This study examines the impact of China’s National New Generation Artificial Intelligence Innovation Development Pilot Zones on firm-level R&D subsidy rent-seeking. Using a staggered difference-in-differences design and a sample of Chinese A-share listed firms from 2009 to 2024, we find that the policy significantly reduces rent-seeking associated with R&D subsidies. Mechanism analysis identifies three channels through which the policy operates: improved access to external finance, stronger innovation incentives, and a reorientation of firms' innovation strategies. Dynamic analysis further shows that the dominant mechanism evolves over time, shifting from financing improvements to enhanced innovation willingness and subsequently to innovation preference realignment. Heterogeneity analysis, guided by the motivation–opportunity–ability framework, indicates that the policy effects are stronger for state-owned enterprises, firms with higher audit quality, and firms located in regions with lower fiscal pressure and greater fiscal transparency. Overall, the findings extend the literature on rent-seeking by identifying an incentive-based governance mechanism that complements traditional supervision-based approaches.
AI-based pricing is becoming an important feature of digital trade, yet its macro-level relevance for cross-border e-commerce remains underexplored. This paper examines the relationship between AI-pricing exposure and South Korea’s cross-border e-commerce competitiveness over 2009–2024. Because the analysis relies on a short annual time series, a parsimonious bootstrap ARDL framework is used. The specification restricts lag length, applies BIC-based model selection, reports bias-corrected bootstrap confidence intervals, and uses a deleted-year jackknife check to assess finite-sample robustness. The results show a stable long-run relationship among AI-driven pricing exposure, digital infrastructure, government digital investment, technological human capital, GDP growth, inflation, and cross-border e-commerce competitiveness. AI-driven dynamic pricing is positively associated with competitiveness in both the short run and the long run, with a larger long-run coefficient. This pattern is consistent with cumulative platform learning, demand discovery, and improved pricing coordination in overseas markets. Digital infrastructure, policy investment, technological human capital, and GDP growth are also positively associated with competitiveness, while inflation shows a negative association. The error-correction term indicates adjustment toward long-run equilibrium, and the deleted-year jackknife results confirm that the main estimates are not driven by a single observation, including the COVID-19 year. The findings suggest that AI pricing capability is most valuable when embedded in a broader digital-economy ecosystem. Policy efforts should therefore combine algorithmic transparency, advanced infrastructure, targeted digital investment, specialized human capital, inter-organizational cooperation, and macroeconomic stability.
This paper uses administrative data matched with micro-level tracking records of rural households in a city in central China to examine the effects of promotive and protective assistance policies—and their combinations—on household income and relative deprivation. The findings show that both types of policies significantly increase household income. Although joint implementation also raises income, no significant positive synergistic effect is observed. Supplementary event-window evidence is consistent with a gradual improvement in household income after first entry into dual-support status. Heterogeneity analysis reveals that policy effects vary across households with different labor endowments, health risks, and human capital levels. Further mechanism analysis suggests that assistance policies promote income growth by improving households’ off-farm employment structure. Finally, both types of policies reduce household relative deprivation and improve relative status, but again, no synergistic effect is found for their joint implementation. These results suggest that further policy adjustments are needed—not simply by increasing the number of policies households receive, but by strengthening interactions among policies to enhance collaborative governance.
This study examines how occupational exposure to industrial robots and artificial intelligence (AI) is associated with workers’ wages and numeracy skills in the Korean labor market, using two waves of the Programme for the International Assessment of Adult Competencies. Despite growing research on the wage and employment impacts of automation and AI, little attention has been paid to how these technologies are associated with workers’ skill formation, especially in rapidly aging economies. We show that high-skilled and more-educated workers are more likely to work in AI-exposed occupations, whereas less-skilled and less-educated workers are concentrated in robot-exposed occupations. AI exposure is positively associated with wage levels, an association stronger among older and high-skilled workers. AI exposure is also positively associated with wage gains, with evidence that young workers in AI-exposed occupations experience smaller gains than their middle-aged counterparts. By contrast, robot exposure is negatively associated with wage levels and shows a weaker and less precisely estimated negative associations with wage growth. Over the 10-year period, greater AI exposure is associated with improvements in numeracy proficiency, with some evidence of greater gains among younger workers. By contrast, greater robot exposure is associated with declines in numeracy skills, with some evidence of larger declines among younger workers. These findings suggest a complementary relationship between AI and human capital, while robot exposure is associated with adverse outcomes in routine-task-intensive occupations. By highlighting skill formation as a key correlate of technological change and labor market outcomes, this study provides new evidence on the interaction between AI, human capital, and inequality in an aging economy.
The European Union’s Carbon Border Adjustment Mechanism (CBAM) imposes a carbon tariff on imports. To evaluate the effectiveness of such tariffs in reducing carbon emission in exporting countries, we develop a general equilibrium model with an endogenous domestic emissions quota. We show that treating exporting countries’ climate policies as strictly exogenous is misleading: without government intervention, a carbon tariff merely reallocates emissions allowances from exporting to non-exporting firms, leaving aggregate domestic emissions unchanged. However, we prove that the exporting country’s government can improve social welfare by strategically tightening its overall carbon quota. By doing so, the government reduces the tariff rent that would otherwise be extracted by the importing countries. Consequently, this endogenous strategic response is central to the effectiveness of CBAM.
