***	−0.099***
				(0.016)	(0.016)	(0.016)
Health					0.016**	0.016**
					(0.007)	(0.007)
Ethnic						−0.025
						(0.029)
Constant	0.159***	0.110***	0.067***	0.074***	0.005	0.030
	(0.006)	(0.007)	(0.016)	(0.016)	(0.033)	(0.043)
Province fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Birth cohort fixed effects	Yes	Yes	Yes	Yes	Yes	Yes
Observed value	4767	4767	4767	4767	4767	4767
R2	0.031	0.047	0.049	0.053	0.054	0.054
Adjustment of R2	0.023	0.039	0.040	0.045	0.045	0.045

In this paper, group regressions are conducted on the samples from the eastern region and the central and western region, and the results are shown in Columns (2) and (3) of Table 7: in the eastern region group, the coefficient of the core explanatory variable (onlychild * cohort) is not significant (the stepwise regression results for the eastern region group are shown in Table 8, where the coefficient of the core explanatory variable remains insignificant), while in the central and western region group, the coefficient of the core explanatory variable is significantly negative. This indicates that there are differences in the impact of the one-child policy on children’s entrepreneurial decisions in different regions. Specifically, the one-child policy has a non-significant inhibitory effect on children’s entrepreneurial decision-making in the eastern region, while it has a significant effect on children in the central and western regions. Possible explanations are: the eastern region has a higher level of economic development, a strong degree of marketization, abundant entrepreneurial opportunities and a higher overall level of social capital, so even if a one-child family is formed under the influence of the one-child policy, it can make up for the lack of a single family’s internal resources through the external active entrepreneurial environment, thus weakening the policy’s inhibitory effect on entrepreneurial decision-making; whereas, in the central and western regions, the economic foundation is weak, there are fewer opportunities for entrepreneurship and most families’ overall social capital is relatively not high. Most families do not have sufficient social capital, and the inhibitory effect of the policy on entrepreneurial decision-making is obvious.

6. Conclusion and Policy Recommendations

In recent years, China has made positive adjustments to its fertility policy. On the premise of adhering to the basic national policy of family planning, it has gradually relaxed the birth restriction and successively implemented the “comprehensive two-child” and “three-child” policies, which have triggered extensive discussions in all walks of life. At the same time, under the current macro-environment of slowing economic growth and a grim employment situation, the promotion of mass innovation and entrepreneurship has become an important topic in the political and academic circles. Looking back at history, what is the impact of the one-child policy implemented in China for more than 30 years on the entrepreneurial behavior of children, and what is its mechanism of action? To address the above questions, this paper, based on the data from the 2010 China Family Tracking Survey (CFPS), tries to use the double-difference method to study the effect of the one-child policy on children’s entrepreneurship and its mechanism, and obtains more conclusions, which are summarized here.

The results of the empirical analysis show that:

First, the one-child policy has a significant inhibitory effect on children’s entrepreneurship, and this paper carries out robustness tests through a variety of methods, and the results show that the research findings have strong robustness.

Second, in-depth research shows that the inhibitory effect of the one-child policy on children’s entrepreneurial behavior is mainly realized through two paths. First, the only child born after the implementation of the one-child policy can obtain a higher level of education, which makes their risk aversion awareness increased, and highly educated groups tend to be more likely to get quality jobs in the job market, this advantage will push up the opportunity cost of their entrepreneurship, and thus inhibit entrepreneurial behavior; Second, the only child born after the implementation of the one-child policy due to parental indulgence and lack of sibling support, tends to present lower social trust and narrower social relationship network, resulting in entrepreneurial information and risk management ability. Second, due to parental indulgence and lack of sibling support, only-children tend to present lower social trust and narrower social relationship networks, resulting in insufficient entrepreneurial information, resource acquisition, and risk-sharing ability, thus inhibiting their entrepreneurial behavior.

Third, this inhibitory effect will show significant heterogeneity due to the differences between urban and rural areas and the characteristics of the eastern, central, and western regions. Specifically, rural areas and eastern regions will significantly weaken the inhibitory effect of the one-child policy on children’s entrepreneurial behavior.

Thus, academically, this study evaluates the policy effects of the family planning policy from a new perspective. Against the backdrop of continuous promotion of the “two-child” and “three-child” policies, it breaks through traditional analytical frameworks such as population aging and labor supply, providing a new explanation for the evolution of childbearing policies from the perspective of entrepreneurship inhibition effects. Specifically, policy adjustments may implicitly consider the economic and social effects of the one-child policy in inhibiting offspring entrepreneurship, supplementing the micro-behavioral logic for understanding the correlation between childbearing policies and economic vitality. Practically, this study also involves how relevant government departments formulate scientific and effective entrepreneurship support policies to stimulate entrepreneurial spirit and innovative potential, providing strong support for achieving the goal of the Chinese path to modernization proposed at the 20th National Congress of the Communist Party of China.

