Does Internet use in the digital age Affect the Labor Participation among Middle-Aged and Elderly Individuals? Evidence from China
Yuqiao Dai 1*, Hongwei Yue 2,b
1College of humanities & social sciences, Huazhong Agricultural University, Wuhan 430070, Hubei, China
2College of Marxism, Nanyang Institute of Technology, Nanyang 473004 ,Henan, China
*Corresponding author’s email: yuqiaodai@163.com
bEmail: ：sgzlily_23@163.com
Funding
This work was supported by the Doctoral Research Initiation Fund Project of Nanyang Institute of Technology (Project No.NGBJ-2020-22) and the Soft Science Project of Henan Province (Grant No. 16242400410469)
Abstracts: In the context of rapid digital technology development and deepening population aging in China, Internet use offers new avenues to enhance labor participation among middle-aged and elderly individuals, while also introducing new dynamics to China’s labor market. This study utilizes data from the 2017, 2018, and 2021 waves of the China General Social Survey (CGSS) to analyze the impact of Internet use on the labor participation of middle-aged and elderly individuals, and to explore the underlying mechanisms. The findings indicate that Internet use is a key variable influencing labor participation in this demographic, exerting significant positive effects on non-agricultural, self-employed, flexible, and formal employment participation. Mechanistically, the use of the internet as a primary source of information and the increased frequency of leisure-time learning activities that enhance human capital are important mediators through which Internet use promotes non-agricultural labor participation among middle-aged and elderly individuals. Therefore, the government should prioritize internet skills training and entrepreneurial guidance for this population to improve their labor capacity and skill levels, bridge the digital divide, and fully leverage the labor-enhancing effects of the internet. This will unlock the labor potential of middle-aged and elderly individuals, facilitating high-quality employment and helping mitigate the pressures of population aging.
Keywords: Internet use; labor participation; employment modes; participation types; middle-aged and elderly individuals.
1. Introduction
As the global population ages and demographic shifts intensify, the world faces significant challenges to economic and social development. On one hand, ensuring that middle-aged and elderly individuals have the right to continued labor participation is crucial for turning population aging into a developmental opportunity. This not only helps older adults increase their income and improve their financial self-sufficiency but also enables them to play active roles in society, enhancing their sense of social participation and overall life satisfaction. On the other hand, the labor participation of older adults can effectively mitigate the negative impacts of the declining “demographic dividend” by transforming the challenges of aging into human capital advantages, promoting sustainable economic growth. According to the World Health Organization’s 2024 forecast, the global population aged 60 and over is projected to increase from 1 billion in 2020 to 1.4 billion by 2030, and to 2.1 billion by 2050. Compared to developed nations, low- and middle-income countries are experiencing significant demographic shifts, with two-thirds of the world’s population over 60 expected to reside in these regions by 2050. As one of the developing countries, China faces additional challenges related to its “aging before wealth” dilemma. Therefore, in the context of a rapidly aging global population and the decline of China’s demographic dividend, the employment or reemployment of middle-aged and elderly individuals has become an urgent issue that demands immediate attention.
At the same time, with the rapid development of the Internet industry, the world has entered a high-tech economic era characterized by digital information. The “Statistical Report on China’s Internet Development” released by the China Internet Network Information Center (CNNIC) shows that as of December 2022, the number of Internet users in China had reached 1.067 billion, with an Internet penetration rate of 75.60%. Notably, the proportion of Internet users aged 50 and above has significantly increased, with the number of elderly Internet users aged 60 and over reaching 153 million, accounting for 14.3% of the total Internet user base. The advent and gradual popularization of the Internet have transformed traditional employment methods, giving rise to new forms of employment. This shift has provided middle-aged and elderly individuals with new employment opportunities and has had a profound impact on China’s labor market.
About Internet-related studies academics have achieved more fruitful results. The existing literature focuses on the following aspects: First is the impact of the Internet on economic structure, industrial, and agricultural production. Studies have shown that the development of the Internet reduces the spread of the informal economy [1], enhances industrial green total factor productivity [2][3] and agricultural productivity [4][5], adjusts employment structures across industries [6], and promotes urban employment concentration [7]. Second is the impact of the Internet on household economic well-being. Existing research indicates that Internet use can reduce information costs for farmers, broaden income channels, improve the employment quality of rural laborers [8],and facilitate economic growth in rural areas [9]. However, it can also widen the income gap between employers and employees [10]. Additionally, the digital divide associated with Internet use may increase poverty rates, hindering poverty alleviation efforts for low-income, elderly, and rural households and widening the income gap between urban and rural areas [11]. Therefore, governments must focus on the efficiency, equity, and inclusiveness of digital economic development. Third is the impact of the Internet on rural economy, e-commerce, and employment. In rural economic development, the application of Internet technologies such as digital media and online platforms boosts the growth of agricultural products and rural tourism, thus promoting rural economic development [12]. Regarding e-commerce. Internet use significantly increases the probability of farmers engaging in online transactions, which in turn promotes income growth [13]. In terms of employment, the use of artificial intelligence and online interview platforms increases the efficiency of the job application process [14]. Fourth is the impact of Internet use on mental health and subjective well-being: The effects of Internet use on mental health and well-being can be viewed from two perspectives. One perspective suggests that Internet use reduces feelings of loneliness, sadness, and hardship, thereby enhancing subjective well-being [15]. On the other hand, some argue that Internet use lowers perceptions of social fairness [16], negatively impacting life satisfaction [17].
