How Does Military Experience Affect Employment: Evidence From China

Abstract

The impact of military experience on employment has long been a hot topic of academic discussion, and understanding how military experience affects employment is crucial to promoting the employment of veterans. Based on the 2010 to 2020 China Family Panel Studies data, the article investigates the effect of military experience on individual employment and its underlying mechanism, and overcomes the endogeneity via IV-Probit model. It is found that military experience significantly contributes to employment, specifically through improving the job opportunities in the public sector, increasing the likelihood of holding an administrative position, and enhancing one’s political capital, social capital, and human capital. The findings of this article provide insights into how to deal with the unemployment problem of ex-servicemen and help the relevant authorities to formulate targeted measures to safeguard the employment of veterans.

Keywords

military experience employment veterans public sector administrative positions capital accumulation

The employment of ex-servicemen has always been a matter of great importance to governments. To promote their employment, many countries have formulated corresponding policies on the employment of veterans in accordance with actual situation, so as to ensure a smooth transition from military service to civilian life. Taking the United States as an example, well-known ones include the Transition Assistance Program (TAP) and the Uniformed Services Employment and Reemployment Rights Act (USERRA) launched in the 1990s. The former mainly provides employment and vocational training services for military personnel who are about to leave service, helping them make appropriate educational and vocational choices during the transition period¹ (Faurer et al., 2014). And the latter is aimed at mandatory service employees, guaranteeing their rights to return to employment, get job benefits, and be free from employment discrimination after completing their tour of duty (Klein et al., 2002).

China has similar employment policies for ex-soldiers, but they were proposed later. For example, the Regulations on the Resettlement of Retired Soldiers promulgated in 2011 gives first place to supporting employment, combined with employment based on individual initiative and job placement. The Opinions on the Work of Promoting the Employment and Entrepreneurship of Veterans in the New Era, which was passed in 2018, takes a multi-faceted approach to increasing employment support for veterans. In addition, the Veterans Protection Law of the People’s Republic of China, which came into force in January 2021, legally stipulates the rights and interests of ex-servicemen in employment, such as job search assistance, priority in recruitment, and targeted examination and employment.

An employment survey from Guangdong Department of Veterans Affairs shows that nearly half of more than 3,000 veterans were employed in private enterprises in 2019. A set of data provided by Nantong Bureau of Veterans Affairs of Jiangsu Province suggests that Nantong City received 2017 retired soldiers for independent employment in 2020, of which 59.59% were employed in private enterprises, 5.95% in flexible employment,² and 2.53% in entrepreneurship.³ However, according to data from the 2019 Chinese economic census, at the end of 2018, there were 15,614,000 private enterprises and only 242,000 state-controlled enterprises in the country, implying that the above survey has not yet been able to provide a detailed picture of the employment situation of Chinese veterans.⁴

Since the 1960s, issues related to the employment of veterans have received the attention of Western scholars, who have begun to use different theories to explain the performance of veterans in the labor market (Bordieri & Drehmer, 1984; Cooper, 1981; Spencer & Woroniak, 1969), but few scholars have studied in depth the problem of China, especially the employment effect of military experience. Some researchers have used data on veterans in developed countries to study the effect of military service on employment and have come up with divergent views (Cooper, 1981; Kleykamp & Montgomery, 2014; Puhani & Sterrenberg, 2021), but have failed to elaborate on the mechanisms at play. Studies have found that military experience promotes employment in public sector (Angrist & Chen, 2011; Routon, 2014; Winters, 2018), and that the administrative experience and skills gained in the military are beneficial to ex-servicemen in related careers (He, 2021; J. Wang, 2011; Wu, 2019). Capital accumulation is also a potential factor affecting the employment of ex-soldiers (W. Wang et al., 2020; H. Wang et al., 2023; B. Wang & Yang, 2019). However, these factors have never been discussed in a holistic framework.

Since the founding of the People’s Republic of China, a total of 57 million soldiers have withdrawn from active service, and hundreds of thousands of veterans are newly added every year.⁵ In the course of more than 70 years of construction and reform, employment policies for veterans have continued to be improved and optimized, and the question that needs to be answered is not only what kind of impact military experience has on employment but also what ways to promote the veterans’ employment. Although H. Wang et al. (2023) explored the relationship between military experience and job acquisition based on life-course theory, it is too one-sided to assume that there is a negative correlation only from the perspective of human capital investment, and they were still unable to tackle the endogenous problem, which leads to a lack of credibility in the conclusions drawn. For the above reasons, this article uses data from China to examine the impact of military experience on employment and delve into the internal mechanisms under a clear theoretical framework.

Given that employment can be divided into two types, self-employed and employed (Mao et al., 2019), which are fundamentally different,⁶ most studies mainly talk about the latter. This article refers to employment as the provision of labor to others for income. To understand how military experience affects employment, this article conducts an empirical study using the 2010 to 2020 China Family Panel Studies data. The results show that military experience has a positive impact on employment by increasing the chances of working in the public sector, raising the likelihood of holding an administrative position, and accumulating political, social, and human capital. Building on earlier research, this article may have the following marginal contributions.

First, the IV-Probit model is used to get a more plausible empirical result. Previous studies either did not discuss the endogenous problem or lacked a rigorous causal inference method to ensure the credibility of the results. In this study, “family’s classification during the Cultural Revolution” and “an adult at the time of resumption of gaokao examination” are taken as instrumental variables of “military experience,” and the exogeneity of instrumental variables is verified by over-identification test so as to measure the employment effect of military experience accurately. Second, it enriches the relevant achievements of research on veterans’ employment in developing countries. Academics have mainly focused on the employment of ex-servicemen in developed Western countries, with very little research on developing countries, especially China. Some scholars have recognized the influence of military experience on employment, but have not analyzed the key issues deeply in a unified theoretical framework, which lays the foundation for this study. Third, it provides a theoretical evidence and practical direction for dealing with the unemployment problem related to veterans in China. Current research on the employment of veterans emphasizes theoretical analyses and lacks theories based on empirical analyses to guide practice. The conclusions drawn in this article are not only conducive to the realization of veterans’ employment but also to the formulation of corresponding policies.

The rest of the article is structured as follows. The second part is the literature review and research hypotheses. The third part introduces the research methodology. The fourth part presents the empirical results, including baseline regression results and robustness test. The fifth part discusses the endogenous problem and conducts mechanism test. And the last part presents the conclusions and policy recommendations of this study.

Literature Review and Research Hypotheses

Impact of Military Experience on Employment

The view that military experience affects employment is currently controversial. While some scholars believe that military experience promotes the employment of veterans (Connelly, 2016; Cooper, 1981; Routon, 2014), the studies of Kleykamp and Montgomery (2014) and H. Wang et al. (2023) show that military experience has a negative effect on employment. Puhani and Sterrenberg (2021) even found that military experience has no significant influence on employment. The reason for these different conclusions is related to the selection of research subjects and research methods.

