Automation, employment insecurity, and social policy preference

Labour automation is not a new phenomenon. Since the 1970s, automated technologies, particularly industrial robots, have been increasingly installed in manufacturing sectors and were designed to automate repetitive manual tasks. The recent technological advancements in AI and ML in the ‘Fourth Industrial Revolution’ further unleashed the capabilities of automated technologies (Frey and Osborne, Reference Frey and Osborne2017). These innovations along with advanced robotic engineering empower the automated technologies to not only take over the manual tasks but also increasingly replace a wide range of cognitive tasks, such as handwriting recognition, fraud detection, and medical diagnosis (Oxford Economics, Reference Economics2019).

Moreover, breathtaking technological advances in AI and ML have reduced the unit cost of robotics, making wider use of automated technologies possible (Frey and Osborne, Reference Frey and Osborne2017). An increasing number of industries are accelerating the automation of their production. According to the World Robotics 2020 Industrial Robots Report, the number of industrial robots operating in factories totaled 2.7 million in 2019, registering an 85 per cent increase since 2014 (International Federation of Robotics, 2020). As the largest single-nation labour market, China is one of the global leaders in automation installation (Liu and Wang, Reference Liu and Wang2019). The ‘Made in China 2025’ blueprint envisaged by the Chinese government has accelerated investment in automation. China has been the largest robot market worldwide since 2013 and continues to register rapid growth in this market (International Federation of Robotics, 2018).

Automated technologies combined with AI and ML are profoundly changing various aspects of socioeconomic activities in the long term, such as bolstering industrial productivity and creating new occupations. Nonetheless, they also inevitably bring out far-reaching labour market impacts, such as changing the nature of work, shifting the employment composition, and generating unemployment. Many studies have shown that employment shares for routine occupations consisting of repetitive and codifiable tasks have steadily declined (Autor et al., Reference Autor, Levy and Murnane2003; Biagi and Sebastian, Reference Biagi, Sebastian and Zimmermann2020). As a result, people in routine occupations are considered as the ‘losers of automation’. Manyika et al. (Reference Manyika, Chui, Miremadi and Bughin2017) estimated that 60 per cent of the current occupations in forty-six major economies are automatable for at least 30 per cent of their constituent work activities. The widening relative difference in productivity between automation and manual labour is expected to lead profit-minded firms to substitute human labour with automated production. In China alone, it is predicted that the increasing use of industrial robots will lead to the lay-off of more than 11 million workers by 2030 (Oxford Economics, Reference Economics2019).

Based on the self-interest thesis, the social policy literature has voluminously established that exposure to various economic risks such as unemployment stimulates people’s demand for the expansion of social spending and public provision of welfare (Anderson and Pontusson, Reference Anderson and Pontusson2007; Rehm, Reference Rehm2009). Social protection is not only redistributive in nature, but ‘also has an insurance aspect’ (Iversen and Soskice, Reference Iversen and Soskice2001), insuring individuals against unemployment risks. The existing literature has widely found that individuals’ social policy preference is a function of unemployment risk (Rehm, Reference Rehm2009; Alesina and Giuliano, Reference Alesina, Giuliano, Benhabib, Bisin and Jackson2011; He et al., Reference He, Zhang and Qian2022). Without doubt, employment insecurity triggered by job automation replacement represents a major risk for employees of affected occupations, who therefore expect greater non-market protection, especially social welfare. Following this, emerging studies posit that individuals’ social policy preferences vary among occupations according to their degree of exposure to automation replacement. Those with higher automation replacement risks are expected to express stronger preference for social policies (Thewissen and Rueda, Reference Thewissen and Rueda2019; Sacchi et al., Reference Sacchi, Guarascio, Vannutelli, Careja, Emmenegger and Giger2020; Busemeyer and Sahm, Reference Busemeyer and Sahm2021; Im and Komp-Leukkunen, Reference Im and Komp-Leukkunen2021). Needless to say, such social policy demands exert political pressure on governments in liberal democratic societies through organised trade union activities and, ultimately, electoral links. Even in political systems where such universal electoral links are largely absent, policy-makers may still be motivated to enact social policy provision in order to safeguard political legitimacy or mitigate social discontent (Ngok and Huang, Reference Ngok and Huang2014).