A review of the existing literature reveals that while the impact of on various socio-economic aspects has received considerable attention, research specifically addressing the relationship between Internet use and labor participation is relatively scarce. Although some studies have explored the connection between Internet use and employment in the labor market, they primarily focus on rural residents [18], the working-age population [19], and women [20], with a notable lack of attention to labor participation among older adults. For instance, previous research indicates that different employment statuses can influence Internet use frequency [21], and that Internet use directly contributes to income growth for rural residents and non-farm employment for rural women [22] [23]. Furthermore, given the complex nature of labor participation, which encompasses unpaid domestic and volunteer work alongside market-oriented labor, there is a more extensive body of literature addressing the latter, particularly regarding female labor supply in the context of economic development [24] [25]. However, studies on the impact of Internet use on labor participation often merely examine its effects on a single dimension, lacking a comprehensive perspective on various modes and types of labor participation. This study enhances the focus on older adults by analyzing the influence of Internet use on labor participation from three dimensions: overall participation, employment types, and modes of engagement. Additionally, beyond its direct effects, Internet use also indirectly influences labor participation through various mechanisms. This analysis deepens our understanding of how the internet affects labor participation behaviors.
2. Theoretical framework and research hypotheses
2.1. Internet Use and Labor Participation Among Middle-Aged and Elderly Individuals
In recent years, the rise and rapid development of digital technology have had a profound impact on China’s labor market. Numerous studies have confirmed that Internet use plays an active role in promoting non-agricultural employment [26] [27]. As a non-market mechanism, Internet use has been instrumental in expanding labor participation and stimulating labor market dynamics. First, the unique communicative advantages of the Internet broaden the scope of information dissemination, enabling job seekers to access relevant job opportunities more quickly and conveniently, thus increasing their chances of labor participation [28]. Additionally, studies have shown that Internet platforms facilitate the exchange and sharing of social resources, expanding social networks, increasing the likelihood of non-agricultural employment, and ultimately boosting income [29].
Secondly, utilizing Internet platforms allows the timely transmission of information about individuals’ potential to the labor market, increasing the likelihood of employment and reducing job search costs. Additionally, the Internet serves as a catalyst for the development of new industries and the expansion of job opportunities, simultaneously diversifying the types of employment available. On one hand, the information dissemination capabilities of the Internet broaden access to employment information, reducing job search costs and increasing the likelihood of formal labor participation [30] [31]. On the other hand, the use of Internet platforms has expanded opportunities for flexible employment, creating new employment prospects. The growing number of flexible workers in roles such as online streamers, food delivery couriers, and ride-hailing drivers reflects the rapid development of flexible jobs in the digital economy [32] [33]. Based on the above analysis, this study proposes the following hypotheses:
H1: Internet use has a positive impact on the overall labor participation of older adults.
H2: Internet use broadens employment modes for older adults, increasing the likelihood of wage employment and self-employment compared to agricultural labor participation.
H3: Internet use changes the forms of labor participation for older adults, increasing the likelihood of flexible and formal employment compared to agricultural labor participation.
2.2. Analysis of the Mechanisms of the Impact of Internet Use on the Labor Participation of Middle-aged and Elderly Groups
Theoretically, the dissemination of employment information, market conditions, and employment policies among older adults is often incomplete. As a result, older individuals may struggle to access the necessary channels to obtain relevant labor market information during their participation in the workforce. This information asymmetry increases their search costs and reduces their opportunities for non-agricultural employment. However, Internet use can mitigate such risks by enabling older adults to access a wealth of information related to government policies, market regulations, and employment opportunities more efficiently. By improving their awareness of information channels and enhancing their ability to identify suitable job positions, Internet use facilitates the quick matching of older adults to appropriate jobs in the labor market. In other words, the use of online platforms allows older adults to proactively seek employment information from various sources, alleviating the issue of insufficient job information. Additionally, the vast communication networks provided by the Internet allow older adults to obtain a greater amount of employment information at lower costs, reducing the unemployment risks caused by information asymmetry and helping them secure more satisfying jobs. According to the information effects theory, the Internet, as a critical tool for information dissemination, has a positive impact on employment outcomes [34]. Research also confirms that job seekers utilizing online information channels can reduce unemployment duration and increase employment opportunities [35]. Based on this theoretical analysis, this paper proposes Hypothesis 4:
H4: Information channels have a positive mediating effect in the process by which Internet use influences the overall labor participation of older adults.
According to social capital theory, social capital plays a significant role in an individual’s job-seeking process [36]. The interaction of middle-aged and older individuals with society and their connections with others reflect their social capital at the individual, group, and organizational levels, enabling them to access both tangible and intangible resources. The social networks formed by social capital facilitate timely access to labor market information, thereby increasing employment opportunities for older adults [37]. Specifically, older individuals who use the Internet more frequently are more likely to develop strong interpersonal networks. In favorable labor market conditions, these networks help them quickly acquire employment information and secure non-agricultural jobs. Thus, social capital assists older adults in building familiar social relationships and effectively gathering labor market information, thereby enhancing their overall labor participation. Additionally, older individuals who use Internet platforms can expand their family and social networks, enabling more frequent communication with others and receiving support for employment decisions, which in turn promotes labor participation. Therefore, it can be inferred that Internet use helps older adults broaden their social networks and leverage these networks to increase the likelihood of securing either employed or self-employed positions. Based on this theoretical analysis, this paper proposes Hypothesis 5:
H5: Social capital mediates the relationship between Internet use and overall labor participation of older adults, exerting a positive effect.
First, engaging in non-agricultural labor in the labor market typically requires a relatively high level of human capital, which many middle-aged and older individuals with lower human capital may lack due to limited knowledge and skills. This is particularly true for those who have reached the legal retirement age. However, the advent of Internet technology provides an opportunity and platform for individuals to independently learn new knowledge and skills, thereby enhancing their human capital and increasing their relative advantage in non-agricultural labor [38]. Thus, Internet use can improve human capital levels and promote non-agricultural labor participation among older adults. Second, through Internet platforms, middle-aged and older individuals can overcome time and situational constraints to actively collect, learn, and accumulate employment-related knowledge during their leisure time. This reduces learning costs and enhances their competitive advantage in the labor market, thereby increasing the likelihood of non-agricultural employment. Consequently, human capital may be a key factor influencing non-agricultural labor participation among older adults. Based on the above discussion, this paper proposes Hypothesis 6:
H6: Human capital mediates the relationship between Internet use and overall labor participation of older adults, exerting a positive effect.