Synthesizing the above analysis and empirical findings, the policy implications of this paper are:

First, to alleviate the risk-averse tendency of families and establish a policy combination of “conceptual guidance and risk-sharing”. In order to address the strong entrepreneurial risk aversion of one-child families, it is necessary to adopt measures in terms of both conceptual guidance and risk sharing. Communities and civil affairs departments can provide entrepreneurial advice to families by organizing entrepreneurial policy interpretation meetings and experience-sharing salons to correct their cognitive bias towards entrepreneurship; the government can jointly develop special insurance with financial institutions and set up a multi-level risk compensation fund to reduce the economic and psychological pressure of entrepreneurship; and entrepreneurial years can be included in the pension points system, and simultaneously optimize the policies of childcare protection, such as providing childcare allowances and building inclusive childcare institutions, etc., so as to alleviate the burden of one-child families in their old-age pension. The Government has also incorporated the number of years spent in entrepreneurship into the pension points system, and simultaneously optimized childcare protection policies, such as providing childcare allowances and building inclusive childcare facilities, in order to alleviate the double burden of old-age pension and childcare on only children and help them start their own businesses.

Secondly, innovation and entrepreneurship education should be further strengthened and deeply integrated into the teaching system of colleges and universities. In order to solve the contradiction of entrepreneurial opportunity cost brought by individual high education, colleges and universities should deepen the reform of the education supply side. In terms of curriculum system, entrepreneurship education should be set as a compulsory credit, build a curriculum module covering basic theories, practical training and cutting-edge directions, and regularly update the curriculum content to meet the market demand; in terms of resource integration, linkage with government and enterprises should be made to build a platform for industry-university-research and application, introduce real projects from enterprises, open up research resources on campus, and organize entrepreneurship events to promote the transformation of achievements; in terms of faculty building, a diversified team should be created, consisting of on-campus faculty members, industry experts and entrepreneurship alumni, through training, training, and entrepreneurial alumni. In the construction of teachers, we create a diversified team composed of on-campus teachers, industry experts and entrepreneurial alumni, enhance the capacity of on-campus teachers through training and posting, invite industry professionals to share their practical experience, and at the same time incorporate the results of teachers’ guidance on entrepreneurship into the assessment, so as to push forward the in-depth fusion of innovation and entrepreneurship education with professional education, and cultivate entrepreneurial consciousness of the highly educated talents.

Thirdly, a multi-level social network cultivation mechanism should be constructed to break down the barriers to entrepreneurial resources for only-children. To address the problems of low social trust and limited social network of only children, we can start from the construction of a trust mechanism and the integration of network resources. On the one hand, build a government-led entrepreneurial credit service platform, use blockchain and other technologies to build entrepreneurs’ credit files, and enhance the foundation of trust in the social network; on the other hand, establish a long-term support mechanism for entrepreneurial mentors, select entrepreneurs with rich entrepreneurial experience and industry experts to form a mentor pool, provide one-on-one guidance for entrepreneurs, and help them accumulate industry experience and social resources; promote industry associations, industrial parks and other entities to jointly build specialized entrepreneurial communities, regularly organize project roadshows, resource docking meetings and other activities, and promote the flow and sharing of technology, capital, talent and other entrepreneurial elements in the community; encourage universities and scientific research institutions to open up their laboratories and scientific research platforms, and provide entrepreneurs with support for technological research and development and transformation of achievements to broaden the channels of access to resources.

Finally, it should be noted that although this paper confirms the influence of the one-child policy on children’s entrepreneurial decision-making through theoretical analysis and quantification, it has not yet involved the in-depth analysis of their entrepreneurial scale and entrepreneurial type, which will be expanded in the following research. Moreover, the one-child policy is only a phase of China’s family planning policy, and future research can further explore the impact of the “two-child” and “three-child” policies on children’s entrepreneurship after the adjustment of the fertility policy, in order to more comprehensively investigate the impact of the family planning policy on intergenerational entrepreneurship. In order to more comprehensively investigate the impact of family planning policies on intergenerational entrepreneurship.

NOTES

¹Proposed by Michael Spence. The theory suggests that when information is asymmetric, individuals (e.g., job seekers) can convey information about their abilities to others (e.g., employers) through observable signals such as academic qualifications, which can help each other to screen and reduce the cost of information screening.

²Source: China Labor Statistics Yearbook 2010, https://www.mohrss.gov.cn/2010/lefte.htm.

³The policy fertility rate refers to the average number of children that women in a region would have throughout their lives if they fully comply with the prescribed childbearing policies.

⁴The third-category regions include six provinces/autonomous regions: Qinghai, Hainan, Yunnan, Xizang Autonomous Region, Ningxia Hui Autonomous Region, and Xinjiang Uygur Autonomous Region.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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