The Effects of Parental Migrant Work Experience on Labor Market Performance of Rural-Urban Migrants: Evidence from China

Abstract

With the development of China’s economy and the deepening of urbanization, the number of migrants whose parents have migrant work experience continues to rise. However, what is the long-term impact of parental migrant work experience on migrant children? Existing literature has not yet adequately answered. Based on the data from the China Migrants Dynamic Survey in 2016–2017, this article uses a multiple linear regression model to examine the impact of parental migrant work experience on the income of rural-urban migrants and its impact mechanism and heterogeneity empirically. We find that parental migrant work experience has a positive impact on the monthly income of second-generation rural-urban migrants. Specifically, compared with those whose parents had no such experience, the average monthly income of those whose parents had such experience increased significantly by 3.08% (approximately 124 yuan), and this effect was more apparent when fathers had migrant work experience. The main influencing channel comes from the significant increase in the probability of rural-urban migrants choosing self-employment. The results of the heterogeneity analysis showed that this effect was more significant in the sample of males and those with high school education and below. After a series of robustness tests, these conclusions remain valid. This work enriches the corresponding research literature and provides empirical evidence for studying the long-term effects of parents’ early experiences on their children.

1. Introduction

Migration is integral to the contemporary social, political and economic world [1,2] and migrants make significant contributions to the urbanization and economy of their host countries [3,4]. Over the past few decades after the reform and opening up, large-scale internal rural-urban migration has driven unprecedented economic growth and urbanization in China [5,6]. At the same time, rural-urban migrants have become a particularly important group in Chinese society, drawing constant attention from policymakers and academics. As the older generation of migrants continues to return to their hometowns, the new rural labor force continues to move to large cities for better development. In this process, the phenomenon of intergenerational transmission of migrant workers has occurred, and the scale of the second generation of rural-urban migrant workers continues to expand [7,8]. Compared with their parents, the second-generation migrant workers have some distinctive characteristics. They have higher educational attainment, entered the city at an earlier age, and have less exposure to agricultural production activities, and are more closely connected with cities [9]. Some scholars have tried to explore whether the intergenerational transmission of migrant workers will affect the development and choice of the second generation of migrant workers, and found that parental migration experience has an intergenerational effect on their residence intentions [10] and employment status [11]. The accumulation of resources through the migration of parents can support the offspring to enter the city and realize citizenization through intergenerational transfer. However, achieving higher income is one of the core pursuits of migrant workers entering the city. What effect does the parents’ migration have on the labor market performance of the second generation of rural-urban migrant workers? How is this effect achieved? Do these effects vary by their personal characteristics? These important issues have so far not been explored in detail.

In view of this, the main objective of this paper is to use a multiple linear regression model and ordinary least squares (OLS) to empirically study the impact of parental migrant work experience on the labor market performance of the second generation of rural-urban migrants, identify the influence mechanism of such experience, and verify the heterogeneity based on individual traits. Our study contributes to three important aspects. First, this paper enriches the literature on the impact of parental migration on children’s outcomes. Most of the relevant literatures mainly reveal the short-term impact of parents’ migrant work experience on children, and several studies on the long-term impact focus on the differences between the children of migrants and those of non-migrants. Based on the national large-scale sample survey data, we take the rural-urban migrants as the research object to analyze the intergenerational impact of parental migration, which is an effective supplement and extension to existing research. Second, we provide evidence from the transmission of family resources for relevant literature and policies that promote full employment of migrant workers, and help to understand differences within this group. Third, using propensity score matching (PSM) and instrumental variable (IV) to alleviate the endogeneity problem in the model estimation, we clarify the causal identification and improve the accuracy and effectiveness to a certain extent.

The rest of this paper is structured as follows: Section 2 provides an overview of migration in China and a theoretical analysis. Section 3 presents the data and estimation strategy used in this paper. The empirical results are presented in Section 4. Section 5 discusses the findings of similar studies and provides suggestions for future research. Section 6 draws conclusions and makes policy implications.

2. Theoretical Analysis

2.1. Migration in China

China introduced the household registration (hukou) system in cities in 1951, not at that time to control population migration but to maintain economic and social order. In 1955, the Chinese government extended the hukou system to the whole country, and in the following two years successively issued four documents restricting and controlling the blind inflow of rural population into cities. Since then, the hukou system has been strictly enforced, which requires each person to be registered with only one place of residence and divides the population into agricultural (rural) and non-agricultural (urban) sectors [12,13]. For a long time, this system has led to a huge urban-rural division and the welfare of rural residents is far less than that of cities [14].