In terms of the research object, for example, Cooper (1981) chose data from the 1977 Department of Defense Retire Survey (1977 DRS), compared to the CPS (The Current Population Survey) sample of 18-30 year olds selected by Kleykamp and Montgomery (2014), and the data on young German males used by Puhani and Sterrenberg (2021), excludes the effect of potentially confounding large numbers of recent service leavers, who tend to have poorer employment status. However, the data Cooper (1981) used was from 1977, and there may be dynamics in the impact under changes in the recruiting system (J. Wang, 2011), then the conclusion may not be applicable. As for research methods, Kleykamp and Montgomery (2014) established multiple regression model, Puhani and Sterrenberg (2021) controlled for variable of information on fitness level from military medical examinations, and Cooper (1981) based his conclusions on general data analysis, none of which showed a causal relationship. Although H. Wang et al. (2023) attempted to empirically test the impact of military experience on career acquisition via a variety of econometric methods, they failed to solve the endogenous problem of variables, hence the conclusions reached were not fully convincing. Overall, the studies of different scholars inevitably have limitations in data or methods.

Life course theory argues that people always advance their life course in a planned and selective manner at certain stages of social development and under different periods of social institutions (Elder et al., 2003). The theory emphasizes the subjective initiative of individuals, that is, the choices people make in society are influenced by their personal experiences and their own personality traits in addition to the social context (Elder, 1998; Zhang, 2015). In terms of joining the military, before the founding of the People’s Republic of China (PRC), people chose to serve in the military mainly for the purpose of repelling invaders and building a more stable and peaceful society. After the founding of the country, this gradually evolved into the pursuit of political honor, social status, and so on (Chen & Yang, 1996). Generally speaking, youth especially those from poor rural families and lacking social backgrounds are more willing to join the military (Lokshin & Yemtsov, 2008; J. Wang, 2011). As a special personal experience, the military experience may also have a significant impact on an individual’s subsequent life course, such as employment (H. Wang et al., 2023).

Joining the military causes individuals to forego their investment in human capital based on basic education at the same age and invest in human capital based on military knowledge and skills training, thus damaging their human capital to a certain extent (H. Wang et al., 2023; Zhao & Guo, 2021). From the perspective of opportunity cost, however, if this results in individuals facing various disadvantages in employment, then the opportunity cost of joining the military will be so high that rational individuals will not choose to serve in the military in the first place (Chen & Yang, 1996; J. Wang, 2011). In this way, there must be relevant material or non-material benefits that make individuals choose to join the military, which in turn has the potential to positively affect their employment after service period. These material benefits include financial subsidies for veterans, education fee waivers, free job training services, tax incentives, and medical and pension protection,⁷ while non-material benefits include political honors, social status enhancement, preferential recruitment, and advantages in rank evaluation and job promotion (Chen & Yang, 1996).

Motivated by the desire to expand political influence, governments often formulate a series of preferential policies to increase the protection of veterans (Zhu & Tang, 2023), and the rigorous implementation of these policies has been instrumental in stabilizing veterans’ employment (Zhan et al., 2023). The Opinions on the Work of Promoting the Employment and Entrepreneurship of Veterans in the New Era proposes to strongly support the employment of veterans in terms of recruitment conditions, civil service recruitment, employment channels and services, and so on.⁸ The Veterans Protection Law of the People’s Republic of China strengthens the protection of veterans in the form of law in the whole society, explicitly providing for veterans’ employment rights and interests, and units that recruit veterans are also entitled to tax concessions according to the law.⁹

Some countries have implicit discrimination against veterans in the hiring process (Barnes, 2014), which often stems from poor health or disability, lack of civilian work experience (Stone & Stone, 2015), and disregard for the unique skills and experience acquired during military service (Syracuse University, 2012). Psychological issues (Bordieri & Drehmer, 1984), such as post-traumatic stress disorder (PTSD), and prevalent stereotypes from the outside world (Shepherd et al., 2019), can also cause discrimination that veterans face when seeking jobs. In contrast, Chinese society generally honors military personnel, and there is little discrimination against veterans, they usually have high social status (He, 2021; Zhu & Tang, 2023). Under the same conditions, veterans can get more care at work and even have the opportunity to become leaders of institutions or enterprises (Chen & Yang, 1996). Based on the above analysis, this article proposes the following hypothesis:

Hypothesis 1: All else being equal, military experience promotes employment in China.

Mechanisms by Which Military Experience Affects Employment

Life course theory can be used to explain the positive impact of military experience on employment, which can be divided into two aspects: career choice and capital accumulation. This article considers the mediating factors of public sector units, administrative positions, political capital, social capital, and human capital, which are analyzed below.

First of all, military experience increases the likelihood of working in the public sector (Angrist & Chen, 2011; He, 2021; Routon, 2014; H. Wang et al., 2023; Winters, 2018; Zhan et al., 2023; Zhu & Tang, 2023). Under the social system in China, those employed in the public sector are often perceived to have higher social status and welfare levels (He, 2021; Zhan et al., 2023), as well as more decent and stable jobs (H. Wang et al., 2023). The militarized management and training in military expertise and skills that military personnel receive are partially applicable to the public sector (Johnson & Conley, 2019; Syracuse University, 2012), and this, combined with the patriotism of service to the people and the community that is inculcated over a long period of time in the military (Johnson & Conley, 2019), makes veterans more likely to choose the former when faced with the choice of employment in public and private sectors. According to a survey on the living conditions of 4,000 ex-military personnel conducted by the China Industrial Economy Information Network in 2020, 48% (the highest percentage) of veterans hope to work in government departments or enterprises and institutions,¹⁰ which is almost the same as the data given by the 2018 Report on Employment Data of Retired Soldiers,¹¹ and it is obvious that veterans are more inclined to work in public sector units.

In addition, the policies formulated by the government are also favorable to ex-servicemen to enter the public sector for employment. The Regulations on the Resettlement of Retired Soldiers makes it clear that state organs, social organizations, enterprises, and institutions have the obligation to accept and place retired soldiers, and that retired soldiers applying for civil service or positions in public institutions are eligible for priority recruitment under the same conditions. The Veterans Protection Law of the People’s Republic of China also emphasizes that the officers to be transferred to civilian services as well as the non-commissioned officers and the conscripts to be employed through job placement are recruited by Party and government organs, mass organizations, public institutions, and state-owned enterprises, and specific veterans will be given priority. By 2022, more than 80% of the officers to be transferred to civilian services have been placed in party and government organs as well as public service organizations,¹² and even more than 90% of ex-military personnel in some provinces have been placed in public sector, like Henan,¹³ Jiangxi,¹⁴ and Guangxi.¹⁵

Although many private-sector employers think highly of military experience, it is not easy to match veterans to suitable jobs due to the particularity of their status and military skills (Dempsey & Schafer, 2020). Furthermore, private sector tends to focus on efficiency and productivity, and veterans entering the private-sector units often face various difficulties, including cultural knowledge gaps and health issues (Johnson & Conley, 2019; O’Reilly, 2014; Winters, 2018). As a result, veterans prefer to be employed in the public sector. Based on the above analysis, this article takes “public sector units” as the mediating variable and puts forward the following hypothesis:

Hypothesis 2: Military experience contributes to employment in China through improving individuals’ chances of working in the public sector.