It is crucial to clarify that unemployment risk does not equate to actual job loss. In spite of their greater vulnerability to displacement, routine workers’ elevated unemployment risk may not necessarily materialise into actual job loss. Thus far, only a fraction of routine workers worldwide has actually been laid off due to automation displacement (Kurer, Reference Kurer2020). Yet, many studies have elucidated that even perceived unemployment threat among routine workers still results in significant attitudinal reactions (Thewissen and Rueda, Reference Thewissen and Rueda2019; Sacchi et al., Reference Sacchi, Guarascio, Vannutelli, Careja, Emmenegger and Giger2020; Busemeyer and Sahm, Reference Busemeyer and Sahm2021; Im and Komp-Leukkunen, Reference Im and Komp-Leukkunen2021).

National context

The past four decades have seen a phenomenal transformation of China’s welfare system. Embedded into the communist planned economy, the old urban welfare system was built primarily on danwei (work units), especially the state-owned enterprises (SOEs) that supported a ‘mini welfare system’. Commencing in the 1980s, China’s transition towards a market economy vigorously unleashed the economic potential of the nation, but also swiftly dismantled its old welfare system (Leung and Nann, Reference Leung and Nann1995). Lifetime employment, a hallmark of communist superiority, was no longer guaranteed, giving way to labour market liberalisation. Guided by the principle of ‘development first, redistribution later’, China’s welfare arrangements vividly represented the doctrine of East Asian ‘welfare productivism’ (Mok et al., Reference Mok, Kühner and Huang2017).

Major social policy reforms were accelerated from the early 2000s, guided by the professed goal of building a ‘harmonious society’ (Ngok and Huang, Reference Ngok and Huang2014). Contributory social insurance programmes in the areas of old-age pension, health care, unemployment, and work injury were instituted in urban formal sectors, with employers also playing a financing role. Empowered by its growing fiscal muscle, the government also gradually established similar social insurance programmes for employees in urban informal sectors as well as for rural residents, with substantial fiscal subsidies injected. In the meantime, the non-contributory dibao programme (Minimum Livelihood Guarantee Scheme) was also launched to serve as the country’s social safety net (Guo et al., Reference Guo, He and Wang2021).

A salient feature of the Chinese welfare system is its high level of fragmentation. Welfare entitlements are largely determined by local citizenship, urban/rural status, and occupation (Ringen and Ngok, Reference Ringen, Ngok and Yi2017; Zhu and Walker, Reference Zhu and Walker2018). Because most welfare programmes are financed and administered at the local level, various factors especially the fiscal capacity of local governments have shaped ‘welfare regionalism’, leading to substantial variance in the level of financial protection across localities (Mok and Wu, Reference Mok and Wu2013). For example, in terms of generosity, the per capita expenditure for urban social health insurance in Beijing is more than four times the outlay in Jiangxi Province (Huang, Reference Huang2015). The social pension payment in Beijing is more than three times the standard in Qinghai Province (Zhu and Walker, Reference Zhu and Walker2018). It would be wrong, however, to assume that the gap is solely explained by the level of economic affluence, because Guo et al. (Reference Guo, He and Wang2021) have revealed an evident gap in terms of dibao standard even within provinces of similar socioeconomic status. Similar disparities also widely exist between occupations, with urban formal sector employees and civil servants enjoying more generous benefits (Ringen and Ngok, Reference Ringen, Ngok and Yi2017; Zhu and Walker, Reference Zhu and Walker2018).

