Agricultural technological innovations exhibit the dual attributes of both driving and tearing. Empirical studies demonstrate that precision agriculture tools (such as soil sensor-based systems and smart irrigation) have boosted rice yields to unprecedented levels in digitally advanced regions like the Yangtze and Pearl River Deltas (Huang and Wang, 2024). Studies show digitization-driven farming systems fundamentally transform production functions, linking operational efficiency directly to export competitiveness and supply chain agility to reshaped trade dynamics (Chen and Zhang, 2023; Liu et al., 2021). However, technology adoption exhibits a Matthew effect: urban hubs with superior infrastructure capture disproportionate returns, whereas rural areas face adoption barriers due to limited capital and technical capacity. Peripheral villages confront prohibitive maintenance costs and fragmented knowledge transfer, creating self-reinforcing exclusion. This polarization reveals a critical mechanism in innovation diffusion: technological adoption trajectories are shaped by regional economic conditions, as innovation resources concentrate in high-return zones—undermining the role of natural resource endowments in equitable technology implementation.

Agricultural innovation exerts a dual influence on rural development from an environmental perspective. Zhang et al. (2023) indicated that technological advancements could promote green agricultural practices by reducing chemical inputs and optimizing resource allocation. However, the ecological outcomes remain complex. Burgess and Morris (2009) and Gras and Cáceres (2020) highlighted a persistent paradox, noting that machinery intended to enhance productivity may instead increase emissions by intensifying land use and fossil fuel dependency, particularly in agricultural regions. This contradiction is especially pronounced in agriculture-dependent economies, where environmental pressures resulting from excessive mechanization can negatively affect rural livelihoods. Moreover, ineffective management of these ecological pressures can lead to a feedback loop that further exacerbates spatial inequality.

Government subsidies and rural financial systems also play a critical role in facilitating agricultural innovation (Liu et al., 2021; Wu et al., 2022). These resources effectively stimulate research and development activities, contributing to ecologically sustainable development and enhanced agricultural productivity. However, the effectiveness of such policies largely depends on their specific implementation mechanisms. Empirical evidence indicates that large-scale agricultural operators near urban areas typically enjoy superior access to financial support and advanced technological resources through institutional channels, creating spatial disparities in technology adoption. Although smallholders dominate agricultural production in many developing countries, they frequently encounter systemic barriers to accessing modern agricultural technologies. Consequently, technology diffusion patterns influenced by existing spatial hierarchies may intensify, rather than mitigate, the urban–rural development gap.

Small-scale farmers encounter several economic barriers to participating in agricultural technological innovation. Gras and Cáceres (2020) indicate that the substantial initial investments required for modern agricultural equipment typically limit its accessibility to commercially oriented producers connected to urban markets, inadvertently exacerbating existing wealth disparities. This accessibility paradox generates self-reinforcing cycles, intensifying the divide between those who possess modern technology and those who do not, thereby reinforcing established economic inequalities. Furthermore, the economic viability of rural areas is closely tied to local ecological conditions, making harmony with the natural environment essential. Thus, it is imperative to balance productivity improvements with ecological preservation, ensuring technological advancements do not compromise environmental sustainability.

There is a wide gap between the incomes of urban and rural residents in China. Exploring the root causes of this gap requires an analysis of the interplay of multiple and complex factors, such as the process of urbanization, differences in production efficiency, the formulation of relevant policies and the allocation of human capital. Clarifying these intertwined influences is a prerequisite for the development of effective interventions to alleviate spatial inequalities between economies in different regions.

Interestingly, the impact of urbanization is closely related to the methods employed to measure income disparities. Yuan et al. (2020) indicate that varying measurement approaches yield significantly different conclusions. Specifically, indicators based on income or consumption suggest a widening urban–rural gap, whereas inequality indices, such as the Theil index and the Gini coefficient, imply that the gap is narrowing, revealing a notable contradiction. Additionally, regional heterogeneity should not be overlooked. Su et al. (2015), employing Granger causality tests, demonstrated that accelerated urbanization in eastern China has intensified income inequality, emphasizing that institutional factors play a crucial moderating role in this process.

Empirical studies have examined the differential impact of productivity gains on rural–urban disparities. Yao and Jiang (2021) indicate that rural productivity enhancements—particularly through agricultural modernization and industrial diversification—significantly boost rural incomes and reduce urban–rural inequality. Conversely, urban productivity growth tends to exacerbate regional disparities, as the agglomeration effect during technology dividend absorption amplifies cities’ inherent advantages. Zhao (2024) highlights that the urban–rural dichotomy is closely tied to economic restructuring pathways. For instance, Zhang and Yang (2019) demonstrate how the transition from traditional agriculture to urban-specialized industries drives economic resource concentration in core cities, leading to increasingly asymmetric regional development trajectories.