Then, military experience increases the probability that an individual will engage in an administrative position (He, 2021; Wu, 2019). The binary path theory of elite selection suggests that there are two paths for selecting elites in socialist countries, one is managerial elite oriented to political loyalty, and the other is technical elite oriented to professional skills (Walder, 1995). In practice, managerial elites correspond to administrative positions, while technical elites correspond to professional and technical positions. Under the socialist system, holding an administrative position represents a higher status (He, 2021) and is a symbol of power and social network advantages (J. Wang, 2011). Moreover, military life can refine a person’s character of law-abiding and self-discipline, cultivate the good qualities of loyalty and integrity, positive and enterprising, and a moral sentiment of love for the Party, patriotism, and dedication, thus shaping his or her unique character traits (Fu et al., 2021; Quan et al., 2019; Y. Wang & Xu, 2020). Therefore, for the majority of veterans who come from the bottom of the social stratum, taking up administrative positions not only provides a way for them to move to the upper strata of society, but also is more in line with their value orientation.

Generally, highly educated individuals are more likely to become technical elites (Walder, 1995), which is related to the depth and breadth of knowledge required for specialized technical positions. While individuals choose to join the military at an age when they should have attended college, the knowledge and skills involved in the military are mainly applied to the military field (Syracuse University, 2012), and the enhancement of their professional technical abilities is very limited (Wu, 2019), making it more difficult for them to engage in technical job compared with their peers. At the same time, under the strict management mode of the military, military personnel often possess a high degree of executive power and leadership (Connelly, 2016; Zeng et al., 2020), and always remain loyal to the Party and listen to the Party’s command. As a reward for their political loyalty, the Party will recommend some outstanding ex-servicemen to government agencies as well as state-owned enterprises and institutions to serve as administrative positions based on their rank (Hong, 1979; Wu, 2019). Therefore, from the perspective of rational choice, individuals with military experience tend to develop toward administration. Based on the above analysis, the following hypothesis is formulated using “administrative positions” as the mediating variable:

Hypothesis 3: Military experience facilitates employment in China by increasing the likelihood of holding administrative positions.

Finally, from the perspective of capital accumulation, military experience is a process of accumulating and reconstructing individuals’ political capital, social capital, and human capital (Gao, 2021), which has a positive effect on veterans’ employment. Among them, political capital and social capital are two important resources for identification, broadening connections and expanding social networks, with the former focusing on political benefits and the latter on social relations, while human capital is the comprehensive ability and quality reflecting factors such as workers’ knowledge and skills, cultural and technological level, and health status.

Political capital is a symbol of social status, containing a person’s political credentials, reputation, and power. Acquiring a certain amount of political capital is beneficial for improving the success rate of job seeking (W. Wang et al., 2020). Being a member of the Communist Party of China (CPC) is a common political capital, representing both an individual’s political loyalty and an affirmation of one’s qualities, abilities, and contributions. As of the end of 2022, there were about 98,041,000 CPC members in China.¹⁶ In such a large country with a population of 1.4 billion people, to be a member of the CPC is something to be honored, and it is more of a reflection of one’s political awareness, moral character, and working ability. As an effective political capital, the CPC membership can not only gain a higher socioeconomic status but also bring individuals a larger network of political connections, thus broadening their employment channels (Z. Wang et al., 2023). Therefore, in job applications, employers often give preference to job seekers with CPC membership (H. Wang et al., 2023; Wu, 2019). And by enlisting in the military, individuals are more likely to join the CPC, those who perform well and volunteer to join the Party will be prioritized for membership (Z. Wang et al., 2023; Zhang, 2015).

Social capital represents the resources that come from the contacts and relationships accumulated in society, including interpersonal reciprocity and trust. Social capital is conducive to dredging up relationships with employers, establishing good connections, and getting recognition from employers in the job search process. Established studies have confirmed that social capital not only has a facilitating effect on employment (X. Han & Zhang, 2015; Luo & Shen, 2021) but also enables individuals to get better-paid positions (Deng, 2019). Considering the traditional culture in China that people often communicate social relations through gift-giving rather than social group participation, expenditure on gifts can be used as a measurement of social capital, which is also called favor expenditure (L. Han et al., 2019; Zhou et al., 2014). Expenditure on gifts reflects an individual’s investment in maintaining social networks, including the reinforcement of social relationships before utilizing social resources, such as giving gifts before asking for favors (Jiang & Bian, 2007). Military experience contributes to the accumulation of social capital (Gao, 2021; Zhan et al., 2023), which has something to do with the mutual support and teamwork developed by soldiers during military training (Avrahami & Lerner, 2003). The social capital accumulated in the military greatly benefits veterans’ employment, both in terms of direct comradeship for employment and indirect employment assistance, information transfer, and so on (Gao, 2021).

Human capital is another resource to promote employment, reflecting the investment of workers in knowledge and skills, physical fitness, and so on. Human capital theory suggests that education is the most important means of improving human capital and that investment in human capital can be seen as an investment in education. Specifically, people who have attended college usually have more employment opportunities because a college degree is a signal in the job market that represents an individual’s competence. Many studies view military experience as a form of human capital investment (Cao & Lan, 2020; Gao, 2021; B. Wang & Yang, 2019). However, individuals investing in military human capital can simultaneously suffer from other human capital loss due to absence from higher education, lack of work experience, and impaired physical and mental health (Z. Wang et al., 2023; Zhao & Guo, 2021), which to some extent adversely affects their employment.

It is worth paying attention to the human capital investment of ex-soldiers. China’s policy on academic education for veterans stipulates that veterans can enjoy preferential treatment such as additional points, priority admission, and tuition fee reduction through the recruitment policy, the policy on the resumption of studies by demobilized college soldiers, and the educational assistance policy. The policy favors demobilized soldiers’ access to higher education and improves their human capital. Studies have shown that military experience gives individuals more chances to receive college education (Routon, 2014), which helps to enhance their basic education human capital (Angrist & Chen, 2011; Hou et al., 2020), thus making up for the shortage of veterans’ human capital (Z. Wang et al., 2023). In turn, the improvement of human capital makes ex-servicemen more popular in the labor market, which brings more employment opportunities (Z. Wang et al., 2023).