In fledged welfare states, robust unemployment protection programmes constitute a pivotal pillar of redistributive policies, providing a last-resort safety net for the laid-off (Clasen and Clegg, Reference Clasen and Clegg2006). In contrast, the residualist welfare philosophy, compounded by informalisation of workforce, has not created an adequate unemployment protection system in many developing countries, including China (He et al., Reference He, Zhang and Qian2022). The unemployment insurance programme mainly covers people at low risk but leaves the high-risk population unprotected, resulting in a remarkable mismatch between risk profile and social protection. Exacerbating the situation is the stark contrast between the large sum of surplus accumulated and the low benefit rate of the unemployment insurance programme (Liu et al., Reference Liu, Liu and Huang2016). Its income replacement rate (less than 20 per cent) is much lower than that of similar unemployment insurance programmes in other countries (Jiang et al., Reference Jiang, Qian and Wen2018). Given the shallow protection that the Chinese welfare system provides, especially unemployment protection, individuals threatened by employment insecurity tend to demonstrate a low expectation for redistributive policies (He et al., Reference He, Zhang and Qian2022).

Theories and hypotheses

It is widely known that social policies not only redistribute wealth, but also provide social insurance to individuals against a range of social risks, particularly old age, unemployment, and illness (Alesina and Giuliano, Reference Alesina, Giuliano, Benhabib, Bisin and Jackson2011). This ‘insurance motive’ stimulates people to support social policies to insure themselves against individual risks (Iversen and Soskice, Reference Iversen and Soskice2001). The received wisdom maintains that automation risk – an important risk for individuals in the labour market – creates demand for redistributive policies. Generous social transfer programmes such as unemployment insurance and minimal wages can compensate for the expected income loss and unemployment caused by automation replacement. However, this line of argument has yielded mixed results. By using cross-country data in Europe, some studies have found that individuals in occupations at greater risk of workplace automation show significantly higher levels of support for redistributive policies in order to compensate for expected income loss due to unemployment (Rehm, Reference Rehm2009; Thewissen and Rueda, Reference Thewissen and Rueda2019). Other studies, however, haven’t found a significant relationship. Dermont and Weisstanner (Reference Dermont and Weisstanner2020) contended that redistributive preference in the early stream of studies was essentially taken as a shorthand to capture both the redistributive and insurance functions of social policies; lumping both dimensions into one composite measure may have masked the considerable attitudinal heterogeneity across distinctive social policy domains. Ultimately, individuals’ preferences for redistribution depend on their cost-benefit calculations regarding redistributive programmes.

It is the central contention of this study that automation risk may not necessarily strengthen individuals’ redistributive preference in China. Gingrich and Ansell (Reference Gingrich and Ansell2012) illuminated that individuals’ labour market risk matters for their social policy preferences only when welfare benefits are employment-dependent. In other words, for welfare programmes in which entitlement is not tied to employment, being paid off or not may not engender discernable attitudinal cleavages in terms of popular support. As a result, one’s labour market risk may exert no influence on his/her preference for employment-independent welfare benefits, simply because ‘it’s not within my reach’ or ‘I’m entitled to it anyway’ (He et al., Reference He, Zhang and Qian2022).

Various studies have reported wide agreement among Chinese people that the government should assume more responsibility in redistribution (Huang, Reference Huang2019; Dalen, Reference Dalen2021). Yet, the benefits of redistributive programmes in China are either linked to the provision of general social services to all citizens, such as basic education, or are employment-independent. For example, the generous fiscal subsidies offered to urban-rural resident social insurance programmes and various cash transfer programmes are not tied to one’s employment status. As such, where redistribution benefits are offered to people as citizens, rather than in the capacity of employees, higher risks of unemployment due to automation do not necessarily trigger stronger demand for redistribution. Put simply, workplace automation or not, eligible individuals are still entitled to benefits from redistributive policies. The above logic leads us to posit:

The existing literature maintains that major exogenous shocks or threats induce individuals to favour immediate, short-term compensation at the expense of gradual, long-term social protection (Anderson and Pontusson, Reference Anderson and Pontusson2007; Häusermann et al., Reference Häusermann, Kurer and Schwander2015; Han and Kwon, Reference Han and Kwon2020). But as we have explained above, the weak unemployment protection system in China by no means offers a reliable social safety net for laid-off individuals (Jiang et al., Reference Jiang, Qian and Wen2018; He et al., Reference He, Zhang and Qian2022). Therefore, we speculate that when short-term protection is shallow or unreliable, rational individuals may project their needs for social protection towards middle- or longer-term benefits.