Combining the above assumptions, the research framework of this paper is shown in Figure 1.
Figure 1. Research framework diagram
3. Data and Method
3.1. Sample
The data analyzed in this study are derived from the three iterations of the Chinese General Social Survey conducted in 2017, 2018, and 2021 (CGSS). The CGSS is primarily overseen and released by the Chinese Survey and Data Center (NSRC) at Renmin University of China. Among these editions, the CGSS 2021 dataset represents the most recent information available in this repository. The project has undertaken 15 annual surveys from 2003 to 2022, encompassing more than 10,000 households and totaling 1,620,036 individuals across all provinces, municipalities, and autonomous regions of China in a geographically diverse and representative manner. For this study, a composite dataset spanning three time periods—2017, 2018, and 2021—has been selected from the CGSS. The study focuses on a sample of middle-aged and elderly individuals aged between 45 and 75 years. After filtering out observations with missing key data and substantial gaps, the final dataset comprises 10,166 valid observations for analysis.
3.2. Measures
Labor force participation. Labor participation encompasses a wide range of activities, including both unpaid public welfare activities and paid employment. As the CGSS questionnaire lacked inquiries on unpaid public welfare activities, this study relying on previous research examined labor participation primarily through three dimensions [39]: non-agricultural labor participation(1=“Non-agricultural labor”;0=“No off-farm labor”), labor employment modalities(4=“self-employed labor”;3=“employed labor”;2=“agricultural labor”;1=“Not engaged in labor), and types of labor participation(4=“formal labor”; 3=“flexible labor”;2=“agricultural labor”;1=“Not engaged in labor”). This examination used the current engagement of economically remunerated labor by the middle-aged and elderly as the dependent variable.
Internet use. This research assessed Internet use among middle-aged and older adults in the previous year, considering their utilization of Internet media, including access through cell phones (1=“ rarely/sometimes/often/very often ”;0=“never ”).
Mechanism variables. In this study, the Internet is selected as a primary source of information, the frequency of leisure social activities and the frequency of leisure learning activities to reflect individual information source channels, social capital channels and human capital channels. These three factors were chosen to represent the individual’s information channel(1=“yes”;0=“no”), social capital(5=“very often”; 4=“often”;3=“sometimes”;2=“rarely”;1=“never”), and human capital1(5=“very often”; 4=“often”;3=“sometimes” ;2=“rarely”;1=“never”), respectively.
Control variables. Building on existing literature, this study controls for various characteristic variables that could influence the labor force engagement of middle-aged and elderly individuals across different levels, including individual, familial, social, and other relevant factors. Key considerations encompass aspects such as gender(1=“male”;0=“female”), age(year), marital status(1=“With partner”;0=“without partner”), educational attainment (3=“college and above”; 2=“middle and high”;1=“elementary and below”), nation(1=“han Chinese”;0=“minority”), current residence (1=“urban area”;0=“rural area”) , number of children(actual number of children in the family), annual household income(logarithmic) and pension insurance participation(1=“yes”;0=“no”). Perceived health status was measured by middle-aged and elderly individuals’ self-rated health scores (5=“very healthy”;1=“unhealthy”). Relative economic status of the family was measured by asking respondents about their family’s economic status belong to in your locality (5=“Well above average”;1=“well below average”). Moreover, due to variations in data years and disparate levels of economic development in different Chinese regions, this research also controls the data year characteristics and regional characteristics in order to better reflect the impact of Internet use on its labor participation.
The definitions and descriptive statistics for each variable are outlined in Table 1. Regarding non-farm labor participation among middle-aged and elderly individuals, the average non-farm labor value is 0.335, signifying a relatively low proportion of non-farm labor within the older age groups in the total sample. In terms of labor employment modes, the mean value for the entire sample is 1.941, indicating a position between non-participation in labor and agricultural labor. Concerning types of labor engagement, the mean value for the overall sample is 2.380, falling between engagement in agricultural labor and flexible labor. Notably, the mean value for non-farm labor participation is notably higher for Internet users compared to non-users. Specifically, 63.8% of the total sample are Internet users, with 43% participating in non-farm labor, whereas only 17% of non-Internet users engage in non-farm labor. Furthermore, a T-test demonstrates a significant disparity in non-farm labor participation between middle-aged and elderly Internet users and non-users.
Table 1. Variable definitions and descriptive statistics.
| Variable | Full sample | No Internet Use | Internet Use | ||
| Mean | S.D. | Mean | Mean | T-test | |
| Non- agricultural labor participation | 0.335 | 0.472 | 0.167 | 0.430 | -0.26*** |
| Labor employment modalities | 1.941 | 1.060 | 1.665 | 2.098 | -0.43*** |
| Type of labor participation | 2.380 | 0.952 | 2.225 | 2.467 | -0.24*** |
| Internet use | 0.638 | 0.481 | 0 | 1 | -1 |
| Internet as a primary source of information | 0.376 | 0.484 | 0.009 | 0.579 | -0.57*** |
| Frequency of leisure social activities | 2.698 | 1.085 | 2.648 | 2.727 | -0.08*** |
| Frequency of leisure learning activities | 1.978 | 1.111 | 1.536 | 2.228 | -0.69*** |
| Gender | 0.530 | 0.499 | 0.564 | 0.511 | 0.05*** |
| Age | 57.83 | 8.304 | 61.377 | 55.825 | 5.55*** |
| Marital status | 0.871 | 0.335 | 0.843 | 0.887 | -0.04*** |
| Educational attainment | 1.912 | 0.587 | 1.651 | 2.060 | -0.41*** |
| Nation | 0.944 | 0.229 | 0.937 | 0.948 | -0.01** |
| Health status | 3.426 | 1.022 | 3.179 | 3.565 | -0.39*** |
| Residence | 0.557 | 0.497 | 0.384 | 0.655 | -0.27*** |
| Number of children | 1.658 | 0.994 | 1.998 | 1.466 | 0.53*** |
| Annual household income | 10.59 | 1.729 | 9.961 | 10.948 | -0.99*** |
| Relative economic status of the family | 2.601 | 0.754 | 2.449 | 2.688 | -0.24*** |
| Pension insurance participation | 0.836 | 0.370 | 0.803 | 0.855 | -0.05*** |
| Region | 2.420 | 0.750 | 2.298 | 2.490 | -0.19*** |
| Year of data | 2018 | 1.516 | 2017.961 | 2018.442 | -0.48*** |
Firstly, this research delves into the influence of Internet utilization on non-farm labor force engagement among middle-aged and elderly individuals through the development of a binary logit model (Equation 1) specifically designed for their non-farm work. In this model, ‘work’ serves as a binary variable indicative of non-farm labor among individuals within the middle to older age brackets. Furthermore, ‘Internet use’ functions as the principal explanatory variable, reflecting the adoption of digital technology within the middle-aged and elderly populace. The control variables denoted as encompass a range of individual, household, region, and year-specific characteristics. Additionally,
is integrated as a term representing random disturbances within the model analysis. Through the utilization of this framework, the objective is to elucidate the relationship between Internet use and labor force participation within these distinct age cohorts.