Government policies significantly influence the extent of the rural–urban income gap. Yao and Jiang (2021) highlight that interventions, such as reforms of the hukou system, effectively narrow the urban–rural income gap, albeit with a time lag. Hu (2023) underscores the importance of agricultural support policies in enhancing rural economic conditions, demonstrating that mechanisms for price stabilization and targeted investments significantly benefit rural areas, contributing to the reduction of regional disparities.

Regional economic prosperity, industrial restructuring, and government macroeconomic policies significantly influence the income disparity between urban and rural areas (Chen and Shen, 2021). Industrial restructuring and macroeconomic interventions, as directed by governmental policies, form critical determinants of urban–rural income inequality. According to Li et al. (2021), although natural factors such as climatic conditions, topography, and landscape play moderating roles, socio-economic determinants remain dominant in shaping regional income disparities. Additionally, disparities in information accessibility and uneven regional development further exacerbate the persistence of the urban–rural income gap (Hou and Yuan, 2023; Piketty et al., 2019).

Educational attainment is another critical determinant of the urban–rural income gap. Sicular et al. (2007) emphasize that higher levels of education significantly reduce this disparity. Specifically, their analysis demonstrates that increased educational attainment facilitates the narrowing of income inequality by equipping rural populations with enhanced skills, thus influencing income through a dual mechanism. This mechanism includes both facilitating labor mobility toward urban labor markets and enhancing productivity within rural industries. Consequently, education fosters greater participation by rural residents in urban employment opportunities and simultaneously boosts productivity in rural sectors.

Wordofa et al. (2021) demonstrate that agricultural technology adoption significantly influences household income. Their study reveals that the high initial investment associated with modern agricultural equipment often limits adoption to commercial producers with urban market linkages, inadvertently widening wealth disparities. This finding highlights the potential of agricultural technology to improve household welfare and underscores the necessity of strengthening technology generation, diffusion, and adoption. Agricultural innovations directly contribute to increased farmer incomes primarily by reducing production costs and enhancing crop yields. Technologies such as biotechnology and precision agriculture exemplify this by simultaneously lowering production costs and increasing outputs (Tang et al., 2022).

Agricultural technological innovations exhibit a dual role in economic development: exacerbating income inequality while driving aggregate income growth. Tan et al. (2022) demonstrate that technological adoption intensifies income disparities in 73 countries, though public expenditure interventions mitigate this effect. Key factors such as the level of public spending, the employment rate in agriculture and export diversification moderate the relationship between technological innovation and inequality. Technological progress also generates indirect effects through multiple channels, such as optimizing employment structures, enhancing resource allocation efficiency, and boosting productivity (Munshi and Rosenzweig, 2013).

The relationship between agricultural technological innovation and income distribution reveals nuanced dynamics across socioeconomic contexts. Zhao and Zhou (2021) investigation identifies three primary mechanisms—labor productivity enhancement, product quality optimization, and workforce migration—through which farm technologies influence rural incomes. Their findings nevertheless indicate paradoxical outcomes in China, where technological adoption occasionally suppresses income growth, revealing how local implementation frameworks can reverse anticipated benefits. This context-dependent effectiveness underscores how regional development disparities, innovation capacities, and digital infrastructure quality mediate technological impacts (Wu et al., 2022).

Distributional consequences prove equally complex. While Giller et al. (2021) document disproportionate benefits accruing to commercial farms and urban consumers, Lowder et al. (2021) bring up policy-technological synergies to redirect advantages toward smallholders. Barrett et al. (2022) macroeconomic modeling confirms that technology selection inherently shapes inequality trajectories, with Ruzzante et al. (2021) advocating labor-intensive innovations to harmonize efficiency and equity. Input–output analyses further reveal how farm-level consumption patterns amplify or dampen broader economic effects.

In summary, while the existing literature on this field is extensive, it still exhibits certain limitations. First, existing scholarship predominantly examines macroeconomic or endogenous factors in urban–rural inequality, largely overlooking innovation’s mediating role. Second, while agricultural innovation’s economic impacts receive attention, its potential for fostering urban–rural integration remains underexplored. Third, rigorous quantitative investigations into prefecture-level mechanisms linking agricultural technology and income disparities remain scarce, particularly analyses disentangling direct and spillover effects.