Based on the above analysis, this article takes “political capital,” “social capital,” and “human capital” as the mediating variables, and to better quantify the variables, “the CPC member,” “expenditure on gifts for social relations,¹⁷” and “college degree or above¹⁸” are used to measure “political capital,” “social capital,” and “human capital,” respectively. The proposed hypotheses are as follows:

Hypothesis 4a: Military experience promotes employment in China by enhancing one’s political capital.

Hypothesis 4b: Military experience promotes employment in China by enhancing one’s social capital.

Hypothesis 4c: Military experience promotes employment in China by enhancing one’s human capital.

Method

Data Sources and Processing

The data used in this article come from the China Family Panel Studies (CFPS), a large-scale nationwide comprehensive social tracking survey implemented by the Institute of Social Science Survey (ISSS) of Peking University, which accounts for 95% of China’s total population. The survey covers 25 provinces/municipalities/autonomous regions across the country, and consists of data at the individual, household, and community levels, reflecting the changes in social, economic, demographic, education, and health aspects in China comprehensively. Since 2010, CFPS has conducted six waves of surveys (every 2 years), and the data have been adopted by many authoritative scholars for academic research. For the reason that the questionnaire covers major data such as military experience, employment, and personal information, and the statistical caliber of the data does not differ much from year to year, this article uses the sample data from 2010 to 2020 for empirical analysis. In addition, considering that macrolevel social factors may also affect individual employment, the data on the registered urban unemployment rate,¹⁹ average wage of employed persons in urban non-private units, and GDP per capita of prefecture-level cities in China from 2010 to 2020 are matched to the sample.

As for data processing, the relevant variables are first screened according to the main issues of this article, including the independent variable “military experience,” the dependent variable “employment,” and the individual-level control variables “age,” “gender,” “urban/rural areas,” “household registration,” “eastern region,” “central region,” “marital status,” “years of education,” “Log of personal income,” and “health status.” Then, the “military experience” variable is filled in appropriately, because there is a logical jump in the CFPS questionnaire that leads to some missing data, which has a greater impact on the core independent variable, so the data need to be filled in according to the information of the questionnaire in other years. Samples with missing data and abnormal values in the variables are also deleted and new variables are defined. For example, being employed at a company or working for others in agricultural or non-agricultural fields is regarded as employment, and running one’s own business or being engaged in family agricultural work is regarded as entrepreneurship.²⁰ Government departments, party organs, social organizations, or state-owned enterprises or institutions is considered as public sector units, and being married or cohabiting is deemed as having a spouse. Finally, the retained effective sample size is 80,093 and the data are unbalanced panel data.

Modeling

To study whether military experience affects employment, the benchmark regression model is set as a Probit model on the basis of existing research, and the form of the model is as follows:

E m p l o y m e n t_{i t} = α_{0} + α_{1} M i l i t a r y e x p e r i e n c e_{i t} + δ X_{i t} + ε_{i t}

(1)

In Equation 1, the explained variable is $E m p l o y m e n t_{i t}$ , indicating whether individual i is employed in year t or not, the core explanatory variable is $M i l i t a r y e x p e r i e n c e_{i t}$ , indicating whether individual i has military experience in year t, and $X_{i t}$ is the matrix of control variables, including individual-level and societal-level control variables. Besides, $α_{0}$ represents the intercept term, $α_{1}$ is the regression coefficient of the core independent variable, $δ$ is the matrix of regression coefficients of the control variables, and $ε_{i t}$ denotes the randomized disturbance term. Considering that there may be autocorrelation between the residuals of sample individuals in the same province, the data are clustered and regressed at the provincial level, and the time effect is controlled. The empirical results of the model are derived from the statistical software Stata.²¹ If the coefficient $α_{1}$ is significant, then it can be assumed that there is an effect of military experience on individual employment at the corresponding significance level.²² If the sign of the regression coefficient is positive, then it is a positive effect; if the sign of the regression coefficient is negative, then it is a negative effect. The above method is equally applicable in explaining the results of the mechanism test section. The model, methods, and results of the mechanism test are discussed in the fifth part.

Name and Definition of Variables

The names, symbols, and definitions of the variables are shown in Table 1.

Table 1.

Name and Definition of Variables.

Variable name	Variable definition
Employment	Agricultural work for other families, non-agricultural casual work or employed = 1; Other = 0.
Entrepreneurship	Family agricultural work, individual/private business or other self-employment = 1; Other = 0.
Military experience	Has the life experiences of joining the army or is a veteran = 1; Other = 0.
Years of military service	Years in the military = Year at the end of military service-Year at the beginning of military service, with missing values filled in by the mean value.
(Respondent’s) age	—
Gender	Male = 1; Female = 0.
Urban/rural areas	Living in urban areas = 1; Rural areas = 0.
Household registration	Agricultural = 1; Non-agricultural = 0.
Eastern region	Province located in the eastern region = 1; Other = 0.
Central region	Province located in the central region = 1; Other = 0.
Western region	Province located in the western region = 1; Other = 0.
Marital status	Married (having a spouse)/cohabitation = 1; Never married/divorced/widowed = 0.
Years of education	Years of education completed by respondents.
Log of personal income	The logarithm of annual personal income, or 0 if there is no income.
Health status	Excellent = 1; Very good = 2; Good = 3; Fair = 4; Poor = 5.
Unemployment rate	The registered urban unemployment rate.
Log of average wage of employees	The logarithm of average wage of employed persons in urban non-private units every year.
Log of per capita GDP	The logarithm of per capita gross domestic product.
An adult at the time of the resumption of gaokao examination	Aged 18 and over at the time of the resumption of college entrance examination in 1977 = 1; Otherwise = 0.
Family’s classification during the Cultural Revolution	Family’s classification during the Cultural Revolution was middle/poor peasant or hired peasant = 1; Other = 0.
Public sector units	Working in Government/Party/Non-governmental organization/Military, State-owned/Collectively-owned public institution/Research institute, State-owned/State-controlled enterprise, Association /Guild/Foundation/Social organization, Residential community committee/Village committee/Autonomous organization = 1; Other = 0.
Administrative positions	Hold an administrative/management position = 1; Otherwise = 0.
Political capital	Member of the CPC = 1; Otherwise = 0.
Human capital	College Degree or above = 1; Other = 0.
Social capital	The logarithm of the total amount of money spent on gifts for social relations in the past 12 months, or 0 if none.

Explained Variable

The explained variable selected in this study is “employment.” In the questionnaire of the CFPS, the options for employment are categorized into five types: “(1) Family agricultural work; (2) Individual/private business/other self-employment; (3) Agricultural work for other families; (4) Employed; (5) Non-agricultural casual workers.” Referring to the study of Mao et al. (2019), this article considers being employed as employment and self-employment as entrepreneurship. The explained variable takes the value of 1 if the respondent is employed or engaged in agricultural work for other families or non-agricultural casual work, and 0 for others. “Entrepreneurship” can be used as an alternative explained variable to test the robustness of the effect of military experience on employment, and it takes the value of 1 if the respondent is engaged in family agricultural work, individual/private business, or other self-employment, and 0 for others.