After the 1980s, on the one hand, with the advancement of reform and opening up and the deepening of market economic reforms, China’s urban industry has developed rapidly, which has led to a huge demand for cheap labor. On the other hand, the reform of the household contract responsibility system has greatly improved agricultural productivity and released a large number of laborers in rural areas. As a result, large-scale laborers holding rural hukou have entered the cities to work. Figure 1 shows that migrants in China have grown substantially. China’s seventh national census in 2020 shows, that the number of internal migrants has reached nearly 376 million (about 27% of the total population). That is, on average, migration is common for one in four households. Nonetheless, rural migrants are denied access to the same benefits and opportunities as urban residents. As rural hukou holders, they cannot enjoy urban welfare schemes such as social security, public health care, and education [9]. Their children are also blocked from the cities and become rural left-behind children.

Following the rise of rural-urban migration, a large body of literature has explored topics such as the determinants of migration, social integration, willingness to settle, and the impact of migration on left-behind children [15]. It is worth noting that the migrants themselves are also constantly changing. According to the 2016 and 2017 China Migrants Dynamic Survey (CMDS) conducted by the National Health Commission of the People’s Republic of China, it is a common and extensive experience for their parents to migrate to work for more and more rural-urban migrants, especially for those who were born in the late 1990s (see Figure 2), which means that many rural people become migrants after their parents.

2.2. Parental Migration and Children’s Development

The literature on the multi-dimensional effects of parental migration on their children is very rich, and scholars have mainly focused on the short-term impacts on education [16,17,18,19,20], health [6,21,22], and well-being of school-age children [23,24]. The findings are mixed for any country. Some scholars believe that the remittances sent by parents are helpful in improving the welfare outcomes of their children [25,26,27]. Parents migrate to work and make the family’s economic situation better, which is helpful to more investment in children’s education and health, and improves their learning and living conditions, thus having a positive effect on their development. This is in line with the assumptions of population migration theory regarding family migration decisions. However, in general, the positive effects of income are unlikely to offset the negative impact of parents working outside the hometown on children’s educational performance and physical and psychological development. The “ignorance effect” caused by the absence of parents dominates the negative impact, and the lack of complete family care and companionship is not conducive to children’s education, nutrition, mental health, and so on [28,29,30,31,32]. Obviously, these studies mainly concentrated on the effects of parental migration on children at school age. A few scholars have recently considered the long-term impacts of parental migration on left-behind children, finding that parental past migration increases children’s expenditure per capita and the probability of intergenerational mobility [33]. Furthermore, a study using nationwide data from the Philippines showed that it has an overall positive impact on education, employment performance, and emotional state of children left behind [34], but little attention was paid to how parental migration influences children’s income, employment, and living standards for the long haul.

To sum up, the effects of parental migration on children are not one-dimensional and this mobility experience also affects children’s performance in adulthood. Theories of intergenerational persistence suggest that parental abilities, traits, behaviors, and outcomes are transmitted to the offspring [35,36,37]. A large number of researchers have been extensively focused on the intergenerational transmission of multiple aspects of parental education, earnings, social status, and skills [38,39,40]. Migration, as a pattern of behavior, may likewise result in intergenerational transmission. When children also migrate for a better future, parental migration will have an intergenerational effect [41]. In the context of the strong altruism and mutual aid relationship of Chinese families, the migration behavior will change the original endowment and development status of the family, so that the family can maximize their overall interests. Therefore, the migration behavior of the parents may change the development of the family, interact with the cultural capital, social capital, and economic capital in the family endowment, and influence the economic performance of the children of the second generation of rural-urban migrants [10]. Specifically, parents will provide their children with direct material or non-material resources, depending on the family’s varying capital endowment capabilities, to enable them to better interact with the labor market in the city to their advantage [42]. First, parental migration helps their children to come into contact with the ideology and lifestyle of the urban population earlier, and to understand the employment environment in the city. Second, the mobility of parents extends the original social network to cities, which will be transformed into children’s social capital and help them alleviate information asymmetry in the labor market. Third, the transfer of wealth accumulated by the parents’ out-migration can reduce the economic burden of their children and provide economic guarantees for them to achieve better employment in the city.

2.3. Factors Affecting Labor Market Performance of Migrants

The existing literature includes many useful explorations on the factors that affect the employment performance of migrants. Some scholars are concerned about the role of national systems or policies, such as the household registration system [43,44]. However, the role of the national system on the income of the migrants is usually macroscopic and difficult to measure, and the migrants with different characteristics are affected differently. The impact of immigration source, immigration time, ethnicity, and assimilation has also received significant attention [45,46,47,48]. Other studies analyzed the impact of language and other skills on the labor market performance of migrants [49,50,51]. In contrast, the analysis of factors affecting such labor market performance lacks the perspective of family and ignores the intergenerational transmission of parental migrant work experience, and few articles analyze the impact of the accumulation and transmission of parental migrant work experience on the income of rural-urban migrants. What kind of occupation a person is engaged in directly affects the level of income of employees, and the first-end characteristics such as family background affect people’s occupational status acquisition through self-caused factors [52]. Parental migrant work experience, which is an innate feature, may affect their children’s income by shaping their children’s career choices.