P（work=1）=（α + βInternet +
+
） （1）
Secondly, this research will employ a multinomial logit model (Equation 2) to explore the impact of Internet use on labor participation among middle-aged and elderly individuals across various employment methods and types of labor engagement. In this model, different values of ‘k’ signify distinct labor scenarios for the middle-aged and elderly groups: k=1 denotes non-participation in labor, k=2 corresponds to engagement in agricultural labor, k=3 indicates involvement in informal and flexible labor, and k=4 represents participation in self-employment and formal labor. Notably, the reference group for this study comprises the middle-aged and elderly individuals who are not participating in the workforce.
P（work=k）=（α + βInternet +
+
） （2）
4. Empirical result analysis
4.1. Basic regression
Table 2 presents the results of the regression analysis examining the correlation between Internet use and labor force participation among middle-aged and elderly individuals, utilizing binary Logit and multivalued Logit models. In the first column of Table 2, it is evident that Internet use significantly impacts non-farm labor participation among this demographic. Specifically, after adjusting for other variables, the probability of non-farm labor participation for those in the middle-aged and elderly categories who use the Internet is 26.1 percentage points higher than those who do not. Moving to the second column, the influence of Internet use on labor practices shows varying effects. Internet use appears to strongly deter engagement in agricultural labor among middle-aged and elderly groups. Conversely, it has a notable positive influence on self-employed labor for this demographic, with a 0.324 unit increase in the probability of self-employment compared to non-users. However, the impact on employed labor proves to be non-significant, thereby testing Hypothesis 2. In the third column, the analysis of labor types demonstrates a significant relationship between Internet use and both flexible and formal labor among middle-aged and elderly populations. Each unit increase in Internet use corresponds to an 18.6% rise in the probability of engaging in flexible labor, while the effect on formal labor stands at 0.362 units, significant at the 1% threshold. Consequently, Internet use significantly contributes to flexible and formal labor practices among the middle-aged and elderly, thereby confirming Hypothesis 3.
Table 2. Benchmark regression results.
| Variable | （1） | （2） | （3） | ||||
| Non-agricultural labor | Agricultural labor | Employed labor | Self-employed labor | Agricultural labor | Flexible labor | Formal labor | |
| Internet use | 0.261*** | -0.514*** | 0.001 | 0.324*** | 0.065 | 0.186** | 0.362*** |
| (0.068) | (0.081) | (0.079) | (0.106) | (0.076) | (0.088) | (0.095) | |
| Gender | 0.972*** | 0.921*** | 1.324*** | 1.087*** | 1.107*** | 0.172** | 0.193** |
| (0.054) | (0.072) | (0.065) | (0.081) | (0.067) | (0.077) | (0.082) | |
| Age | 0.047 | -0.064 | -0.014 | -0.126 | 0.016 | 0.067 | 0.540*** |
| (0.064) | (0.066) | (0.073) | (0.090) | (0.062) | (0.070) | (0.079) | |
| Age2 | -0.002*** | -0.000 | -0.002** | -0.001 | -0.001** | -0.000 | -0.004*** |
| (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | (0.001) | |
| Marital status | -0.101 | 0.296*** | -0.106 | 0.129 | 0.214** | 0.166 | 0.260** |
| (0.090) | (0.115) | (0.100) | (0.141) | (0.097) | (0.103) | (0.113) | |
| Middle school and high school (elementary and below as reference) | 0.251*** | -0.199** | 0.139 | 0.218* | 0.235*** | 0.771*** | 1.040*** |
| (0.079) | (0.077) | (0.091) | (0.114) | (0.074) | (0.093) | (0.117) | |
| College and above | 0.870*** | -1.133*** | 0.801*** | 0.586*** | 2.250*** | 1.992*** | 2.368*** |
| (0.106) | (0.331) | (0.125) | (0.159) | (0.244) | (0.257) | (0.264) | |
| Nation | 0.224* | -0.074 | 0.296** | -0.031 | 0.078 | -0.063 | 0.079 |
| (0.124) | (0.135) | (0.149) | (0.166) | (0.134) | (0.162) | (0.188) | |
| Fairly unhealthy (very unhealthy as reference) | 0.705*** | 0.452*** | 0.767*** | 1.024*** | 0.419** | -0.024 | -0.128 |
| (0.221) | (0.172) | (0.243) | (0.357) | (0.165) | (0.178) | (0.215) | |
| Fair | 0.955*** | 0.727*** | 1.190*** | 1.270*** | 0.766*** | 0.145 | 0.209 |
| (0.213) | (0.169) | (0.233) | (0.347) | (0.162) | (0.174) | (0.206) | |
| Fairly healthy | 1.311*** | 0.783*** | 1.595*** | 1.504*** | 0.949*** | 0.007 | 0.107 |
| (0.212) | (0.169) | (0.232) | (0.346) | (0.162) | (0.175) | (0.206) | |
| Very healthy | 1.329*** | 0.834*** | 1.597*** | 1.617*** | 0.908*** | -0.211 | 0.069 |
| (0.219) | (0.186) | (0.240) | (0.353) | (0.178) | (0.200) | (0.228) | |
| Residence | 0.293*** | -3.126*** | -0.416*** | -0.669*** | 0.002 | 1.357*** | 2.169*** |