This study offers three significant advancements to the field. First, it combines agricultural tech innovation and urban–rural income inequality within a cohesive analytical framework, using panel data from 280 Chinese cities (2008–2021) to empirically demonstrate that agricultural technological innovation significantly narrows the urban–rural income gap. This provides new evidence for understanding its role in integrated urban–rural development.

Second, the paper identifies three key mechanisms through which agricultural technological innovation reduces the income gap: employment structure optimization, factor allocation improvement, and production efficiency enhancement. These findings enrich theoretical research and offer practical insights for policy-making, such as facilitating rural labor transfer and improving resource allocation efficiency.

Third, the paper reveals threshold effects in the relationship between agricultural technological innovation and the income gap. The moderating effect strengthens with higher urbanization levels, government support, and information accessibility, providing precise guidance for policy formulation.

Overall, this study addresses critical gaps in the literature and offers valuable insights for promoting urban–rural integration through agricultural technological innovation.

Currently, scholars primarily employ several factors to measure the urban–rural income gap, including the Theil Index, Gini Coefficient, and Urban–Rural Income Ratio. Each of these indicators possesses distinct advantages and disadvantages when analyzing income disparity issues.

While the Gini coefficient is widely used to measure the degree of inequality, its diagnostic limitations become apparent in urban–rural analyses. The metric’s aggregated nature masks geographical blind spots and demonstrates limited capacity to detect income extremes—particularly problematic when assessing impoverished populations. By compressing spatial disparities into singular values and smoothing distribution tails, it inadvertently obscures nuanced fluctuations in vulnerable groups’ earning trajectories.

As for the Urban–Rural Income Ratio, it is easy to calculate and based on simple principles, which makes it frequently utilized in policy discussions and media reports. However, the Urban–Rural Income Ratio merely reflects the average level of income distribution and is prone to overlook the internal income inequality present within urban and rural areas.

In contrast, the Theil Index is adept at capturing differences in extreme values (Oancea and Pirjol, 2019) and exhibits greater sensitivity in measuring inequality (Qu et al., 2023). It is particularly suitable for analyzing income inequality in rapidly changing economies (Jorgenson and Timmer, 2011). In order to ensure the scientificity and rationality of variable selection, this paper did the test based on Bayesian Information Criterion for the three indicators, Theil index, RUIR, and Gini index, and found that bic_Theil < bic_RUIR < bic_Gini, which indicates that the Theil index is more capable of identifying causality in the regression analysis of this paper. Therefore, this paper uses Theil as the dependent variable in the baseline regression and includes RUIR as an alternative indicator of the dependent variable in the robustness test.

In this study, the natural logarithm of the quantity of agricultural patents is employed as a proxy variable for agricultural technology innovation (ATC_i,t), notwithstanding the recognized limitations of this indicator. While patent counts primarily capture formal, patentable innovations and fail to account for informal innovations and non-patentable incremental improvements, they remain the most viable metric for measuring technological innovation in the extant literature, given considerations of data availability and indicator comparability. The ATC index is calculated as the natural logarithm of the total number of granted invention patents classified under the International Patent Classification (IPC) A01 category. This metric aligns with prior studies measuring innovation through patent output (Griliches, 1990; Hall et al., 2005). It should be noted that the patent count used in this study specifically refers to valid patents that have been granted and are maintaining normal annual fee payments, indicating that these patents remain within their validity period and are legally protected. This approach ensures that our innovation measure captures economically meaningful technological advances rather than dormant or abandoned patents.

The utilization of patent data as an innovation metric is justified by three principal considerations. First, patent statistics provide an objective and quantifiable measure of innovation output. The volume of patent applications serves as a direct indicator of the innovative activities undertaken by enterprises and research institutions (Griliches, 1990). Second, the legal protection mechanism inherent in the patent system enhances the reliability of this metric. Given the complexity and substantial costs associated with patent application procedures, economic agents typically pursue patent protection only for innovations with significant market potential and commercial value. This self-selection mechanism consequently improves the validity of patents as innovation indicators (Hall et al., 2005).

Furthermore, the systematic documentation and public accessibility of patent data facilitate robust cross-temporal and cross-regional comparative analyses, thereby providing a reliable empirical foundation for this research. In the context of China’s economic transition towards an innovation-driven growth model, patent statistics have gained particular significance as a crucial indicator of technological innovation capacity. The aforementioned characteristics collectively substantiate the appropriateness of employing patent data as a proxy for agricultural technology innovation in this analysis.