Old-age support is one such example. Government and families have been two key sources of old-age support in the Chinese society. The former instituted a contributory old-age pension system that is basic in benefits but employment-dependent, while the latter operates on the basis of filial piety. However, unemployment due to automation replacement would not only lead to a significant reduction (and even loss) of pension benefits, but also result in a big loss of household income, which in turn weakens families’ ability in providing old-age support. Thus, automation displacement may shake both foundations, aggravating individuals’ anxiety about old-age support and increasing their expectation for government responsibility in this regard. Such anxiety has been exacerbated by concerns regarding the financial sustainability of China’s pension system, and the weakening of family support due to changes in family structure and social norms (Feng et al., Reference Feng, Liu, Guan and Mor2012; Yang and Chen, Reference Yang and Chen2019). In consequence, as far as Chinese urban workers are concerned, the adverse impacts triggered by unemployment are prolonged and highly consequential, making old-age support an even bigger individual risk (He, Reference He2022). Given the contextual dynamics explained above, it is reasonable to hypothesise:

If H₂ holds true, we expect its impact to be contingent upon macro welfare system context. A growing stream of the social policy literature highlights the importance of policy feedback effects through which pre-existing social policies and welfare programmes shape citizens’ policy preferences and behaviors (Larsen, Reference Larsen2008; Jordan, Reference Jordan2010). He et al. (Reference He, Ratigan and Qian2021) recently discovered that the subnational variation in China’s social health insurance programmes in both coverage and generosity had indeed produced varying attitudinal feedback patterns at the local level. What individuals want from the government ultimately depends on how existing social policies shape their experience of individual risk (Gingrich and Ansell, Reference Gingrich and Ansell2012). Hence, this study seeks to go beyond the conventional argument that automation risk affects social policy preferences through individual’s likelihood of workplace displacement, by bringing macro institutional factors into the equation. Given the highly fragmented social welfare system in China, it is reasonable to expect that individuals living in different local welfare systems would demonstrate salient differences in how they perceive government responsibility in mitigating automation risk. Inspired by the policy feedback literature, we propose two competing hypotheses with regard to the moderating effect of welfare systems: buffering effect and reinforcing effect.

Generous welfare arrangements can provide stronger social safety nets against labour market shocks, and therefore, individuals in these welfare systems are expected to be less sensitive to labour market risks because welfare programmes act as a robust buffer against potential job and income loss. Thewissen and Rueda’s pioneering work revealed salient attitudinal feedback effects in that a more generous welfare state generates a buffering effect on the negative impact of technological change. A more developed welfare state reduces the demand for a further expansion of social policy (Thewissen and Rueda, Reference Thewissen and Rueda2019; Busemeyer and Sahm, Reference Busemeyer and Sahm2021). Following this theoretical logic, we speculate that when automation risk hits, generous local welfare systems in China should be able to better cushion the labour market shock and individuals should show less demand for old-age support. Thus, we put forth the following hypothesis:

Contrary to the notion of the buffering effect, another stream of the policy feedback literature posits that social policies and public opinion usually form a reciprocal relationship. It is argued that generous welfare systems can create material incentives and normative expectations that further reinforce social policy support because they construct new constituencies of beneficiaries that are organised to defend the material benefits provided to them by existing policies (Pierson, Reference Pierson1994; Campbell, Reference Campbell2012). Many studies in both China and Western welfare states have found that generous welfare systems are indeed usually associated with larger populations in support of strong government responsibility in welfare provision (Im and Meng, Reference Im and Meng2016; Huang, Reference Huang2019; Dalen, Reference Dalen2021). Such a reinforcing effect may be at play in China as people in generous local welfare systems are likely to appreciate the social protection provided to cushion the adverse impact of workplace automation and thus expect additional welfare provision. We hence put forth the following hypothesis:

Data and variables

Regression results

Because our dependent variables were binary, we employed logit models to perform regression analysis. Considering that respondents nested within the same province may have shared some common characteristics, we clustered standard errors at the provincial level. Following the statistical strategies of Thewissen and Rueda (Reference Thewissen and Rueda2019), we regressed our dependent variables in a stepwise fashion. Model 1 included only RTI, our key independent variable, while a range of individual- and provincial-level controls were included in Model 2. Model 3 and Model 4 further added two moderating variables that captured the generosity of local welfare provision. This stepwise statistical strategy allowed us to examine the effect of key explanatory variables individually and in combination and to assess the explanatory robustness of these variables. Because UIS and OPP were moderately correlated, we added them separately in regression models in order to avoid potential complications resulting from multicollinearity.