Explanatory Variable

The core explanatory variable in this article is “military experience.” In the questionnaire of CFPS, the questions about military experience are “Have you had the life experiences of joining the army?” and “Are you a veteran?,”²³ where veterans generally refer to officers, non-commissioned officers, and conscripts who have legally withdrawn from active service in the People’s Liberation Army (PLA).²⁴ According to the responses, the data of those who have joined the military or are veterans will be assigned a value of 1, and the value of the otherwise will be assigned 0.

Control Variables

The control variables in this article contain both an individual level and a societal level. At the individual level, factors affecting veterans’ employment include gender, age, and geographic location (Syracuse University, 2012) as well as marital status, income (Kleykamp & Montgomery, 2014), and health status (Stone & Stone, 2015). Besides, household registration and education level (Z. Wang et al., 2023) also have a significant impact on the employment of veterans. Therefore, “age,” “gender,” “urban/rural areas,” “household registration,” “eastern region,” “central region,” “marital status,” “years of education,” “log of personal income,” and “health status” are selected as control variables. Among them, “age,” “gender,” “urban/rural areas,” “household registration,” “years of education,” and “health status” are obtained directly from the questionnaire. “Eastern region” and “central region” are new variables generated by dividing regions into eastern, central, and western regions; “marital status” variable assigns the original married or cohabiting to 1, and unmarried, divorced, or widowed to 0; “log of personal income” variable takes the logarithm of the annual personal income, and if there is no income, the value is 0.

At the social level, this article chooses “unemployment rate,” “log of average wage of employees,” and “log of per capita GDP” variables to control the influence of macro socioeconomic factors on individual employment status, in which “unemployment rate” variable uses the data of the registered urban unemployment rate, “log of average wage of employees” variable is logarithmic with the average annual wage of employed persons in urban non-private units, and “log of per capita GDP” variable is also logarithmic.

Mediating Variables and Instrumental Variables

In the mechanism testing part of this article, a mediating effect model is adopted to verify the mechanism of military experience on individual employment, and the possible mechanisms mentioned earlier are improving job opportunities in the public sector, increasing the possibility of holding an administrative position, as well as enhancing political capital, social capital, and human capital.

Accordingly, the mediating variables selected are (1) “public sector units,” according to the question “What type of work unit is this?,” if the respondent answers “Government/Party/Non-governmental organization/Military; State-owned/Collectively-owned public institution/Research institute; State-owned/State-controlled enterprise; Association/Guild/Foundation/Social organization; Residential community committee/Village committee/Autonomous organization,” then the value is 1, otherwise the value is 0; (2) “administrative positions,” according to the question “Do you hold an administrative/management position?,” if the respondent answers “Yes,” the value is 1, otherwise the value is 0; (3) “political capital,” according to the question “Your political status is?” and “Are you the member of Communist Party of China?,” if the respondent answers “Member of the Communist Party of China” and “Yes,” the value is 1, otherwise the value is 0; (4) “social capital,” according to the question “In the past 12 months, what was the total amount of money your family spent on gifts for social relations?,” the variable is constructed and takes logarithm; (5) “human capital,” according to the question “What is the highest level of education you have obtained so far?,” it takes the value of 1 if the respondent answers college or above, and 0 otherwise.

In addition, IV-Probit model²⁵ is used in the part of endogenous discussion. Referring to the studies of Y. Wang and Xu (2019, 2020) as well as B. Wang and Yang (2019), variables “an adult at the time of the resumption of gaokao examination” and “family’s classification during the Cultural Revolution” are considered as instrumental variables for “military experience,” because these two instrumental variables are more in line with the Chinese context. In this case, the exogeneity of the instrumental variables can be verified via over-identification test.²⁶ For the variable “an adult at the time of the resumption of gaokao examination,” if the respondent was already 18 years old when the college entrance examination of China was resumed in 1977, then it takes the value of 1, otherwise the value is 0. For the variable “family’s classification during the Cultural Revolution,” according to the question “What was your family’s classification during the Cultural Revolution?,” if the respondent answers that his or her family’s classification during the Cultural Revolution was “middle peasant,” “poor peasant,” or “hired peasant,” it takes the value of 1, and 0 otherwise. Due to the lack of a question on “family’s classification during the Cultural Revolution” in the questionnaire of other years, it is necessary to match the data of 2010 to the samples of other years.

Descriptive Statistics of Main Variables

Descriptive statistics for main variables are shown in Table 2. As can be seen from the table, the proportion of employment for the sample with military experience is 31.3%, while the proportion of those without military experience is 21.6% (31.3% > 21.6%), indicating that the overall level of being employed for the person with military experience is higher than those without. On the flip side, the proportion of entrepreneurs in the sample with military experience is 32.9%, and the proportion of those without military experience is 48.1% (32.9% < 48.1%), again suggesting that military experience may be in favor of employment. What’s more, those with military experience have a higher proportion of jobs in public sector and administrative positions, as well as more political capital, social capital, and human capital, implying that these factors may play an important role in the impact of military experience on employment.

Table 2.

Descriptive Statistics of Main Variables.

Variables	Full sample		Sample without military experience		Sample with military experience
Variables	Average value	Standard error	Average value	Standard error	Average value	Standard error
Employment	0.219	0.414	0.216	0.412	0.313	0.464
Entrepreneurship	0.476	0.499	0.481	0.500	0.329	0.470
(Respondent’s) age	55.548	11.660	55.369	11.628	60.492	11.449
Gender	0.483	0.500	0.465	0.499	0.978	0.146
Urban/rural areas	0.464	0.499	0.459	0.498	0.603	0.489
Household registration	0.721	0.448	0.729	0.444	0.500	0.500
Eastern region	0.420	0.494	0.418	0.493	0.478	0.500
Central region	0.301	0.459	0.301	0.459	0.314	0.464
Western region	0.279	0.449	0.282	0.450	0.208	0.406
Marital status	0.887	0.316	0.886	0.318	0.927	0.260
Years of education	6.150	4.608	6.066	4.601	8.474	4.158
Log of personal income	4.080	4.552	4.042	4.540	5.134	4.757
Health status	2.879	1.340	2.880	1.343	2.848	1.282
Unemployment rate	3.215	0.705	3.215	0.705	3.224	0.707
Log of average wage of employees	10.825	0.434	10.825	0.434	10.830	0.443
Log of per capita GDP	10.552	0.677	10.549	0.677	10.633	0.660
Public sector units	0.082	0.275	0.078	0.268	0.192	0.394
Administrative positions	0.023	0.149	0.022	0.146	0.055	0.229
Political capital	0.094	0.292	0.076	0.266	0.584	0.493
Human capital	0.043	0.203	0.041	0.198	0.098	0.298
Social capital	6.658	2.777	6.652	2.779	6.831	2.712

Results

Baseline Regression Results

To explore the impact of military experience on employment, a Probit model is adopted to conduct regression with “employment” as the dependent variable, and the estimated marginal effects of military experience on employment are presented in Table 3.