Moreover, scholars have noticed some changes in the choice of employment forms for Chinese rural-urban migrants. Specifically, many rural migrants in China, on the one hand, face the labor market pressure of unemployment, discrimination, damage to rights and interests, poor working conditions, and limited career advancement in the wage sector [53]. On the other hand, among the rural-urban migrants, there are obvious differences between employees and employers and self-employed persons in terms of labor style, economic situation, and social status [54], and the social status of self-employed groups is obviously superior to that of employee groups. Self-employed migrant workers are more likely to achieve the accumulation of resources, the upward mobility of social status, and the promotion of professional class, which means better economic, social, and psychological development [55]. Many migrants actively choose to become self-employed for higher returns, more flexible working hours and upward personal development [56,57], and access to family support would significantly increase the likelihood of this choice [58]. Rural-urban migrants with higher social and physical capital have a higher probability of self-employment, and the help of family members can play a considerable role in their selection [59]. Additionally, parents have a deeper appreciation for the obstacles in general employment and when their children enter the labor market as “second-generation rural-urban migrants,” they are more inclined to support children to choose more flexible and free forms of self-employment. From this perspective, migrant workers with parental migration experience may be more likely to choose the form of self-employment because they can inherit various resources accumulated by their parents.

Based on the above analysis, this paper proposes the following research hypotheses:

Hypothesis 1.

Parental migrant work experience will be positively associated with the labor market performance of rural-urban migrants.

Hypothesis 2.

Parental migrant work experience will positively influence rural-urban migrants’ choice of self-employment, and subsequently, positively affect their labor market performance.

3. Data and Methods

3.1. Data

The data were obtained from the 2016 and 2017 China Migrants Dynamic Survey (CMDS) conducted by the National Health Commission. The CMDS is mainly aimed at the migrants aged 15 and above who have lived in the inflow cities for one month or more and have non-local household registration (hukou). The survey content includes the personal characteristics, family status, employment, mobility trends and willingness to stay, health and public services of migrants, etc. It is carried out once a year and adopts a stratified, multi-stage, and scale-proportional sampling method, covering 31 provinces in China. Overall, the survey enables us to obtain long time-span, variable-rich, and nationally representative mixed cross-section data on the internal migrants in China. We use data from the CMDS in 2016 and 2017 because only these two years contain the variables of interest. We only kept a sample of people who were from rural to urban areas for work. After excluding missing values, 192,917 baseline samples were retained.

3.2. Variables

3.2.1. Outcome Variable

The main indicators to measure labor market performance are: employment, income, and hours of work [45]. Of these, income is the key indicator that represents the final labor market outcome. We use the self-reported monthly income of migrants as the dependent variable, which is determined according to “what was your personal net income in the last month (or last employment)?” in the CMDS questionnaire. We take the logarithm of income to avoid the interference of extreme values.

3.2.2. Explanatory Variable

Parental migrant work experience, judged according to the question “Before you migrated/left for the first time, did your parents have any migrant work/business experience?” in the questionnaire. According to the answer set for this question, this study selects “both parents have,” “fathers have, mothers do not have” and “mothers have, fathers, do not have” to define parents with migrant work experience, and records it as 1. The answer “neither parent” means that parents do not have migrant work experience, and is recorded as 0.

3.2.3. Control Variables

To investigate the effect of parental migrant work experience on the income of rural-urban migrants, referring to other research [50,60], it is necessary to control the inherent characteristics and human capital of individuals that affect the income and employment choices of rural-urban migrants. We control for gender, age, ethnicity, marriage, and education of the individual. Considering the differences in the income levels of different industries, occupation types, and unit nature, we control for a series of individual employment characteristics including occupation type, industry type, and ownership of the employer [61,62]. In addition, we incorporate the fixed effects of inflow counties to control the impact of the inherent characteristics of the economy and the overall environment in different regions on the income of rural-urban migrants.

3.2.4. Mechanism Variable

It can be seen from some studies that engaging in self-employment activities is indeed a feasible way for some rural-urban migrants to enhance their economy and even achieve upward mobility [56,57]. For the second generation of rural-urban migrants, the intergenerational transmission of family resources created by parental migration may make them more likely to choose self-employment than other migrants. Hence, we choose self-employment as the mechanism variable, which is specified as a dummy. If the respondent is an employer or self-employed worker, the variable value is 1; if it is an employee, the variable value is 0.

The detailed definitions and descriptive statistics of the relevant variables are presented in

Table 1. Variable definition and descriptive statistics.