Table 1 presents the estimation of the relationship between RTI and individuals’ redistributive preference. As expected, RTI score did not explain redistributive preference in a statistically significant manner across all models. H₁ was therefore endorsed. The generosity of neither old-age pension nor unemployment benefit predicted the dependent variable. Unsurprisingly, holding egalitarian views strongly predicted redistributive preference. Three control variables were related to the dependent variable; being older, being unmarried, and holding urban hukou led a person to support stronger government responsibility in redistribution.

Table 1

Logistic regression results: support for government redistribution

Note. Robustness standard errors clustered at the provincial level are reported in parentheses; ***p<0.01, **p<0.05, *p<0.1.

In Table 2, we analysed the relationship between RTI and individuals’ expectation for government responsibility in old-age support. RTI score significantly predicted the dependent variable, indicating that those in routine occupations held stronger preferences for social protection in the form of old-age support. Thus, H₂ was supported. Moving to the effect of provincial structural factors, we noted a strong negative statistical association between generosity of unemployment benefit and one’s expectation of government responsibility in old-age support. Generosity of old-age pension registered a similar effect on the dependent variable. It should be noted that urban hukou status was positively associated with both dependent variables, similar to the findings of previous studies (Huang, Reference Huang2019; He et al., Reference He, Ratigan and Qian2021). This association is likely to rest on the policy feedback effect. As urban hukou holders are generally entitled to more generous welfare benefits, such experiences reinforce their beliefs that the government should continue to be responsible for welfare provision (He et al., Reference He, Zhang and Qian2022).

Table 2

Logistic regression results: support for government responsibility in old-age support

Note. Robustness standard errors clustered at the provincial level are reported in parentheses; ***p<0.01, **p<0.05, *p<0.1.

Table 3 estimates cross-level interactions. Model 1 and Model 2 included the interaction term between RTI and the level of unemployment benefits. Model 3 and 4 added the interaction term between RTI and the level of old-age pension in a province. The interaction terms turned out to be significantly positive across all models, suggesting that the generosity of provincial welfare provision robustly reinforced the stimulating effect of RTI on expectation of government involvement in old-age support. In other words, when facing automation risks, individuals in provinces with generous welfare provision were more likely to expect the government to shoulder the main responsibility in supporting the elderly.

Table 3

Logistic models: interactions on old-age support

Note. Robustness standard errors clustered at the provincial level are reported in parentheses; *** p<0.01, ** p<0.05, * p<0.1.

Figure 1 further illustrates the estimated effects of the interaction terms. We observed that the average marginal effect of RTI on the dependent variable increased as the level of provincial welfare generosity increased. Specifically, when the value of unemployment benefit in log form was above 6, the marginal effect of RTI became statistically significant between 2 per cent and 6 per cent. The statistical significance was lost when the log-form value fell below 6. Similarly, the marginal effect of RTI was statistically significant between 3 per cent and 5 per cent when the value of old-age pension (in log form) was above 8. When the value fell below 8, the marginal effect of RTI became negative and insignificant. These results combine to confirm that the generosity of provincial welfare systems positively moderated the stimulating effect of RTI on the preference for government involvement in old-age support. For example, office clerks tend to exhibit higher expectation of governments’ responsibility in old-age support than professionals. This attitudinal gap between clerks and professionals widens in provinces that offer generous welfare provision. In sum, the results presented in Table 3 and Figure 1 led us to endorse the reinforcing effect hypothesis (H_3b ) and reject the buffering effect hypothesis (H_3a ). The main empirical results were corroborated by several robustness checks that are exhibited in the appendix due to page limit.

Figure 1.

Marginal effects of RTI on old-age support.