China’s urban–rural income disparities are shaped by multiple socioeconomic forces, necessitating rigorous control of confounding variables to isolate agricultural technology’s unique effects. Regional development imbalances, labor mobility patterns, and fiscal transfer mechanisms particularly require statistical normalization when assessing how farming innovations influence this economic divide.

Economic development level (Eco), quantified by the logarithmic value of the city’s per capita GDP. By controlling for the level of economic development, the direct effect of economic development on the urban–rural income gap can be ruled out to more accurately identify the independent effect of agricultural technology innovation on the urban–rural income gap.

Table 1 presents the outcomes of the correlation analysis among different variables, specifically the correlation between Theil and the explanatory variables. Theil shows a significant negative correlation with ATC, Eco, and Eco2, indicating that economic development and technological innovation significantly contribute to reducing the urban–rural income gap. Furthermore, Fin is negatively correlated with Theil, suggesting that the expansion of financial markets aids in diminishing the urban–rural disparity. In contrast, Gov and Prim are significantly positively correlated with Theil, indicating that higher government expenditures and an increased share of agriculture may exacerbate the urban–rural income gap. Moreover, Env exhibits a significant negative correlation with Theil, suggesting that improvements in ecological governance are associated with a reduction in income disparity. Overall, the correlations among the variables reveal multiple factors influencing the urban–rural income gap, thereby demonstrating a robust explanatory capacity.

To assess the stability of our main findings, we examine whether the estimated effect of agricultural technological innovation on the urban–rural income gap is sensitive to the inclusion of different control variables. Following the approach suggested in the literature on robustness checks, we first estimate a model with ATC as the sole predictor variable. Subsequently, we create a series of models where ATC is paired with each control variable individually. Finally, we present the comprehensive model including all control variables.

Table 4 presents the results of this robustness check. Column (1) shows the baseline effect of ATC without any control variables. Columns (2) through (7) pair ATC with individual control variables, while Column (8) includes all control variables simultaneously. The results demonstrate that the coefficient of ATC remains consistently negative and statistically significant across all specifications, with coefficients ranging from-0.0044 to-0.0015. This stability is particularly noteworthy given the substantial differences in model specifications and the theoretical importance of each control variable.

It is worth noting that the magnitude of the ATC coefficient decreases when economic development variables (Eco and Eco2) are included, suggesting that part of the relationship between agricultural technological innovation and the urban–rural income gap is related to overall economic development. However, the persistent significance of ATC across all specifications confirms that agricultural technological innovation has an independent effect on reducing income inequality beyond general economic factors.

The consistency of these results across different model specifications provides strong evidence for the robustness of our main finding that agricultural technological innovation plays a significant role in narrowing the urban–rural income gap in China.

This investigation employs the Theil index as the primary metric for measuring urban–rural income disparity in the benchmark regression analysis. To strengthen the robustness, an alternative dependent variable—the urban–rural income ratio (RUIR)—is also incorporated. RUIR is determined by dividing urban per capita disposable income by rural per capita net income. Recognizing that nominal income comparisons may be influenced by regional variations in living costs, we have enhanced our robustness analysis by incorporating the Consumer Price Index (CPI) as an additional control variable to account for cost-of-living differentials across regions. The regression outcomes, presented in Column (1) of Table 5, demonstrate that even after controlling for regional price variations, the coefficient for ATC remains significantly negative at-0.0269, confirming that our findings are robust to cost-of-living adjustments and align with the initial regression results. This specification ensures that our measured effects capture genuine improvements in relative purchasing power rather than nominal income changes, thereby providing a more accurate assessment of agricultural technological innovation’s impact on real income disparities.

In the main regression, patent output is employed as the metric for assessing agricultural technological innovation. In this context, research and development efficiency (Eff) is used as the key explanatory variable. Eff is calculated as the ratio of granted agricultural patents to patent applications. The regression outcomes in Table 5, Column (2), show that improvements in R&D efficiency have a positive impact on reducing the urban–rural income gap, with a marginally stronger effect compared to patent output.

Additionally, patents are categorized into invention patents, utility model patents, and design patents, each reflecting different levels of technological complexity. This section further examines the influence of invention patents (Inv) and utility model patents (Uti) on the urban–rural income disparity. The regression results for Inv and Uti, shown in Columns (3) and (4) of Table 5, reveal that the coefficients for Eff, Inv, and Uti are all negative and statistically significant.

Comparing the results with those of the baseline regression reveals that the coefficient for Eff is slightly larger than that for ATC, while the coefficient for Uti is notably smaller, with a corresponding reduction in statistical significance. This indicates that agricultural technological innovation efficiency has a greater impact on narrowing the urban–rural income gap, whereas the effect of patent application is primarily embodied in invention patents.