| (0.070) | (0.125) | (0.079) | (0.096) | (0.080) | (0.092) | (0.116) | |
| Number of children in the family | 0.115*** | 0.217*** | 0.120*** | 0.297*** | 0.068* | -0.116*** | -0.311*** |
| (0.041) | (0.038) | (0.043) | (0.046) | (0.036) | (0.043) | (0.055) | |
| Annual household income | 0.275*** | -0.020 | 0.293*** | 0.243*** | 0.129*** | 0.066*** | 0.166*** |
| (0.035) | (0.018) | (0.031) | (0.039) | (0.018) | (0.020) | (0.030) | |
| Below average (well below average) | 0.291** | -0.072 | 0.227* | 0.501** | 0.143 | 0.163 | 0.191 |
| (0.125) | (0.125) | (0.138) | (0.214) | (0.118) | (0.142) | (0.167) | |
| Average | 0.202 | -0.019 | 0.026 | 0.663*** | 0.155 | 0.296** | 0.277* |
| (0.126) | (0.126) | (0.139) | (0.214) | (0.120) | (0.144) | (0.168) | |
| Above average | 0.429*** | -0.004 | 0.050 | 1.262*** | 0.497*** | 0.415** | 0.343 |
| (0.154) | (0.200) | (0.176) | (0.243) | (0.187) | (0.211) | (0.230) | |
| Well above average | 0.110 | 0.657 | -0.010 | 1.011 | 0.391 | -0.292 | -0.533 |
| (0.438) | (0.530) | (0.531) | (0.632) | (0.520) | (0.667) | (0.792) | |
| Pension insurance participation | -0.012 | 0.367*** | 0.237*** | 0.038 | 0.489*** | 0.218** | 0.682*** |
| (0.075) | (0.087) | (0.087) | (0.104) | (0.079) | (0.097) | (0.121) | |
| Year 2018 (Year 2017 as reference) | -0.128** | -0.104 | -0.183*** | -0.155* | -0.119 | 0.135 | -0.051 |
| (0.058) | (0.079) | (0.070) | (0.088) | (0.073) | (0.085) | (0.090) | |
| Year 2021 | 0.138* | 0.063 | 0.163* | 0.001 | 0.142 | 0.196* | 0.186* |
| (0.072) | (0.093) | (0.084) | (0.106) | (0.089) | (0.103) | (0.109) | |
| Central (western region as reference) | 0.119 | -0.243*** | 0.063 | -0.132 | -0.051 | 0.088 | 0.143 |
| (0.088) | (0.094) | (0.108) | (0.123) | (0.095) | (0.119) | (0.144) | |
| Eastern | 0.441*** | -0.994*** | 0.278*** | -0.259** | -0.009 | 0.147 | 0.395*** |
| (0.082) | (0.095) | (0.098) | (0.114) | (0.090) | (0.110) | (0.128) | |
| N | 10068 | 10068 | 10068 | 10068 | 10068 | 10068 | 10068 |
| Pseudo R2 | 0.301 | 0.299 | 0.237 | ||||
Note: (1) The data reported in the table are marginal effects. (2) Robust standard errors in parentheses, with not engaged labor force participation as the benchmark group output. (3) * p < 0.1, ** p < 0.05, *** p < 0.01.
4.2 Heterogeneity analysis
In this study, China’s middle-aged and elderly people are categorized into three major regions (eastern, central, and western regions), two residential areas (urban and rural) and two age groups (45-60 and 61-75). Table 4 demonstrates the results of subset regressions for various regions, distinguishing between urban and rural household settings, and age groups. The country is segmented into three primary regions – eastern, central, and western – based on the classification criteria established by the National Bureau of Statistics of China. The regression findings reveal a significant positive impact of Internet use on non-agricultural labor participation among middle-aged and elderly populations in the central region, with a significance level of 1%, surpassing that of the eastern and western regions (Table 4, Panel A). This outcome suggests that the role of Internet utilization in promoting non-agricultural labor engagement among middle-aged and older individuals is particularly pronounced in the central region. This trend may be attributed to the increasing prevalence of Internet technology in recent years, with China’s Internet penetration rate displaying a westward migration pattern. Based on the data from December 2020, there was a significant 40% year-on-year increase in the number of Internet users in central and western China compared to 2016. Moreover, this growth outperformed the eastern region by 12.4 percentage points.
Next, the influence of Internet use on non-agricultural labor participation among middle-aged and older individuals varies based on urban-rural distinctions. The study further categorizes middle-aged and elderly individuals into two segments based on their residence: one segment comprises those residing in urban areas, while the other consists of individuals living in rural settings. The regression findings indicate that the utilization of digital technology notably enhances non-agricultural labor engagement among rural middle-aged and elderly individuals in comparison to their urban counterparts (Table 4, Panel B). This disparity may be attributed to the rapid pace of urbanization, leading to a significant migration of rural laborers from agricultural to non-agricultural sectors. Additionally, the diminishing gap in Internet access between urban and rural regions in recent years has rendered digital technology more effective in facilitating non-agricultural labor among rural middle-aged and elderly populations as opposed to their urban counterparts.