Variable	Definition	Parents Have Migrant Work Experience = 1		Parents Do Not Have Migrant Work Experience = 0
		n	Mean (SD)	n	Mean (SD)
Ln Income	Log of migrants’ net income in the last month (or last employment)	39,653	8.12 (0.87)	153,264	8.06 (0.91)
Gender	male = 1; female = 0	39,653	0.60 (0.49)	153,264	0.60 (0.49)
Education	Elementary school and below = 1; junior high school = 2; high school = 3; junior college and above = 4	39,653	2.50 (0.86)	153,264	2.21 (0.87)
Ethnicity	Han = 1; ethnic minority = 0	39,653	0.92 (0.26)	153,264	0.92 (0.28)
Age	Age of migrants	39,653	29.85 (7.67)	153,264	37.58 (9.49)
Marriage	Married = 1; Unmarried = 0	39,653	0.68 (0.47)	153,264	0.86 (0.35)
Occupation type	Managers, civil servants and clerks = 1; professional technical personnel = 2; business service personnel = 3; production and transportation personnel = 4; migrants with no fixed occupation and others = 5	39,362	3.23 (0.73)	152,272	3.26 (0.69)
Industry type	Primary industry = 1; secondary industry = 2; tertiary industry = 3	39,362	2.60 (0.52)	152,272	2.62 (0.53)
Ownership of the employer	Government organizations = 1; State-owned enterprises = 2; Private collective enterprises = 3; self-employed households = 4; Foreign-funded enterprises = 5; No units and others = 6	39,362	3.79 (1.09)	152,272	3.90 (1.14)
Self-employment	Employers or self-employed worker = 1; employee = 0	38,846	0.36 (0.48)	149,803	0.44 (0.50)

. It can be seen that the samples with parental migrant work experience account for approximately 21% of the total sample. The average monthly income of the samples used is approximately 4000 yuan. The income of second-generation rural-urban migrants is slightly higher than that of the sample without parental migrant work experience. The male sample accounts for 82% of the total sample, the average education level is in the middle and high school stage. The average age of the rural-urban migrants whose parents have migrant work experience is about 30, and the married sample accounts for 68%. The average age of samples whose parents do not have migrant work experience is about 38, and the married sample accounts for 86%. The industries of rural-urban migrants are mainly the secondary and tertiary industries, and the occupations they are engaged in are mostly commercial service industries, production, and transportation. The ownership of their employer is mainly concentrated in two categories: private collective enterprises and self-employed households.

3.3. Estimation Model

The OLS model is used to test the impact of parental migrant work experience on rural-urban migrants’ income, that is, whether parental migrant work experience will bring about the income growth of migrant workers. The basic model is as follows:

$$Ln{Y}_{ijt}=\alpha +\beta P{M}_{ijt}+{X^{\prime }}_{ijt}\gamma +{\eta }_j+{\lambda }_t+{\epsilon }_{ijt}$$

(1)

$Ln{Y}_{ijt}$ represents the logarithm of the last month or last employment income of rural-urban migrants $i$ in the inflow region $j$ in each year $t$. The dummy variable $P{M}_{ijt}$ represents parental migrant work experience and is the core explanatory variable. ${X^{\prime }}_{ijt}$ is a series of control variables, including control variables of migrants’ characteristics (gender, education level, ethnicity, age, marital status) and employment characteristics control variables (occupation type, industry type, and the ownership of employer). ${\eta }_j$ indicates the fixed effect of inflow counties, used to control factors such as geographical location and regional culture that vary from county to county and do not change with time. ${\lambda }_t$ is the year fixed effect, controlling for factors that vary over time but not by county. ${\epsilon }_{ijt}$ is the error term. $\beta$ is the core coefficient of our concern. If $\beta$ is significantly positive after controlling for personal characteristics, employment characteristics, regional fixed effects, and year fixed effects, it shows that parental migrant work experience does have a significant positive effect on the income of rural-urban migrants.

4. Results

4.1. Benchmark Regression Results

The stepwise regression results are reported in

Table 2. Effects of parental work experience on the income of rural-urban migrants.

Explanatory Variables	Explained Variable: Ln Income
	(1)	(2)	(3)
Parental migrant work experience	0.0289 *** (0.0051)	0.0273 *** (0.0051)	0.0308 *** (0.0048)
Gender	0.2841 *** (0.0041)	0.2680 *** (0.0041)	0.2629 *** (0.0040)
Ethnicity	0.0780 *** (0.0079)	0.0470 *** (0.0079)	0.0369 *** (0.0076)
Age	0.0432 *** (0.0016)	0.0460 *** (0.0016)	0.0439 *** (0.0015)
Age squared	−0.0007 *** (0.0000)	−0.0007 *** (0.0000)	−0.0006 *** (0.0000)
Marriage	0.1222 *** (0.0058)	0.1286 *** (0.0058)	0.1243 *** (0.0055)
Education:
Junior high school		0.1334 *** (0.0067)	0.1115 *** (0.0064)
High school/Technical secondary school		0.2191 *** (0.0078)	0.1895 *** (0.0075)
Junior college and above		0.3586 *** (0.0086)	0.3321 *** (0.0086)
Occupation type:
Professional skilled worker			0.0255 * (0.0124)
Business Service Workers			−0.0506 *** (0.0123)
Production and transportation personnel			−0.0451 *** (0.0126)
Unemployed and others			−0.1777 *** (0.0159)
Industry type
Secondary industry			0.5472 *** (0.0279)
Tertiary industry			0.4937 *** (0.0280)
Ownership of employer:
State-owned enterprise			0.1403 *** (0.0129)
Private collective enterprise			0.1769 *** (0.0106)
Self-employed households			0.2078 *** (0.0109)
Foreign-funded enterprise			0.1606 *** (0.0124)
No units and others			0.0886 *** (0.0120)
County fixed effects	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes
Constants	7.1192 *** (0.0390)	6.8754 *** (0.0394)	6.3172 *** (0.0571)
n	192,917	192,917	191,634
R-squared	0.097	0.106	0.125