In the primary regression analysis, this study incorporates fixed effects for both city and year to account for unobserved factors influencing the urban–rural income disparity at these levels. Additionally, recognizing that geographic location, economic capacity, and industrial composition vary across provinces and may impact income disparities within cities, the analysis extends to include fixed effects for province and region. To address the influence of time-varying factors, particularly shifts in provincial and regional strategies over different years, the extended Equation 3 introduces cross-fixed effects for “province × year” and “area × year” (Goodman-Bacon, 2021). Where ρ pt denotes the “province × year” cross fixed effect capturing policy or economic fluctuations over time in each province, ω rt denotes the “region × year” cross fixed effect controlling for time-varying factors (e.g., adjustments in regional development strategies) at the regional level, and the remaining variables have the same meaning as in model (1). The findings in Columns (5)–(6) of Table 5 confirm that the study’s conclusions remain consistent and reliable. (3) Thei l it = β 0 + β 1 AT C it + λContro l it + δ i + η t + γ p + ρ pt + ω rt + ε it

The benchmark regression model in this paper may face endogeneity concerns arising from potential bidirectional relationships. While advancements in agricultural technology can reduce the urban–rural income disparity, the converse is also possible: a decrease in this disparity might draw skilled labor to rural areas, thereby fostering agricultural technological progress and creating endogeneity issues.

Considering that the impact of current agricultural technological innovation on the urban–rural income gap in Chinese cities may exhibit a time lag, future reductions in this gap are unlikely to influence present agricultural innovation. To address this potential issue, agricultural technological innovation lagged by one or two periods can be utilized as instrumental variables for the current values, effectively mitigating concerns of reverse causality.

In Table 6, columns (1) and (2), showcase the regression outcomes using a one-period lag, both with and without control variables factored in. The findings clearly indicate that L1. ATC positively influences the urban–rural income gap. Similarly, Columns (3) and (4) in Table 6 display the regression results with a two-period lag. The coefficient for L2. ATC remains negative and statistically significant at the 1% level.

Significant disparities exist across regions in terms of economic development, natural resource endowments, and agricultural structures. To investigate whether the effect of agricultural technological innovation on the urban–rural income gap differs by region, this study classifies the data according to the four major economic regions defined by the National Bureau of Statistics of China. The regression results for each region are presented in Table 10. Columns (1) to (4) correspond to the eastern, central, western, and northeastern regions, respectively. The regression outcomes reveal that the signs and statistical significance of the ATC coefficients vary across the different regional samples. Specifically, agricultural technological innovation in the eastern and western regions significantly reduces the urban–rural income gap. In contrast, in the central region, agricultural technological innovation is correlated with the local Theil index. Additionally, the coefficient for ATC in the northeastern region does not exhibit statistical significance.

The empirical results demonstrate stark regional asymmetries in agricultural innovation’s inequality effects. Coastal eastern provinces leverage robust economic ecosystems to accelerate agricultural tech absorption, where rural productivity gains directly counteract urban wage premiums (Jin et al., 2010). Western regions reveal policy-driven growth corridors-targeted subsidies and infrastructure investments enable leapfrog technological adoption (Hsu et al., 2023), though sustainability concerns persist as noted by Han et al. (2024).

Central China’s fragmented infrastructure and underdeveloped markets constrain tech penetration, creating winner-takes-all dynamics that exacerbate rural stratification (Han et al., 2023). This corroborates Huang et al.'s (2013) findings on uneven agricultural modernization in central provinces. Northeastern rustbelt areas present industrial monocultures and demographic hollowing that blunt innovation’s redistributive potential—here, aging farming populations and entrenched state-owned enterprises create structural inertia that even advanced technologies struggle to disrupt (Rozelle and Swinnen, 2004). These geographical fault lines underscore how institutional path dependencies mediate technological impacts on inequality.

The fiscal system and administrative hierarchy of China imply differing priorities among cities of various administrative levels in terms of fiscal transfer payments, land policies, and infrastructure development, which affect their capacity to promote technological innovation. To investigate whether urban administrative levels influence the effectiveness of agricultural technological innovation in narrowing the urban–rural income gap, this paper divides the entire sample into high and low administrative levels subsamples for grouped regression analysis. Columns (5) and (6) of Table 10 present the results of heterogeneity tests based on urban administrative levels.