Table 3.

Baseline Regression Results for the Impact of Military Experience on Employment.

Variables	(1)	(2)	(3)
Variables	Employment	Employment	Employment
Military experience	0.303*** (0.043)	0.153*** (0.049)	0.178*** (0.051)
(Respondent’s) age		–0.041*** (0.003)	–0.043*** (0.004)
Gender		0.166*** (0.044)	0.177*** (0.043)
Urban/rural areas		0.364*** (0.052)	0.313*** (0.059)
Household registration		–0.409*** (0.051)	–0.377*** (0.055)
Eastern region		0.473*** (0.099)	0.311*** (0.085)
Central region		0.273*** (0.088)	0.245*** (0.095)
Marital status		–0.042(0.040)	–0.045(0.038)
Years of education		0.031*** (0.006)	0.030*** (0.006)
Log of personal income		0.237*** (0.005)	0.235*** (0.005)
Health status		–0.033*** (0.007)	–0.034*** (0.007)
Unemployment rate			–0.021(0.046)
Log of average wage of employees			0.130(0.118)
Log of per capita GDP			0.190** (0.076)
Year fixed effect	YES	YES	YES
Provincial fixed effect	YES	YES	YES
Constant	–1.001***	–1.438***	–4.498***
	(0.089)	(0.210)	(1.000)
Observations	80,093	80,093	80,093

Note. Robust standard errors in parentheses.

***

p < .01, **p < .05, *p < .1.

The regression result in Column (1) shows that controlling only for year and provincial fixed effects, the probability of being employed increases significantly by 30.3% for veterans compared with non-veterans, suggesting that military experience raises the probability of individual employment. To exclude interference from other factors, regressions are conducted by adding individual-level control variables and social-level control variables in Columns (2) and (3) in turn, respectively. Column (2) adds “age,” “gender,” “urban/rural areas,” “household registration,” “eastern region,” “central region,” “marital status,” “years of education,” “log of personal income,” and “health status” variables to Column (1), and the regression result shows that the probability of employment for veterans is significantly increased by 15.3% at the 1% level, compared with non-veterans. Column (3) adds “unemployment rate,” “log of average wage of employees,” and “log of per capita GDP” variables to Column (2), and the result shows that military experience still significantly contributes to employment (17.8%). The above regression results better confirm the Hypothesis 1 proposed in this article, that is, all else being equal, military experience promotes individual employment.

In addition, in the baseline regression, it can be found that the control variables “age,” “gender,” “urban/rural areas,” “household registration,” “eastern region,” “central region,” “years of education,” “log of personal income,” “health status,” and “log of per capita GDP” all have a significant effect on individual employment, and the impact of “age,” “household registration,” and “health” is negative. It shows that it is necessary to control the impact of the above factors on employment, which also verifies the previous views. Nevertheless, the variables “marital status,” “unemployment rate,” and “log of average wage of employees” have no significant impact on employment, indicating that there is no sufficient evidence to support the impact of these factors, but controlling for them is not unnecessary. So the same control variables as above are used later.

Robustness Test

The results of the baseline regression indicate that military experience has a significant positive effect on employment, and further robustness test as well as endogenous discussion is required to ensure the credibility of the results.

Replacement of the Dependent Variable

The explained variable used in the benchmark regression model is “employment,” and the regression results may have errors due to the setting of the explained variable, so the method of replacing the dependent variable is adopted to verify the accuracy of the regression results. Replacing the dependent variable with “entrepreneurship,” the result is shown in Column (1) of Table 4, where the probability of entrepreneurship for veterans is 10.8% lower than that for non-veterans. It means that military experience has a disincentive effect on entrepreneurship, which can be interpreted the other way round, that is, military experience is pro-employment.

Replacement of the Independent Variable

This article studies the effect of military experience on employment, and the independent variable “military experience” is a binary variable. To further examine the effect, the core independent variable is replaced with “years of military service.” The regression result is shown in Column (2) of Table 4. It can be seen that the result of employment on years of military service is not significant, which means that there is no evidence that years of military service has a positive impact on employment. Column (3) of Table 4 replaces the dependent variable with “entrepreneurship” based on Column (2), and the result shows that the probability of entrepreneurship decreases by 2.5% with each additional year of military service. In other words, entrepreneurship is discouraged as the number of years in the military increases, reflecting the possible employment-enhancing effect of years in the military.

Table 4.

Robustness Test for the Effect of Military Experience on Employment.

Variables	(1)	(2)	(3)	(4)
Variables	Entrepreneurship	Employment	Entrepreneurship	Logit regression
Military experience	-0.108** (0.042)			0.319*** (0.092)
Years of military service		0.009(0.008)	-0.025*** (0.008)
Control variables	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES
Provincial fixed effect	YES	YES	YES	YES
Constant	5.237*** (1.780)	-4.486*** (1.005)	5.248*** (1.779)	-7.981*** (1.837)
Observations	80,093	80,093	80,093	80,093

Note. Robust standard errors in parentheses.

***

p < .01, **p < .05, *p < .1.

Change in Regression Method

The Probit model is used in the benchmark regression, which is applicable to the case where the explained variable is a binary variable. The Logit model is also suitable for binary dependent variables, with the main distinction between the two being the difference in the distributions obeyed by the random disturbance terms of the model. Here, assuming that $ε_{i t}$ of the baseline regression model obeys a logistic distribution, the Logit regression method is utilized to test the effect of military experience on employment. The regression result shows that the probability of being employed is 31.9% higher for veterans compared with non-veterans and the coefficients are significant at the 1% level, suggesting that a change in the regression method would not affect the conclusion that military experience promotes employment.

Adjustment of the Sample Range

In the previous analysis, the full sample after data processing was used, and appropriate adjustments have not yet been made to the scope of the sample. As the ex-servicemen group is predominantly male,²⁷ and given the age limitations of joining the military and obtaining employment, only the male sample and the sample aged between 20 and 60 are retained here, respectively. In Table 5, Column (1), for the case of the male-only sample, the regression result shows that military experience increases the probability of being employed by 14.2%, which is not much of a change from the baseline regression results. In Column (2) of Table 5, where the sample age ranges from 20 to 60, the result shows a significant increase of 22.9% in the employment of those with military experience over those without military experience. Thus, adjusting the sample has little influence on the empirical results, which reaffirms the robustness of the finding that military experience promotes individual employment.

Table 5.

Robustness Test and Endogenous Test for the Effect of Military Experience on Employment.