Notes: Numbers in parentheses are robust standard errors, * p < 0.10, *** p < 0.01.

. Columns 1–3 all control the individual characteristics, such as gender, ethnicity, and age, as well as the inflow county and the year fixed effects. Columns 2 and 3 successively add the education level and various employment characteristics that have a greater impact on the income of rural-urban migrants as control variables. The results of each column in Table 2 are significantly positive at the 1% level, indicating that parental migrant work experience has a significant positive impact on the labor market performance of rural-urban migrants. Specifically, we find that compared with rural-urban migrants whose parents without migrant work experience (Column 3), the monthly income of rural-urban migrants with parental migrant work experience increased by 3.08%, ceteris paribus; that is, the intergenerational transmission of the parental work experience increased the monthly income of the rural-urban migrants by an average of about 124 RMB. These results support Hypothesis 1 of this study. Meanwhile, among the control variables, we can find that the level of education significantly affects the income of migrants. The more educated the migrant population, the higher the return on their income. This is consistent with the existing research on return to education and empirical facts [63]. In addition, it can be seen that men, of Han ethnicity, and married rural-urban migrants seem to be more advantaged in the labor market and have significantly higher incomes.

4.2. Whose Migrant Work Experience Is More Influential?

We further explore the different effects of both parents’, only fathers’, and only mothers’ migrant work on the monthly income of second-generation rural-urban migrants. According to

Table 3. Impact of different parental migrant work experiences on the income of second-generation rural-urban migrants.

Variables	Explained Variable: Ln Income
	Migrant Work_Parents (1)	Migrant Work_Father (2)	Migrant Work_Mother (3)
Parental migrant work experience	0.0289 ***	0.0309 ***	−0.0016
	(0.0057)	(0.0078)	(0.0191)
Control variables a	Yes	Yes	Yes
County fixed effects	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes
Constants	6.3395 ***	6.3929 ***	6.4290 ***
	(0.0418	(0.0433)	(0.0444)
n	178,635	163,332	154,211
R-squared	0.126	0.128	0.128

Notes: ^a Control variables are gender, education, ethnicity, age, marriage, occupation type, industry type, and ownership of the employer. Numbers in parentheses are robust standard errors, *** p < 0.01.

, both parents and only the father with migrant work experience have a significant positive impact on the monthly income of rural-urban migrants (p < 0.01). The mother having migrant work experience has a weak negative effect on the monthly income of rural-urban migrants, but it is not statistically significant. Moreover, the impact of fathers’ migrant work on the monthly income is higher than that of both parents’ migrant work. During the growth of children, fathers’ migrant work can bring higher income to support the family, and the mother’s extensive care and attention can help the child’s positive and healthy development [64,65,66]. When the children become “second-generation rural-urban migrants”, compared with rural-urban migrants with both parents or only the mother with migrant work experience, the “growth advantage” of rural-urban migrants whose fathers have migrant work experience is also reflected in employment and income. As males, fathers have a comparative advantage in the labor market, and when they migrate to cities for work, their families can also promote the accumulation of family resources in the place of household registration and the place of migration. Children can use the family resources accumulated by fathers and mothers in different places through transmission and conversion, which will lead them to obtain more favorable employment conditions, thereby achieving income growth.

4.3. Robustness Test

In the regression model, a series of personal characteristic variables, employment characteristic variables, and county fixed effects of the migrants are controlled to ensure the accuracy of the estimation results. However, it is impossible to control all the variables affecting rural-urban migrants’ income and parental migrant work experience. To solve the possible estimation errors caused by the missing variables, we use the PSM and IV methods for the robustness test.

4.3.1. PSM Test

The core idea of the PSM method is to find a suitable counterfactual control group for the treatment group (in this article, the sample with parental migrant work experience), and then perform quantitative estimation after the sample is matched. We selected gender, education, ethnicity, age, marriage, occupation type, industry type, ownership of the employer, employment forms, inflows to districts and counties, and survey year as matching variables to meet the conditional independence assumption as far as possible to determine more similar control group individuals for the treatment group individuals. Since there are far more individuals in the control group than in the treatment group, the use of one-to-many matching can improve the matching efficiency. We therefore mainly use the one-to-four matching method within 0.01 caliper to estimate (we also tried a variety of matching methods, all of which yielded consistent results). As presented in

Table 4. Comparison of PSM estimation results and main regression results.