Administrative levels influence local governments’ fiscal resources, policy implementation capacity, and support for innovation (Rodríguez-Pose, 2013). This hierarchical structure has been extensively documented by Xu (2011) who analyzed how China’s administrative ranking system affects resource allocation and economic outcomes. Cities directly governed by the central government, along with provincial capitals and sub-provincial urban centers, generally enjoy stronger financial backing and greater decision-making freedom. These findings align with Zhang (2006) who demonstrated that higher administrative rank correlates with increased fiscal transfers and policy autonomy in Chinese cities. This positions them to drive technological advancements and build out essential infrastructure more efficiently than other regions, which may contribute to narrowing the urban–rural income gap. In contrast, ordinary prefecture-level cities may suffer from inadequate resources, making it challenging to effectively drive agricultural technological innovation, and thus having a weaker impact on income disparity. This resource constraint pattern is consistent with Jin and Zou (2005) who found significant fiscal disparities between different administrative tiers in China’s intergovernmental system.

Innovation vitality is a significant indicator of urban development quality. According to the theory of innovation diffusion, the rate at which technological innovation effects spread varies across different regions (Chen et al., 2024) This theoretical foundation builds upon Rogers (2003) seminal work on diffusion of innovations, which established how innovation adoption rates differ across geographic and institutional contexts. Areas with strong innovation vitality can adopt new technologies more rapidly, thereby narrowing the productivity and income gaps between urban and rural areas. To examine whether the impact of agricultural technological innovation on the urban–rural income gap varies across cities with different levels of innovation vitality, this paper employs the Urban Innovation Index to characterize urban innovation vitality. Cities are classified into high and low groups based on their annual mean values. The results, presented in columns (1) and (2) of Table 11, indicate that the estimated coefficient of ATC is significantly negative, suggesting that improvements in agricultural technological innovation have a positive effect on narrowing the urban–rural income gap in cities with varying innovation vitality. However, this effect is stronger and more significant in cities with high innovation vitality.

The reasons are as follows: cities with strong innovation vitality typically possess more comprehensive research and development systems, technology transfer mechanisms, and market structures, enabling them to more rapidly apply agricultural technological innovations to actual production. These findings are consistent with Cooke et al. (1997) who demonstrated that regional innovation systems with stronger institutional capacity exhibit superior technology absorption and diffusion capabilities. This facilitates a more effective enhancement of agricultural productivity, thereby improving farmers’ incomes and narrowing the urban–rural income gap. Conversely, in cities with weaker innovation vitality, the diffusion and application of agricultural technologies progress relatively slowly, and the effect of technological innovation on improving income distribution patterns is relatively limited.

The strength of IP safeguards directly impacts the motivation for tech advancements (Furukawa, 2010). To examine the effect of urban intellectual property protection on the impact of agricultural technological innovation on the urban–rural income gap, this paper classifies the entire sample of cities into high and low protection level groups based on the average level of intellectual property protection. Subsequently, regression analyses are performed for each group. The results are presented in Columns (3) and (4) of Table 11.

The analysis reveals intellectual property (IP) regimes’ pivotal role in mediating agricultural innovation’s distributional outcomes. Regions with robust IP frameworks demonstrate technology’s equalizing potential: enforceable patent protections stimulate sustained R&D investment while ensuring scalable tech transfer to smallholders. Here, innovation commercialization correlates strongly with productivity surges (15–22% yield improvements) and rural income growth (Chu et al., 2014; Yang et al., 2014), effectively narrowing urban–rural wage differentials through dual channels—direct farm revenue boosts and secondary employment in agritech sectors.

Conversely, weak IP enforcement regions exhibit innovation stagnation. Underdeveloped legal infrastructures create “technology deserts” where limited adoption (<30% penetration rates) disproportionately benefits commercial farms, leaving subsistence farmers trapped in low-productivity cycles. This bifurcation aligns with endogenous growth theory’s predictions (Bielig, 2015): strong IP regimes function as economic equalizers by boosting innovation returns and diffusion breadth, whereas fragmented protections exacerbate rural stratification through uneven technological dividends.

Scholtz posits that economic effects cannot be generated without technology diffusion, and that the flow of information is a key factor driving both technology diffusion and the application of innovations (Scholtz, 2018). This perspective aligns with Aker (2011) who demonstrated how information and communication technologies significantly reduce transaction costs and improve market efficiency in agricultural settings. Whether the flow of information factors promotes a stronger economic effect of agricultural technology innovation, thereby influencing income disparity, is investigated in this paper. Using the average number of Internet broadband users per 100 individuals as a standard, this paper explores the impact of varying levels of information accessibility on the income-regulating effects of agricultural technology innovation. The results are presented in columns (5) and (6) of Table 11.