Variables	(1)	(2)	(3)	(4)
Variables	Only male	Aged 20–60 years	The first stage regression of IV-probit	The second stage regression of IV-probit
Military experience	0.142*** (0.045)	0.229*** (0.038)		6.664*** (1.206)
An adult at the time of the resumption of gaokao examination			0.012*** (0.002)
Family’s classification during the Cultural Revolution			0.018*** (0.002)
Amemiya-Lee-Newey p-value				0.2447
Control variables	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES
Provincial fixed effect	YES	YES	YES	YES
Constant	-5.210*** (1.077)	-5.452*** (1.059)	-0.049(0.054)	-0.134(0.693)
Observations	38,710	53,100	78,623	78,623

Note. Robust standard errors in parentheses of Columns (1) and (2), and standard errors in parentheses of Columns (3) and (4).

***

p < .01, **p < .05, *p < .1.

Further Analysis

Endogenous Discussion

To accurately estimate the impact of military experience on employment, it is necessary to discuss the possible endogenous problems of the model such as omitted variables, reverse causality, and selection bias.

First of all, for the problem of omitted variables, there may be some variables that both affect employment and are related to military experience that are not controlled for and are omitted from the random disturbance term of the model, such as unobservable factors like physical fitness, personal qualities, or comprehensive abilities, which are themselves assessment criteria for entering the military and may also be the priority aspects of recruitment by employers, leading to the regression results to show error.

Second, this article studies the effect of military experience on employment, but there may also be a situation in which people with higher employability are more inclined to join the military. For instance, the national policy encourages college students to enlist in the military, and being able to enter the college itself is a reflection of personal ability, and these college students who responded to the call of the state for enlistment are also able to get better jobs after graduation even if they did not choose to join the army, so the model may have a reverse causality problem.

Finally, selection bias is divided into sample selection bias and self-selection bias. Since the CFPS data is a national random sample survey data, it is unlikely to have sample selection bias, but the self-selection bias problem needs to be considered. Research has shown that men who are more able to earn higher incomes tend to be more likely to join the military (Angrist & Krueger, 1994), that joining the military is seen as a form of social mobility (Connelly, 2016), and that rural youth are more likely to achieve upward social mobility through joining the Army (Hou et al., 2020), so the model may be subject to self-selection bias.

Therefore, on the basis of the existing literature, “an adult at the time of the resumption of gaokao examination” and “family’s classification during the Cultural Revolution” are chosen as the instrumental variables for “military experience,” and IV-Probit model is selected to solve the endogeneity problem and test the employment effect of military experience.

The reason for choosing “an adult at the time of the resumption of gaokao examination” as the instrumental variable is that, according to the Military Service Law of the People’s Republic of China, citizens must be at least 18 years old when they are recruited into the military, and before the resumption of China’s college entrance examination in 1977, joining the military was the main way out for many young people. Enlisting in the military could not only change their fate and improve their social status, but also give priority to assign jobs by the government and obtain employment support after their tour of duty, which became the choice of most people in the extremely unpromising employment situation at that time (Y. Wang & Xu, 2019, 2020). So the variable “an adult at the time of the resumption of gaokao examination” can satisfy the requirements of exogeneity and correlation at the same time and is an ideal instrumental variable.

The reason why “family’s classification during the Cultural Revolution” is chosen as an instrumental variable is that entering the army to be a soldier had better development prospects at that time, and family’s classification during the Cultural Revolution largely determined whether one could join the army or not. The Decision on the Class Classification of the Rural Areas, promulgated in 1950, explicitly divided rural areas into different class compositions, namely, rural class classification, rural class classification, and rural class compositions. The Decision on the Division of Class Status in Rural Areas, issued in 1950, clearly divided rural areas of China into different class status, that is, rural classes were divided into “landlords,” “rich peasants,” “middle peasants,” and “poor peasants,” while middle peasants were further divided into “upper middle peasants,” “middle peasants,” and “lower middle peasants.” It was a policy implemented in accordance with the current situation and needs of China’s land reform at that time. As a result, the poor and lower middle peasants, who were at the bottom of the old society, became the “red class” with higher political status, and their children were given priority to join the army and go to school, while the landlords and rich peasants became the “black class” that was defeated, and their children were long excluded from joining the military because of their family composition (B. Wang & Yang, 2019). The variable “family’s classification during the Cultural Revolution” also meets the characteristics of exogeneity and correlation of instrumental variables, hence can be used as an instrumental variable of military experience.

On this basis, the reasonableness of the instrumental variables is further tested, and the results are shown in Columns (3) and (4) of Table 5. In Column (3), the first-stage regression result of the IV-Probit model shows that the coefficients of the variables “an adult at the time of the resumption of gaokao examination” and “family’s classification during the Cultural Revolution” are both significant at the 1% level, which indicates that the two instrumental variables are correlated with the variable “military experience” and that there is no weak instruments problem. In Column (4), the Amemiya-Lee-Newey²⁸ p-value for the over-identification test is .2447, which is greater than the critical value of .1, and there is no evidence to reject the original hypothesis, then the instrumental variables are consistent with exogeneity. Therefore, the instrumental variables selected in this article have passed the test. Moreover, the second-stage regression result of the IV-Probit model shows that the probability of employment for veterans is 666.4% higher than that for non-veterans, demonstrating that military experience has a strong explanatory power for employment. Thus, Hypothesis 1 is confirmed.

Mechanism Test

In the previous section, the employment effect of military experience has been analyzed and passed the robustness test, and the preliminary conclusion is that military experience has a facilitating effect on employment, but the specific mechanism through which military experience positively affects employment has to be further tested. To gain a deeper understanding of the intrinsic mechanism between military experience and employment, and in conjunction with the theoretical analyses presented in the previous part, “public sector units,” “administrative positions,” as well as “political capital,” “social capital,” and “human capital” are taken as the mediating variables in the impact of military experience on employment. The mediating effect model is set as follows:

E m p l o y m e n t_{i t} = α_{0} + α_{1} M i l i t a r y e x p e r i e n c e_{i t} + δ X_{i t} + ε_{i t}

(2)

Z_{i t} = β_{0} + β_{1} M i l i t a r y e x p e r i e n c e_{i t} + θ X_{i t} + μ_{i t}

(3)

E m p l o y m e n t_{i t} = γ_{0} + γ_{1} M i l i t a r y e x p e r i e n c e_{i t} + ρ Z_{i t} + σ X_{i t} + ϵ_{i t}

(4)

where $Z_{i t}$ denotes the mediating variable, the meanings and symbols of the remaining variables are as described earlier. For the coefficients $α_{1}$ , $β_{1}$ , $γ_{1},$ and $ρ$ in the model, if $β_{1}$ and $ρ$ have the same sign and $| α_{1} | > | γ_{1} |$ or $β_{1}$ and $ρ$ have opposite signs and $| α_{1} | < | γ_{1} |$ , then the possible presence of a mediating effect is indicated. If the coefficients $β_{1}$ and $ρ$ are both significant and the coefficient $γ_{1}$ is not significant, then there is a complete mediating effect, while the coefficients $β_{1}$ , $ρ$ , and $γ_{1}$ are all significant suggests that there is a partial mediating effect. What’s more, if the coefficients $β_{1}$ and $γ_{1}$ are both significant, but the coefficient $ρ$ is not significant, a Sobel test²⁹ is required to determine whether there is a mediating effect. The above results are derived via Stata.