Lastly, the impact of Internet use on non-farm labor participation among middle-aged and older individuals is examined based on the age heterogeneity within these groups. The subgroup analyses reveal that the influence of Internet utilization on non-farm labor engagement is more pronounced in the 45-60 age bracket compared to the 61-75 age bracket (Table 4, Panel C). This disparity can be attributed to several factors. Firstly, older individuals tend to have limited openness to new technologies, resulting in lower Internet use rates and a lack of access to updated labor information. Secondly, individuals above 60 years old often encounter challenges in the competitive labor market, facing difficulties in securing employment and demonstrating proactive engagement in non-agricultural labor activities.
Table 3. Split-sample regression results by region, urban/rural, and age.
| （A） | （1） | （2） | （3） | |
| Eastern region | Central Region | Western region | ||
| Internet use | 0.146 (0.097) | 0.399***(0.124) | 0.347**(0.164) | |
| Control variables | Control | Control | Control | |
| N | 5849 | 2607 | 1606 | |
| Pseudo R2 | 0.354 | 0.260 | 0.257 | |
| （B） | （1） | （2） | ||
| Urban | Rural | |||
| Internet use | 0.306***(0.111) | 0.373***(0.090) | ||
| Control variables | Control | Control | ||
| N | 5630 | 4438 | ||
| Pseudo R2 | 0.411 | 0.220 | ||
| （C） | （1） | （2） | ||
| 45<=Age<=60 | 61<=Age<=75 | |||
| Internet use | 0.667*** (0.073) | 0.385***(0.143) | ||
| Control variables | Control | Control | ||
| Observations | 6284 | 3784 | ||
| Pseudo R2 | 0.165 | 0.065 | ||
Note: (1) 45-60 is the middle-aged group, 61-75 is the elderly group. (2) The rest of the table notes are the same as in Table 2.
4.3 Mechanism analysis
This study utilizes the KHB method to examine the mediating effects of the a forementioned three mechanism variables [40]. Mediating effects entail the involvement of core explanatory variables through mechanism variables, thereby influencing the explained variable. Conversely, direct effects denote the impact of the core explanatory variable on the explained variable without intermediary variables. Table 7 presents the outcomes of the KHB methodology, and both Logit and OLS model estimations reveal that the mediation effect coefficients of the information channel and human capital are markedly positive, with an exception being the negative coefficient associated with the mediation effect of social capital. This highlights the crucial roles of information channels and human capital as conduits through which Internet use fosters non-farm labor participation among middle-aged and elderly individuals. The findings suggest a significant positive influence of Internet use on non-farm labor participation by enriching the information channels and human capital of this demographic. Consequently, Hypotheses 4 and 6 are corroborated, while Hypothesis 5 remains unverified. Examination of columns (2) and (4) in Table 7 reveals the magnitude of mediating effects exerted by the three mechanism variables in both models, with the information channel accounting for approximately 57% and 9% of these effects, and human capital contributing around 9% and 14%, respectively. In the Logit and OLS models, the mediating effect of Internet use on non-farm labor corresponds to 59.18% and 89.13% of the total effect, respectively. This indicates that a substantial proportion, nearly 74%, of the impact of Internet use on non-farm labor among middle-aged and elderly individuals is transmitted through the information channel and human capital.
Table4. Mediation effect tests for the three mechanism variables based on the KHB approach.
| Explained variables | Explanatory variables | Intermediary variable | （1） | （2） | （3） | （4） |
| Logit | 0LS | |||||
| intermediary effect | intermediary effect／ aggregate effect（％） | intermediary effect | intermediary effect／ aggregate effect（％） | |||
| Non-agricultural labor participation | Internet use | Information channels | 0.153*** (0.027) | 57.3% | 0.038*** (0.004) | 9.7% |
| social capital | -0.019*** (0.005) | -7.3% | -0.003*** (0.001) | -7.0% | ||
| human capital | 0.025** (0.010) | 9.8% | 0.006*** (0.002) | 14.0% | ||
Note: (1) The mediating effect of Internet use on non-farm labor under the Logit model is 0.158, the direct effect is 0.108, and the total effect is 0.267, with the mediating effect accounting for 59.18% of the total effect; the mediating effect of digital technology use on non-farm labor under the OLS model is 0.041, the direct effect is 0.005, and the total effect is 0.046, with the mediating effect accounting for 89.13%. (2) The rest of the table notes are the same as in Table 2.
4.4 Robustness checks
Based on the preceding regression analysis, it is evident that Internet use plays a varied and significant role in the labor force participation of middle-aged and elderly individuals. The extent of individuals’ Internet use can differ based on their occupational characteristics and work environment. Consequently, the model utilized in the preceding section may encounter endogeneity issues stemming from sample “self-selection bias,” necessitating robustness testing of the regression outcomes. Due to constraints related to data variables, this study encounters challenges in establishing valid instrumental variables. To further examine the stability of Internet use’s impact on labor participation among the middle-aged and elderly cohorts, this segment will validate the data analysis results from the previous section by assessing the engagement of these groups in non-agricultural labor participation through the Propensity Score Matching Method (PSM).
When employing the Propensity Score Matching (PSM) method, it is essential to assess the balance assumption, as detailed in Table 8. Following matching, the standardized deviations for the majority of variables decreased, with all reductions exceeding 50%, signifying improved data balance. Moreover, post-matching, the absolute standardized deviations of most variables fell within the 20% threshold. In line with established criteria [41]. the absolute standardized deviations of each covariate post-matching were within the acceptable range of 20%, affirming the effectiveness of the matching process. Overall, the disparities between the treatment and control groups diminished significantly post-matching, and the p-values for the majority of variables supported the initial hypothesis that no systematic differences existed between the experimental and control groups. Therefore, the matched samples generated through PSM demonstrate satisfactory balance based on the testing outcomes.