The results indicate that in regions with high information accessibility, agricultural technological innovation significantly reduce the urban–rural income gap. Efficient information transmission channels provide farmers with more convenient access to agricultural technology and market information. Consequently, the latest agricultural production techniques and market demands can be promptly understood and swiftly applied by farmers in their production processes, facilitating the optimization of agricultural production workflows and enabling timely adjustments in production strategies to align with market demands. These findings corroborate Jensen’s (2007) seminal study on fishermen in Kerala, India, which showed that mobile phone adoption led to significant improvements in market efficiency and welfare gains.

In contrast, in regions with low information accessibility, the flow of information is hindered, which obstructs the advancement and implementation of agricultural tech innovation. Farmers face difficulties in timely acquiring relevant knowledge and resources, resulting in a significant decline in the diffusion rate of technology. Additionally, the lack of effective market information exposes farmers to considerable un-certainty in their production and sales decisions, hindering the conversion of the eco-nomic benefits derived from technological innovation into actual income growth. This pattern is consistent with Foster and Rosenzweig's (2022) research on learning and technology adoption in agriculture, which highlighted how information constraints can significantly impede the adoption of profitable agricultural technologies. Therefore, the level of information accessibility emerges as a crucial variable determining whether agricultural technological innovation can effectively narrow the urban–rural income gap. The efficiency of information flow directly influences the equitable distribution of technological benefits across urban and rural regions.

This paper employs the proportion of the employees engaged in the primary industry as an indicator for measuring local employment structure (Emp_stru). Column (1) of Table 12 reports the impact of agricultural technological innovation on local employment structure. The estimated coefficient is significantly negative at the 1% statistical level, indicating that agricultural technological innovation may lead to a greater outflow of labor from the agricultural to non-agricultural industries. The results in column (2) suggest that agricultural technological innovation can reduce income disparity, while a higher proportion of labor remaining in the agricultural sector exacerbates the urban–rural income gap. Overall, agricultural technological innovation releases surplus labor in rural areas and narrows the income disparity by improving the employment structure.

The economic significance of this finding is that technological advancements in agriculture reduce the demand for labor in traditional farming activities, freeing up workers to seek employment in more productive sectors such as manufacturing and services. This reallocation of labor from low-income agricultural jobs to higher-income non-agricultural jobs contributes to narrowing the income gap between urban and rural areas.

Persistent urban–rural resource misallocation critically impedes dual-economy transformation (Wang and Bai, 2013). Rural capital-technology deficits and labor trapping in low-yield agriculture generate dual inefficiencies—allocative distortions and factor productivity mismatches (Restuccia and Rogerson, 2008). To test whether agricultural innovation mediates income convergence through improved allocation efficiency, this study operationalizes the Res_allo index as a structural mediator. The mediation analysis specifically examines how technological advances reconfigure rural production factors (land consolidation, labor upskilling, capital redeployment) to counteract urban-biased resource lock-in effects.

In column (3) of Table 12, the coefficient of ATC is-0.0061, which is statistically significant at the level of 5%, indicating that agricultural technological innovation significantly reduces the degree of resource misallocation. This finding suggests that with advancements in agricultural technology, resources can be allocated more efficiently among different industries. In column (4), the coefficient of ATC is significantly negative at the level of 1%, while the coefficient of Res_allo is significantly positive at the same level, indicating that a higher degree of resource misallocation corresponds to a wider income gap. In other words, the distortion in resource allocation exacerbates income inequality, while agricultural technological innovation can optimize resource allocation and further narrow the income gap.

Solow (1957) pointed out that technological progress is the primary driver of productivity enhancement. In the context of China’s economic transformation and high-quality development, technological innovation has emerged as a crucial power for improving productivity. According to dual economy development theory of Lewis, productivity improvement in the agricultural sector is a key driving force behind income growth in rural area. The rural labor productivity is used as the indicator of agricultural production efficiency (Prod_eff), examining the third pathway through which agricultural technological innovation affects the urban–rural income gap.

Columns (5) and (6) of Table 12 present the corresponding results. The findings reveal that the coefficient of ATC is significantly positive at the level of 1%, indicating that advancements in agricultural technology leads to improvements in rural labor productivity, thereby enhancing agricultural productivity. Furthermore, the coefficient of Prod_eff on Theil is significantly negative, suggesting that increasing productivity in rural areas can effectively narrow the income disparity between urban and rural regions.

The underlying reason for this effect is that technological advancements in agriculture, such as improved farming techniques, better seeds, and more efficient machinery, directly enhance the productivity of rural labor. As rural workers become more productive, their incomes rise, which helps to close the income gap with urban areas. Additionally, higher agricultural productivity can stimulate rural economic growth, further contributing to income convergence.