Estimation Method	Estimated Coefficient
PSM	0.0267 ***
	(0.0071)
OLS	0.0308 ***
	(0.0048)
n	188,649

Notes: Standard errors in parentheses estimated by PSM are bootstrap standard errors, and robust standard errors are in parentheses estimated by OLS. *** p < 0.01.

, the PSM estimation result (0.0267) is significantly positive, which is very close to the result of the baseline model. This further indicates that parental migrant work experience has a significant positive effect on the income of second-generation rural-urban migrants, verifying the robustness of the benchmark regression results.

PSM estimation also needs to verify the common support condition and balance assumption to ensure the matching quality and reliability of the estimation results. A graph of the density function of the treatment and control groups before and after matching is presented in Figure 3. The propensity score intervals of the treatment and control groups after the matching have considerable overlap, suggesting that most of the observed values shared a common range. Therefore, the overlap assumption is satisfied and we only lose very few samples. In addition, our balance test finds that the standardization bias of each matching variable after matching is reduced to less than 5%, and the t-test results show that the mean difference of most variables after matching is not significant; the t-statistics of all variables are greatly reduced, indicating that the matching is more successful [67]. Second, as presented in

Table 5. Balance test of PSM matching.

	Pseudo R2	Chi-Square	Mean of Bias	B Value (%)	R
Before matching	0.120	23,055.12	18.9	91.3 *	0.69
After matching	0.000	15.75	0.6	2.8	0.98

Notes: Mean bias is the mean value of the standardized deviation. According to Rubin (2001), B < 25%, and if R is within (0.5, 2), it can be considered that the assumption of matching balance is fully satisfied. * p < 0.10.

, the pseudo R2, Chi-square, and deviation mean values of the sample after matching all decrease, the B value is greatly reduced and less than 25%, and the R-value is 0.98, which satisfies the balance assumption well.

4.3.2. IV Test

In this section, we use the urban unemployment rate of the sample’s household registration area in 2001 as an instrumental variable [68], which meets the requirement of relevance and exclusion. On the one hand, the higher the urban unemployment rate in the place of household registration, the more difficult it is for people to achieve non-agricultural employment in the local area, and the greater the possibility of people migrating to find employment opportunities [69]. Given this, the higher the urban unemployment rate in the place of hukou, the greater the possibility of parental migrant work. Furthermore, our independent variable is the parental migrant work experience of the rural-urban migrants, and the use of the historical unemployment rate is necessary to ensure that the IV is correlated with parental migration at that time. China joined the World Trade Organization in 2001. Changes in China’s inter-regional economic development pattern and industrial layout have led to more frequent rural population movements. At this time, the higher the urban unemployment rate in a region, the more likely it is for local residents to migrate to work. On the other hand, as a population outflow place, the urban unemployment rate in the place of hukou has relatively little effect on the income of rural-urban migrants in the cities of inflow, making this IV better meet the exogenous requirements. The results of IV regression are reported in

Table 6. IV estimation results.

Variables	Second Stage Regression: Ln Income	First-Stage Regression: Parental Working Experience
Parental migrant work experience	1.6939 *** (0.1872)
Unemployment rate		2.5386 *** (0.1703)
n	191,633
R-squared	0.1435
Weak instrumental variable test:
Cragg-Donald Wald F-statistics	215.345

Notes: Instrumental variable estimation also controls individual-level characteristics, employment characteristic variables, county fixed effects, and year fixed effects. Numbers in parentheses are robust standard errors, *** p < 0.01.

. The first-stage regression results indicate that the urban unemployment rate in the household registration area in 2001 has a significant positive impact on the parental migrant work experience, and the coefficient is significantly positive, which proves the effectiveness of this instrumental variable. At the same time, the weak IV F-test value (Cragg-Donald Wald F-test value) is relatively large, rejecting the hypothesis of weak IV. The regression results of the second stage reveal that the parental migrant work experience has significantly increased the income of second-generation migrants, which is 1.69, showing the result in Table 2 is robust.

4.4. Mechanism Analysis

The above analysis demonstrates that parental migrant work experience has a significant positive impact on the labor market performance of rural-urban migrants. On this basis, an important question is through what channels parental migrant work experience affects such income. We further examine this mechanism. In terms of theories of intergenerational persistence theory, the material, social, and cultural resources of parents may play a role in children’s choice of form of employment. Judging from the estimated results reported in

Table 7. Mechanism test: Impact of parental migrant work experience on the employment choices of rural-urban migrants.