Table 5. Equilibrium test results.
| Variable | Sample | Mean | T-test | ||||
| Treated | Control | bais（%） | Reduct bais（%） | T 值 | P>T | ||
| Gender | Unmatched | 0.51009 | 0.56312 | -10.6 | -5.12 | 0.000 | |
| Matched | 0.51056 | 0.50818 | 0.5 | 95.5 | 0.27 | 0.789 | |
| Age | Unmatched | 55.812 | 61.338 | -70.2 | -33.87 | 0.000 | |
| Matched | 55.98 | 56.751 | -9.8 | 86.1 | -5.76 | 0.000 | |
| Marital status | Unmatched | 0.88711 | 0.84454 | 12.5 | 6.14 | 0.000 | |
| Matched | 0.88792 | 0.8822 | 1.7 | 86.6 | 1.01 | 0.315 | |
| Middle school and high school | Unmatched | 0.67531 | 0.59868 | 16 | 7.75 | 0.000 | |
| Matched | 0.68868 | 0.75472 | -13.8 | 13.8 | -8.29 | 0.000 | |
| College and above | Unmatched | 0.19301 | 0.02591 | 55.5 | 24.41 | 0.000 | |
| Matched | 0.17669 | 0.13431 | 14.1 | 74.6 | 6.58 | 0.000 | |
| Nation | Unmatched | 0.94829 | 0.9366 | 5 | 2.45 | 0.014 | |
| Matched | 0.94809 | 0.94317 | 2.1 | 57.9 | 1.22 | 0.223 | |
| Fairly unhealthy | Unmatched | 0.11056 | 0.22133 | -30.1 | -15.09 | 0.000 | |
| Matched | 0.11256 | 0.1143 | -0.5 | 98.4 | -0.31 | 0.757 | |
| Fair | Unmatched | 0.29907 | 0.2919 | 1.6 | 0.76 | 0.449 | |
| Matched | 0.30052 | 0.2548 | 10 | -537.5 | 5.74 | 0.000 | |
| Fairly healthy | Unmatched | 0.41242 | 0.31946 | 19.4 | 9.27 | 0.000 | |
| Matched | 0.40991 | 0.42451 | -3 | 84.3 | -1.66 | 0.096 | |
| Very healthy | Unmatched | 0.15528 | 0.10447 | 15.2 | 7.14 | 0.000 | |
| Matched | 0.15399 | 0.16939 | -4.6 | 69.7 | -2.35 | 0.019 | |
| Residence | Unmatched | 0.6573 | 0.38506 | 56.6 | 27.38 | 0.000 | |
| Matched | 0.64979 | 0.65979 | -2.1 | 96.3 | -1.18 | 0.238 | |
| Number of children in the family | Unmatched | 1.4646 | 1.9978 | -53.4 | -26.79 | 0.000 | |
| Matched | 1.4744 | 1.4623 | 1.2 | 97.7 | 0.82 | 0.413 | |
| Annual household income | Unmatched | 10.948 | 9.9608 | 57.2 | 28.58 | 0.000 | |
| Matched | 10.92 | 10.82 | 5.8 | 89.8 | 2.8 | 0.005 | |
| Below average | Unmatched | 0.32407 | 0.39305 | -14.4 | -6.99 | 0.000 | |
| Matched | 0.32799 | 0.30481 | 4.8 | 66.4 | 2.8 | 0.005 | |
| Average | Unmatched | 0.51832 | 0.45314 | 13.1 | 6.29 | 0.000 | |
| Matched | 0.51881 | 0.54786 | -5.8 | 55.4 | -3.27 | 0.001 | |
| Above average | Unmatched | 0.10528 | 0.04465 | 23.2 | 10.62 | 0.000 | |
| Matched | 0.10065 | 0.09287 | 3 | 87.2 | 1.48 | 0.140 | |
| Well above average | Unmatched | 0.00373 | 0.00413 | -0.7 | -0.32 | 0.752 | |
| Matched | 0.00286 | 0.00524 | -3.8 | -483.9 | -2.1 | 0.035 | |
| Pension insurance participation | Unmatched | 0.85637 | 0.80458 | 13.8 | 6.78 | 0.000 | |
| Matched | 0.85474 | 0.84283 | 3.2 | 77 | 1.87 | 0.062 | |
| Year 2018 | Unmatched | 0.38758 | 0.37679 | 2.2 | 1.07 | 0.285 | |
| Matched | 0.39387 | 0.35434 | 8.1 | -266.5 | 4.59 | 0.000 | |
| Year 2021 | Unmatched | 0.26413 | 0.14581 | 29.6 | 13.86 | 0.000 | |
| Matched | 0.25052 | 0.32973 | -19.8 | 33 | -9.83 | 0.000 | |
| Central | Unmatched | 0.22422 | 0.32222 | -22.1 | -10.83 | 0.000 | |
| Matched | 0.22639 | 0.29084 | -14.6 | 34.2 | -8.28 | 0.000 | |
| Western | Unmatched | 0.63354 | 0.4876 | 29.7 | 14.39 | 0.000 | |
| Matched | 0.62883 | 0.5812 | 9.7 | 67.4 | 5.47 | 0.000 | |
In order to enhance the stability of the matching outcomes, this study employs nearest neighbor matching (n=2), caliper matching, and kernel matching techniques. Table 9 presents the average treatment effect (ATE) for the experimental group, average treatment effect for the control group (ATU), and overall ATE resulting from the application of these three distinct Propensity Score Matching (PSM) approaches. The findings indicate that by accounting for variations in several observable variables using the PSM method, the ATE values range between 0.0811 and 0.0972. This suggests that utilizing the Internet leads to an increase in nonfarm labor force participation among middle-aged and elderly individuals by approximately 8.11 to 9.72 percentage points, aligning closely with the estimates derived from the baseline model in the preceding section. In conclusion, following adjustments for endogeneity and selectivity bias through PSM, the analysis reinforces the notion that Internet use continues to positively influence non-farm labor engagement among middle-aged and older populations.
Table 6. PSM results for non-farm labor participation.
| Nearest neighbor matching（n=2） | Caliper matching | Kernel matching | |
| ATT | 0.1256 | 0.1010 | 0.1093 |
| ATU | 0.0476 | 0.0459 | 0.0523 |
| ATE | 0.0972 | 0.0811 | 0.0885 |
Notes: (1) For caliper matching, the caliper is chosen to be 0.01. (2) The kernel matching defaults to a quadratic kernel with a bandwidth of 0.01.