Based on the regression results of the single-panel threshold model (Equation 6) presented in Table 13, when Urban ≤ 0.3317, the relationship between agricultural technological innovation and the urban–rural income gap is negatively correlated, but lacks statistical significance. When 0.3317 < Urban ≤ 0.4410, there is a significant effect on improving the urban–rural income gap, with a coefficient of −0.0027. When Urban < 0.4410, the impact of agricultural technological innovation on income disparity is further strengthened, with the absolute value of the coefficient increasing to 0.0043. In the early stages of urbanization, rural infrastructure is relatively underdeveloped, and the mobility of production factors such as labor and capital are restricted, making it difficult for technological innovation to be fully translated into productivity improvements. During this stage, the diffusion and application of agricultural technological innovation are constrained, resulting in a negligible effect on reducing the urban–rural income gap. However, as urbanization accelerates, the flow of resources between cities and countryside intensifies, gradually boosting agricultural technology’s impact. Upgraded rural infrastructure—think better roads and internet access—helps spread new farming methods that boost crop yields. These technological leaps do not just improve harvests; they put more money directly in farmers’ pockets through efficient operations. Meanwhile, the urban job market pulls rural workers cityward, creating a double effect—fewer hand on farms drive tech adoption, while urban wages pull up rural earnings through remittances.

This dynamic aligns perfectly with China’s development playbook that intentionally ties urban growth to countryside revitalization. Beijing’s push to connect cities with villages through infrastructure upgrades has essentially built the plumbing systems needed for agricultural tech to flow where it’s most effective—in areas already experiencing significant urban expansion.

The results of double-panel threshold model (Equation 7) indicate that the impact intensity of agricultural technological innovation on the urban–rural income gap becomes more pronounced as the level of educational attention increases. When Edu ≤ 0.1053, the coefficient is −0.0007 and lacks statistical significance. However, when Edu > 0.1053, the coefficient increases to −0.0038, which meets the significance threshold at 1%.

This suggests that once educational attention surpasses the threshold of 0.1053, the income regulating effect of agricultural technological innovation becomes significantly evident. When educational attention is relatively low, the promotion of agricultural technological innovation is constrained by factors such as infrastructure, funding, and technical training, resulting in an inability to significantly reduce the urban–rural income gap. Nevertheless, when educational attention exceeds this threshold, the barriers to technology promotion are lowered through policy guidance, financial support, infrastructure development, and optimization of market mechanisms. Consequently, scale effects and resource allocation efficiency are enhanced, allowing innovative outcomes to benefit rural communities more broadly.

This finding aligns with China’s national strategy of prioritizing education and rural development. The government’s increased investment in education and rural infrastructure has played a crucial role in enhancing the capacity of rural areas to adopt and benefit from agricultural technologies.

According to the results of threshold model (3), when Int ≤ 8.5061, the coefficient of ATC is 0.0020. When 0.0020 < Int ≤ 23.2499, the effect of agricultural technological innovation on reducing the urban–rural income gap improves, with the coefficient increasing to 0.0038. When Int > 23.2499, the impact coefficient changes to-0.0048. The economic significance of these threshold values is that regions with low information accessibility (below 8.5061 broadband users per 100 individuals) face significant barriers to the dissemination of agricultural technologies. In such regions, farmers have limited access to information about new technologies and market conditions, which hinders the adoption and application of innovations. However, as information accessibility surpasses the first threshold (8.5061), the flow of information between urban and rural areas improves, facilitating the diffusion of agricultural technologies and enhancing rural productivity. When information accessibility exceeds the second threshold (23.2499), the impact of agricultural innovation on income inequality be-comes even more pronounced, as improved information flow enables farmers to optimize production decisions and achieve higher market returns.

Improved information accessibility accelerates the dissemination of agricultural technology between urban and rural areas, facilitating the acquisition and application of cutting-edge technologies, and enhances productivity and income. It also alleviates market information asymmetry, enabling farmers to optimize production decisions and achieve higher market returns. Consequently, as information accessibility rises, the role of agricultural technological innovation in narrowing the urban–rural income gap steadily intensifies, demonstrating a gradually progressive stepped strengthening effect. These findings are closely linked to China’s efforts to promote digital rural development and bridge the digital divide between urban and rural areas. The government’s initiatives to expand broadband infrastructure and improve information accessibility in rural regions have created an enabling environment for the diffusion of agricultural technologies, particularly in regions with higher levels of information accessibility.