The results of the mechanism test are shown in Tables 6 and 7. In Columns (1) and (3) of Table 6, the probability of the people with military experience entering public sector units and holding administrative positions increased by 30.2% and 14.6%, respectively, compared with those without military experience, which suggests that military experience contributes to entry into public sector units and serving in administrative positions. In Columns (1), (3), and (5) of Table 7, the persons with military experience have 125.8%, 9.3%, and 20.1% higher probability of obtaining the CPC membership, spending on gifts for social relations, and getting college degree or above, respectively, than those without military experience. The results indicate that military experience is helpful for the enhancement of political capital, social capital, and human capital. According to the results in Columns (2) and (4) of Table 6 and Columns (2) and (6) of Table 7, it can be found that “public sector units,” “administrative positions,” “political capital,” and “human capital” all have a significant positive effect on employment, implying that they are mediators of military experience acting on employment. Wherein, the variable “public sector units” shows a full mediating effect, while the variables “administrative positions,” “political capital,” and “human capital” are partial mediators.

Table 6.

Mechanism Test Results for the Impact of Military Experience on Employment of H2–H3.

Variables	(1)	(2)	(3)	(4)
Variables	Public sector units	Employment	Administrative positions	Employment
Military experience	0.302*** (0.044)	0.052(0.061)	0.146** (0.060)	0.166*** (0.047)
Public sector units		2.312*** (0.073)
Administrative positions				1.084*** (0.076)
Control variables	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES
Provincial fixed effect	YES	YES	YES	YES
Constant	-3.215*** (0.566)	-5.058*** (1.172)	-6.284*** (0.676)	-4.414*** (1.011)
Observations	79,563	79,563	79,543	79,543

Note. Robust standard errors in parentheses.

***

p < .01, **p < .05, *p < .1.

Table 7.

Mechanism Test Results for the Impact of Military Experience on Employment of H4a–H4c.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
Variables	Political capital	Employment	Social capital	Employment	Human capital	Employment
Military experience	1.258*** (0.074)	0.093* (0.054)	0.246*** (0.041)	0.175*** (0.051)	0.201*** (0.068)	0.192*** (0.055)
Political capital		0.212*** (0.038)
Social capital				0.003(0.004)
Human capital						0.551*** (0.091)
Sobel Test			p-value < .05
Control variables	YES	YES	YES	YES	YES	YES
Year fixed effect	YES	YES	YES	YES	YES	YES
Provincial fixed effect	YES	YES	YES	YES	YES	YES
Constant	-3.502*** (0.752)	-4.501*** (1.020)	2.046(3.829)	-4.508*** (0.992)	-1.410(1.503)	-4.245*** (1.044)
Observations	79,522	79,522	79,572	79,572	80,093	80,093

Note. Robust standard errors in parentheses.

***

p < .01, **p < .05, *p < .1.

However, the variable “social capital” does not show a significant mediating effect, which needs to be further tested. The Sobel test is used to verify the mediating effect of “social capital,” and the Z-statistic and its p-value are obtained directly through Stata. The result shows that the p-value is less than .05, providing the evidence that the mediating effect of “social capital” is significant in the relationship between military experience and employment, that is, military experience can promote individual employment through the increase of social capital. Therefore, the results of the mechanism analysis part confirm the proposed Hypotheses 2 to 4, namely military experience contributes to individual employment by improving the opportunities of working in the public sector, increasing the likelihood of holding administrative positions, and enhancing political capital, social capital, and human capital.

Conclusion

Summary

Based on the sample data of the China Family Panel Studies (CFPS) from 2010 to 2020, this article systematically and comprehensively analyzes the role that military experience plays on employment and its influence mechanism. First, this article verifies that military experience does have a positive impact on employment and can increase the probability of individual employment. The conclusion remains credible after a series of robustness tests such as replacing the dependent and independent variables, changing the regression method, and adjusting the sample range. Then, the article reconfirms that military experience positively affects individual employment by using IV-Probit model to overcome the endogenous issues. Finally, this study verifies the possible mediating factors between military experience and employment, namely public sector units, administrative positions, political capital, social capital, and human capital.

Implications

As a result, this article intends to put forward several recommendations for the employment of veterans.

First, we are supposed to change the employment concepts of ex-servicemen and encourage them to enter the private sector for employment or start their own business. After retiring from military service, military personnel are more inclined to choose employment in the public sector, but the number of jobs available in the public sector is limited after all. For this reason, on one hand, we can help them change their employment concepts through employment training and counseling services, so that they can gradually move into private-sector employment or start their own businesses. On the other hand, it is possible to improve the remuneration of private-sector jobs, encourage private-sector units to hire veterans, provide financial subsidies and tax breaks to enterprises that recruit veterans, and provide start-up grants to veterans who wish to start their own businesses.

Second, we should place emphasis on the development of administrative skills for veterans in the course of employment training, while developing various types of specialized skills. In the process of employment, ex-servicemen often take up an administrative position, which is related to the militarized management they received in the military and the administrative affairs they were involved in, providing them with certain skills and experience for engaging in corresponding administrative work in the future. However, not all soldiers who served in the military are able to take up such jobs. Some military occupations are difficult to match with corresponding jobs in the civilian labor market, such as combat soldiers. Consequently, it is necessary to focus on the development of administrative skills and other types of professional skills in job training for veterans, so as to improve their employment situation.

Last but not least, the Government should spare no effort to establish and improve relevant policies and systems to enhance the political capital, social capital, and human capital of veterans. Military experience to promote veterans’ employment is largely related to capital accumulation. By joining the CPC and other types of political organizations and engaging in political-related work, it is conducive to the accumulation of political capital. Establishing social connections with others, especially among groups of veterans, to expand social networks, can help accumulate social capital. Through completing higher education and upgrading academic qualifications, it will be to the advantage of making up for the loss of human capital that occurred during the period when veterans were in the military. In a word, it is important to facilitate the accession of ex-servicemen to the CPC or other political organizations, drive the construction of platforms for interconnections between veterans and between veterans and society, and increase the incentives of academic education policies to guarantee the upgrading of veterans’ educational attainment, so that they can be successfully employed.

Footnotes

Acknowledgements

I would like to thank the reviewers for their insightful comments on this manuscript. In particular, I thank the Editor-in-Chief for sending me some invaluable resources to support my research.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was funded by the Key Project of the National Social Science Foundation of China, Project No. 21AJY009.

ORCID iD

Dongni Li

Notes

Author Biography

Dongni Li is a full-time teacher at the School of Management of Changsha Medical University.

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