5. Discussion
With the rapid advancement of digital technology and the increasing aging of the population, examining the impact of Internet use on labor participation among middle-aged and elderly individuals is of significant importance. Such an exploration not only helps these individuals boost their economic income and improve their financial self-sufficiency but also empowers them to play an active role in society, enhancing their sense of social engagement, contribution, and overall quality of life in later years. Existing research has largely focused on the effects of Internet use on labor market issues, such as labor supply, gender wage gaps, urban-rural wage disparities, and poverty vulnerability. However, relatively few studies have addressed the relationship between Internet use and labor participation behavior specifically among middle-aged and elderly groups. The contributions of this study are threefold: first, by utilizing micro-level data, this research provides a comprehensive analysis of the effects of Internet use on labor participation, focusing on overall labor participation, employment types, and labor engagement forms, thereby enriching the existing literature. Second, beyond the direct impact of Internet use on labor participation, the study also examines the indirect mechanisms through which Internet use influences labor behavior, specifically focusing on non-farm labor participation among the middle-aged and elderly, thus deepening the analysis. Finally, the study further investigates whether differences in Internet use among various subgroups of middle-aged and elderly individuals lead to differential impacts on their non-farm labor participation, thereby enhancing the accuracy of the findings and offering valuable insights for more targeted policy formulation.
This study also has several limitations. First, due to the limitations of the CGSS questionnaire data, the core explanatory variable for Internet use is based solely on the question, “In the past year, have you used Internet media (including mobile Internet access)?” This measure only captures whether middle-aged and elderly individuals use the Internet, without accounting for critical details such as duration, frequency, or content of use. Future research should aim to incorporate more comprehensive variables. Second, since this study utilizes mixed cross-sectional data from three CGSS waves over a long time span, a universally suitable and effective instrumental variable could not be identified to address the endogeneity issue arising from potential reverse causality between Internet use and labor force participation. Although the Propensity Score Matching (PSM) method was used for robustness checks, future research should focus on developing more robust strategies to address endogeneity.
6. Conclusions and Policy Recommendations
Against the backdrop of rapid digital technology development and a deepening aging population in China, ensuring the continued labor participation of middle-aged and elderly individuals is a key strategy for actively addressing population aging and promoting sustainable economic development. Building on theoretical analysis, this study employs data from the 2017, 2018, and 2021 waves of the China General Social Survey (CGSS) and utilizes various methods, including the Logit model, KHB method, and Propensity Score Matching, to empirically analyze the impact of Internet use on the labor participation of middle-aged and elderly individuals. The study reaches the following conclusions:
（1）Internet use significantly promotes non-farm labor participation among middle-aged and elderly individuals. Compared to those who do not use the Internet, middle-aged and elderly individuals who do are much more likely to engage in non-farm labor. Specifically, Internet use has a notable positive effect on self-employment, with Internet users having a higher probability of engaging in self-employment than non-users. Additionally, Internet use significantly boosts participation in both flexible and formal employment among this demographic.
（2）The effect of Internet use on non-farm labor participation varies across different regions, age groups, and rural-urban settings. The promotion effect is particularly pronounced in the central regions of China. Furthermore, compared to urban middle-aged and elderly individuals, Internet use more significantly encourages non-farm labor participation among rural counterparts. Internet use also has a more pronounced impact on non-farm labor participation among the middle-aged group (45-60 years) than among older individuals (61-75 years).
（3）By using the Internet for information acquisition, learning, and social interaction, middle-aged and elderly individuals expand their information sources, thereby improving both their access to information and their human capital. This, in turn, promotes their participation in non-farm labor.
Therefore, in the context of rapid digital technology development and deepening population aging, it is essential to effectively leverage the Internet’s role in promoting non-farm labor participation among middle-aged and elderly individuals, harnessing the advantages of elderly human resources, and supporting sustainable economic development. Based on this, the following three recommendations are proposed:
（1）The government should prioritize training middle-aged and elderly individuals in digital skills, particularly focusing on those with lower education levels who are willing to work. Special employment services should be provided to improve their employability and skill levels in the digital economy, bridging the digital divide. This will help unlock the labor potential of the elderly, promote diverse employment forms, and achieve high-quality employment among the elderly, thereby alleviating the pressure of population aging on the pension system.
（2）The government should increase funding for regions with insufficient Internet development, promoting the balanced development of Internet infrastructure and technology. Advanced digital technologies from eastern cities should be extended to the western regions and rural areas, ensuring that digital platforms are more widely accessible to rural residents in central and western areas. This will foster the positive impact of the Internet on non-farm employment, raise incomes for middle-aged and elderly populations in these regions, and reduce income disparities.
（3）The government should enhance the regulation of Internet information to curb the spread of misinformation and create a safer, more efficient market for employment information-sharing platforms. This would improve the legal framework, standardization, and transparency of platform governance, safeguarding the legal rights of middle-aged and elderly individuals. At the same time, efforts should be made to increase their human capital accumulation. By establishing specialized employment service platforms for the elderly, providing targeted re-employment training, and offering subsidies for employer training, the government can help improve the knowledge and skills of middle-aged and elderly individuals.
Author Contributions: YD contributed in Writing – original draft, Conceptualization, Data curation, Formal analysis, Methodology, and Writing – review & editing. HY contributed in Funding acquisition, editing and supervision of the paper. All authors contributed to the article and approved the submitted version.
Funding: This research was funded by the Doctoral Research Initiation Fund Project of Nanyang Institute of Technology (Project No.NGBJ-2020-22) and the Soft Science Project of Henan Province (Grant No. 16242400410469).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: The data presented in this study are available on http://www.cnsda.org/index.php?r=projects/index (accessed on 18 January 2024).
Conflicts of Interest: The authors declare no conflicts of interest.
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