As artificial intelligence (AI) technology sweeps across the globe, its impact on employment and wage structures becomes a topic of increasing concern and interestAn insightful estimation of AI exposure across various occupations in China hints at a nuanced picture regarding job growthFor instance, a teaching team led by Gao links AI exposure levels to projected employment growth rates over the next decade, suggesting that a 40-point increase in the AI exposure index related to replacement effects may correspond to a 4.4 percentage point decrease in cumulative employment growth, while a similar increase in augmentation effects could boost growth by 9.1 percentage pointsThis dual nature of AI—acting as both a replacement and an enhancer—fuels discussions about the future of job markets.

Applying these principles to China’s Standard Occupational Classification, researchers calculated the net employment impact of AI for various job roles

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Their findings reveal a varied influence on employment growth rates across different professions, leading to the conclusion that AI may reduce cumulative employment growth rates by 1.8 percentage points over the next decade compared to baseline predictions, averaging out to an annual reduction of about 0.18 percentage pointsGiven China's massive workforce, this reduction indicates that AI is unlikely to trigger widespread unemployment.

However, these estimates carry inherent uncertaintiesThe assessed changes in employment growth rates consider productivity effects across all industries, along with positive spillover effects—those sectors impacted by AI-driven enhancements are expected to expand and create new labor demandIf we only evaluate AI's impact on current jobholders, even enhancement-oriented AI technologies could still lead to job losses, particularly among higher-income workers who may find their skills becoming obsolete due to technological advancements

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This situation unravels the complexity of interpreting overall unemployment rate changes, as stable aggregate figures may mask underlying structural shifts within the labor market.

Furthermore, the employed microeconomic parameters are based on U.Sdata due to the unavailability of Chinese microdata, limiting the precision of estimates specific to ChinaDespite these limitations, the preliminary analysis provides a basis for understanding AI's potential impact on employment in China.

Another pressing issue arising from the advent of AI is its potential to widen wage disparitiesVarious studies have employed different methodologies to assess how AI and automation impact wagesA central consensus among researchers is that the transformative potential of these technologies tends to exacerbate income inequality

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For instance, empirical research indicates that regions adopting higher levels of information technology and automation observe wage polarization, where middle-income job opportunities diminish while high- and low-income roles proliferate.

The International Labour Organization (ILO) cautions that while technological advancements may create new job opportunities, they can also deepen existing inequalities, particularly affecting low-wage workers, women, and individuals engaged in informal employmentSome scholars assert that accelerated automation could depress the wages of low-skilled labor, while high-skilled workers—those less affected by AI advancements—are likely to experience wage growth, thereby increasing income gaps.

The impact of AI on wage disparities appears to be more intricate compared to existing technologies

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Several researchers differentiate between the labor-replacing effects of AI and labor-enhancing effects, noting their distinct influences on wages and employmentThe premise of labor-saving technologies implies an improvement in the quality of capital capable of replacing workers for routine tasks—a scenario that may further drive labor displacement.

In contrast, labor-enhancing technologies improve worker productivity and cater to technologically adept individuals while potentially harming older workers with higher education levels and wages who struggle to adaptGenerally, replacement effects are associated with a decrease in labor income shares, whereas enhancement effects might lead to a slight increaseSome argue that AI, by boosting productivity and creating new tasks, has the potential to positively influence employment and income levels

However, the uneven distribution of benefits across different demographics could exacerbate disparities between income groups.

To provide a clearer picture, researchers drew from online job posting data in China and combined it with the estimated AI exposure across various professions to ascertain AI's impact on wage inequalityConsistent with mainstream predictions, jobs with high AI replacement effects witnessed slower wage growth from 2018 to 2023. Interestingly, while the internal Gini coefficient for professions with minimal augmentation effects slightly increased within this period, the rise was marginalTheoretically, within AI-augmented professions exist two opposing wage forces: skill-biased technological advancements that may lead to wage polarization and skill loss effects that could reduce the wage gap between older and newer employees.

Nevertheless, a cautious approach is advisable when interpreting AI's influence on income disparities

Due to the recent widespread use of large language models from 2023 onward, the long-term wage impacts of AI remain speculative and demand continuous data tracking for accurate assessment.

Additionally, concerns surrounding labor income shares potentially declining are prevalent within the literatureMany studies analyzing labor income share trends indicate a consistent decline over recent decadesAmerican manufacturing sectors exhibit declines in labor income shares, attributed, in part, to capital deepening, which has been highlighted as a primary contributor to the decreasing trend.

Recent studies have adopted an industry-focused approach to evaluate shifts in labor income shares, identifying a significant drop in labor income share globally, with about half of the decrease explainable through declining relative prices of investment goods

Evidence from various research initiatives indicates that substantial declines primarily occur within sectors, notably in manufacturing and trade.

Some theorists argue that advancements in technology, particularly skill-biased technical change (SBTC), can account for approximately 20% of the decline in labor income share observed within the U.Smanufacturing sector from 1970 to 2010. While previous analyses have highlighted labor income share declines in certain industries, extending the view over the last 200 years shows cyclical patterns without clear linear trends upward or downward.

In predicting the shifts in labor income shares during the AI era, opinions varySome scholars believe that even with the complete replacement of human labor by AI, the absolute income levels for workers might remain stable

Others anticipate that as AI technology progresses, labor income shares will stabilize rather than decline to zeroEsteemed economists have posited concepts such as the self-stabilizing effects of automation and balanced growth paths, emphasizing that as automation outpaces job creation, there will be a cost suppression of labor utilization, which will create suitable new tasks.

Combining estimates of labor income share based on AI exposure and parameters derived from literature, projections indicate that labor income shares in China could decline by approximately 0.73% over the next five yearsThis decrease is particularly noteworthy in sectors like office support and agriculture, with a minimal rise in industries tied to education and healthcare.

However, rising income shares present challenges requiring attention and strategies for potential adaptation, especially as the gig economy evolves

The trend in flexible employment is continuously growing, foreshadowing the demand for robust social security systems to protect labor rightsThe various flavors of "non-standard work," from temporary positions to gig roles arising from platform economics, have proliferated, signifying a need for comprehensive protection in a shifting job landscapeThe digital economy has increasingly led to an abundance of flexible jobs, with China alone boasting about 200 million flexible workers as of 2021.

Yet this increased flexibility can come at a costThe absence of formal labor contracts often leaves gig workers vulnerable, lacking both job security and benefitsHelsinki's call to provide sufficient social security for gig workers reflects a growing recognition of the need to address this gap as more individuals enter the labor market through non-traditional means

Whereas platforms may shy away from bearing social insurance responsibilities, the growing number of freelancers and gig workers presents ongoing challenges that policymakers must grapple with to ensure adequacy in future support.

Addressing these challenges requires a multi-faceted approach toward refining social security frameworksBenefits should prioritize the essentials of enforced saving and fair income distributionThe modern gig economy complicates traditional employer-employee relationships, complicating the implementation of mandated savings frameworksAs a result, there’s a pressing need to reinforce legal relationships that clarify obligations regarding contributions to social insuranceWithout a clear structure delineating social responsibility from both businesses and workers, the sustainability of social security systems becomes increasingly precarious.

Moreover, the need for social safety nets becomes more pronounced, especially for vulnerable demographics within this transforming landscape