Understanding intra-household resource allocation is critical for welfare analysis, yet consumption inequality within subgroups – particularly among co-wives – remains underexplored due to methodological and data constraints. This study introduces a novel empirical approach, to estimate individual resource shares for co-wives in polygynous households under flexible assumptions, using data from Burkina Faso. Our findings are threefold. First, consistent with prior literature, husbands receive a disproportionately large share of household resources in both monogamous and polygynous households, with inequality being more pronounced in the latter. Second, senior wives receive significantly larger resource shares than junior wives; while biological children modestly improve a junior wife's relative position, seniority rank is the dominant and structurally embedded driver of resource allocation. Third, poverty analysis reveals that women – especially junior wives – and children are the most materially deprived. These results demonstrate that targeted poverty policies must account for intra-household hierarchies, particularly within polygynous families.
Based on 3,642 firm-year observations from 476 Chinese A-share listed firms in high-pollution industries during 2015-2024, this study distinguishes the ex ante signaling effect of green bond issuance from its ex post policy implementation effect. Environmental performance (EP) is measured using the Wind ESG Environmental Pillar Score, while a disclosure-excluded substantive environmental performance (SEP) index, constructed from environmental penalties and verified environmental governance investment, is used to test whether the estimated effect reflects substantive improvement. Two-way fixed-effects results show that green bond issuance and issuance intensity are associated with significant post-issuance improvements in EP, providing evidence of the ex post policy implementation effect. The SEP results further indicate that the ex post policy implementation effect is not limited to disclosure-related improvements in the EP score. Dynamic estimates indicate that EP improves in the issuance year, whereas SEP becomes significant in the first post-issuance year and strengthens in the second. Financing-constraint relief, environmental information disclosure quality, and external regulatory attention jointly transmit the ex post policy implementation effect. The credibility of the ex ante signal is stronger when it is subsequently supported by third-party certification, stricter environmental regulation, a more developed regional green finance system, traceable use of proceeds, and SEP improvement. However, 47 issuer-year observations, accounting for 14.78% of the green bond sample, meet at least one potential greenwashing criterion, and these potentially symbolic issuances are not associated with a significant announcement return. Overall, green bond issuance acts as a credible policy instrument when initial signals are supported by verifiable implementation, but may remain symbolic when expanded disclosure is not accompanied by investment, compliance, or substantive environmental outcomes.
By incorporating a learning-by-doing mechanism into a standard CES production framework, this paper examines how capital imports affect the skill premium within Chinese firms. We identify two channels: capital–skill complementarity and improvements in the relative productivity of skilled labour. Capital imports increase the skill premium through both channels, with the scale effect of capital–skill complementarity being stronger. However, their relative importance varies across firms and industries and depends on workforce skill composition. Capital–skill complementarity is largely insensitive to workers’ skill levels, whereas the productivity channel is skill-specific. When productivity gains shift from high-skilled to medium-skilled workers, capital imports continue to raise the skill premium through capital–skill complementarity, but the productivity channel no longer contributes to skill-premium growth. The findings highlight the importance of vocational training, worker reskilling, innovation, and R&D investment in managing the distributional effects of global capital integration.
In the transition to a knowledge-driven economy, intellectual capital (IC) underpins firms’ long-term value creation. This paper examines how tax incentives affect intellectual capital efficiency (ICE). The results show that tax incentives significantly enhance ICE. This positive effect is moderated by internal control quality and market competitive position: it is more pronounced in firms with high-quality internal controls and stronger competitive positions. Heterogeneity analysis further reveals larger effects among non-state-owned enterprises and high-tech firms. Mechanism tests indicate that tax incentives improve ICE primarily by alleviating corporate financing constraints, and they boost the value-added efficiency of both human capital and structural capital simultaneously.This study extends the literature on ICE determinants from a macroeconomic policy perspective, and provides empirical evidence and policy implications for emerging economies seeking to optimize tax incentive systems and advance knowledge-based economic upgrading.
Removing bottlenecks in digital infrastructure is essential for ensuring the smooth circulation of data as production factor. Its function in reshaping urban growth patterns therefore merits close attention, especially if digital infrastructure is to advance alongside social equity and green development. Using the panel data for 214 prefecture-level cities from 2011 to 2023, this research uses difference-in-differences model and treats the pilot policy of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment in digital infrastructure. We check its effect on urban inclusive green growth and explore the potential mechanisms and heterogeneous effects. It was concluded as follows. First, the pilot policy obviously promotes urban inclusive green growth(IGG), and this result keeps robust across a range of robustness checks. Second, such policy enhances urban IGG by fostering fintech development and green innovation. Third, the heterogeneity analysis shows that this effect is especially strong in eastern cities, super-large cities, and non-resource-based cities. These findings suggest that, at critical stage of China’s economic transformation, policymakers should continue to amplify the positive effect of the pilot policy by promoting fintech development and green innovation. Meanwhile, governments should adopt place-based policy measures according to differences in location, city size, and resource endowments, so as to improve the spatial layout and policy effectiveness of that Pilot Zone.