Variables	Explained Variable: Self-Employment
Parental migrant work experience	0.0256 *** (0.0020)
Control variables a	Yes
County fixed effects	Yes
Year fixed effects	Yes
Constant	−0.4689 *** (0.0141)
n	189,118
R-squared	0.549

Notes: Self-employment is a dummy variable: self-employment is assigned a value of 1; employed is assigned a value of 0. ^a Control variables are gender, education, ethnicity, age, marriage, occupation type, industry type, and ownership of the employer. The numbers in parentheses are clustered standard errors at the county level. Numbers in parentheses are robust standard errors, *** p < 0.01.

, the estimated coefficient is significantly positive at the 1% level. Parental migrant work experience has significantly increased the probability of rural-urban migrants choosing self-employment by approximately 2.6%. In other words, the mechanism analysis reveals that, compared with migrants whose parents have no experience of migrating to work, the intergenerational transmission of parental migrant work experience and accumulated resources makes rural-urban migrants more likely to become self-employed and thus obtain higher economic benefits. This result supports Hypothesis 2.

4.5. Heterogeneity Analysis

4.5.1. Based on Gender

Gender differences generally exist in the labor market [70], and some scholars have confirmed such differences in the income of rural-urban migrants from different aspects, highlighting the fact that the income of male migrants is higher than that of females [71]. We examine gender differences in the income of rural-urban migrants affected by parental migrant work experience.

The results reported in

Table 8. Heterogeneity analysis based on Gender.

Variables	Explained Variable: Ln Income
	Male	Female
Parental migrant work experience	0.0344 *** (0.0062)	0.0221 *** (0.0077)
Control variables a	Yes	Yes
County fixed effects	Yes	Yes
Year fixed effects	Yes	Yes
Constants	6.4774 *** (0.0520)	6.4752 *** (0.0688)
n	114,603	77,030
R-squared	0.119	0.116

Notes: The between-group coefficient test indicates that the two coefficients of are statistically significantly different. ^a Control variables are education, ethnicity, age, marriage, occupation type, industry type, and ownership of the employer. The numbers in parentheses are clustered standard errors at the county level. Numbers in parentheses are robust standard errors, *** p < 0.01.

show that parental work experience has a significant impact on the income of both male and female rural-urban migrants, and the impact on men is higher than that on women. The analysis framework of the influence mechanism is further used to explore the reasons for the formation of gender differences. The results in

Table 9. Heterogeneity analysis based on Gender: Differences in Influence Mechanism.

Variables	Explained Variable: Self-Employment
	Male	Female
Parental migrant work experience	0.0284 *** (0.0026)	0.0207 *** (0.0032)
Control variables a	Yes	Yes
County fixed effects	Yes	Yes
Year fixed effects	Yes	Yes
Constants	−0.4729 *** (0.0187)	−0.4374 *** (0.0227)
n	112,999	76,117
R-squared	0.562	0.545

Notes: The between-group coefficient test indicates that the two coefficients of are statistically significantly different. ^a Control variables are education, ethnicity, age, marriage, occupation type, industry type, and ownership of the employer. The numbers in parentheses are clustered standard errors at the county level. Numbers in parentheses are robust standard errors, *** p < 0.01.

indicate that the influence channels are the same for both men and women. That is, parental migrant work experience significantly increases the probability of rural-urban migrants choosing self-employment, but the impact on men is slightly larger than that on women. A possible explanation is that although the intergenerational transmission of parental migrant work experience has brought more entrepreneurial support from families to both male and female migrants, women are trapped in lower human capital, social capital, and risk tolerance, and it is more difficult to achieve self-employment.

4.5.2. Based on Education

The result of the heterogeneity analysis based on education is reported in

Table 10. Heterogeneity analysis based on education level.

Variables	Explained Variable: Ln Income
	Elementary School and Below	Junior High School	High School/Technical Secondary School	Junior College and Above
Parental migrant work experience	0.0494 ** (0.0190)	0.0307 *** (0.0071)	0.0451 *** (0.0088)	−0.0084 (0.0115)
Control variables a	Yes	Yes	Yes	Yes
County fixed effects	Yes	Yes	Yes	Yes
Year fixed effects	Yes	Yes	Yes	Yes
Constants	6.6786 *** (0.1285)	6.5237 *** (0.0619)	6.1881 *** (0.1211)	6.3315 *** (0.1338)
n	32,239	97,313	39,770	22,199
R-squared	0.158	0.101	0.141	0.213

Notes: ^a Control variables are gender, ethnicity, age, marriage, occupation type, industry type, and ownership of the employer. Numbers in parentheses are robust standard errors, ** p < 0.05, *** p < 0.01.

. We can see that parental migrant work experience has a significant positive impact on the income of rural-urban migrants with a high school education and below. From the perspective of the impact mechanism, for rural-urban migrants with a lower level of education, due to limitations such as low academic qualifications, it is difficult for them to enter industries with higher income, more stability, and better social prestige, and they are at a disadvantage when entering the labor market. They can often only engage in occupations with long working hours, low wages, and poor labor security [72]. Therefore, they are more willing to become self-employed with the support of their families, using the capital accumulated by their parents to migrate to work or do business and then improve their income. In contrast, highly educated migrants with a college degree or above are more likely to engage in wage work [58]. In other words, when the rural-urban migrants’ years of education reach a certain level, they can rely on their own human capital and ability to enter work in higher-income industries. Therefore, the resources accumulated by parents as migrant workers show asymmetry with the formal work in the labor market. It is difficult for the intergenerational experience of parental migrant work experience transmission to bring advantages to the employment and income of migrants with high education levels.

5. Discussion

The effects of parental migration on children’s development have been of wide interest to researchers. Nonetheless, most existing studies have focused primarily on the effects of children’s childhood or schooling stages. In contrast, this study analyzes the long-term effects of parental migrant work experience on their children who become migrants from the perspective of intergenerational transmission, using the rural-urban migrant population in China as the research object. We find that parental migrant work experience has a positive effect on their children’s earnings in adulthood, which is consistent with the findings of several recent studies that focus on the long-term effects of parental migration on their children [33,34]. Previous studies have revealed that the intergenerational transmission of parental resources will affect the income and occupation of children [73,74,75]. For rural-urban migrants whose parents have migrant work experience, they can inherit their parents’ accumulated work experience, employment information, social relationship resources, economic benefits, and other resources [75,76], which can serve as favorable factors in the labor market competition to help them achieve higher-income employment. Furthermore, the pattern of only fathers having migrant working experience has a greater effect. We also find that rural-urban migrants with parents’ migrant work experience have a higher probability of self-employment due to their ability to pass on various capital accumulated by their parents, which is less explored in the existing literature. In terms of heterogeneity effects, the impact of parental migration varies by gender and education of the migrants. Compared with women [77], male rural-urban migrants have a higher utilization rate of resources accumulated by their parents’ migrant labor [78]. For those migrants with education levels of high school and below, they are more positively influenced by their parents’ experience of migrating to work and show a stronger dependence on family resources.

This study also has some limitations that can be made up in the future. First, due to the limitation of data and lack of information on migrants’ parents, we cannot fully analyze the various resources accumulated by parents’ labor to influence the income of rural-urban migrants. It also fails to analyze the impact of parental migrant work experience and parental characteristics on the growth of migrants in depth, which requires more evidence. Future studies with better data could take into account more variables that capture the characteristics of parents and their mobility. Second, the research conclusions are limited to rural-urban migrants, and the impact of parental migrant work experience on the employment and income of the non-migrant population needs to be further explored, which will be the future research direction. Third, this study only discusses self-employment as the mechanism, and it would be valuable to explore other channels in future studies.

6. Conclusions

From the perspective of family resource accumulation and transmission, we combine data from the China Migrants Dynamic Survey (CMDS) in 2016 and 2017, and empirically examine the impact of parental migrant work experience on the labor market performance of rural-urban migrants. The conclusions drawn are the following: First, parental migrant work experience has a positive impact on the monthly income of second-generation rural-urban migrants entering the labor market. The income effect is more significant when only the father has migrant work experience. Second, the mechanism analysis demonstrates that parental migrant work experience significantly increases the probability of rural-urban migrants choosing self-employment and starting a business. Third, the analysis of heterogeneity shows that parental migrant work experience has a greater impact on the income of male rural-urban migrants than women, and such experience has a more significant impact on rural-urban migrants with high school education and below.

Based on the findings, this study draws the following policy implications. The government should pay attention to the differences among the migrants, introduce targeted policies for different types and characteristics of the migrants, and improve policy accuracy. First, it attaches importance to the advantages of second-generation rural-urban migrants’ self-employment and helps them use their own family resources to achieve self-employment and increase income. The intergenerational transmission of parental migrant work experience has made second-generation rural-urban migrants more inclined to choose self-employed forms of employment. Therefore, in the process of new urbanization, the government should actively include second-generation rural-urban migrants as a policy consideration for mass entrepreneurship by relaxing financing constraints, providing key services, releasing household registration restrictions, and so on, to create a good employment and entrepreneurship environment for rural-urban migrants. Furthermore, supporting rural-urban migrants to achieve “promoting employment through entrepreneurship,” and treat self-employment as a viable option to alleviate the employment pressure and improve their income [79]. Second, the government should carry out targeted entrepreneurship and employment training for those rural-urban migrants who have a low level of education but choose self-employment to help them achieve sustainable operations and provide better public social service. Third, the inflowing cities should motivate rural-urban migrants to improve their labor market performance through a good welfare protection system. In addition to providing various employment policies and convenient conditions for self-employment for rural-urban migrants, the government should also share the fruits of urbanization and improve the social security system for self-employed migrant workers.