Creation or disruption? Doubts from the internet applications in China's rural sector

Zhou, Feng; Deng, Hongtu Article Creation or disruption? Doubts from the internet applications in China's rural sector Journal of Innovation & Knowledge (JIK) Provided in Cooperation with: Elsevier Suggested Citation: Zhou, Feng; Deng, Hongtu (2023) : Creation or disruption? Doubts from the internet applications in China's rural sector, Journal of Innovation & Knowledge (JIK), ISSN 2444-569X, Elsevier, Amsterdam, Vol. 8, Iss. 4, pp. 1-14, https://doi.org/10.1016/j.jik.2023.100450 This Version is available at: https://hdl.handle.net/10419/327356 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by-nc-nd/4.0/ Creation or disruption? Doubts from the internet applications in China’s rural sector Feng Zhou a , Hongtu Deng b, * a Business School, Shandong University of Political Science and Law, Jinan, China b School of Economics and Statistics, Guangzhou University, Guangzhou, China ARTICLE INFO Article History: Received 25 July 2022 Accepted 3 November 2023 Available online 21 November 2023 ABSTRACT To address the current large gap in research on the Internet applications in the Chinese rural sector, this paper explores the effect of these applications on the labor productivity of Chinese farmers from a creative destruction perspective. First, we construct a model to analyze the direct and indirect paths of the Internet applications impact on the productivity of farm households at the theoretical level; second, we conduct an empirical study corresponding to the theory based on micro-survey data obtained from China’sfirst nationwide large-scale social survey project. Specifically, we verified the Internet applications direct effect on farm household labor productivity through the mean treatment effect approach and explored the efficiency and mechanism of action of the Internet productivity effect in the rural sector using the mediating effect model, Probit model, and the counterfactual analysis framework. The study finds that the productivity effect of the rural Internet applications still has a certain gap compared with the urban sector, although it is not at the "Solow’s paradox" stage. Further discussion reveals that the Internet adoption in rural areas is a process of "creative destruction", and its destructive effects are dominant at this stage, while the creative effects still need to be further explored. This paper emphasizes that, for developing countries, accurately identifying the development stage of the Internet applications and further exploring its creative role is an important way to improve labor productivity in rural areas, and it is also an important step to promote the construction of digital villages. © 2023 Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Keywords: Internet applications Farm labor productivity Creative destruction Intermediary effect JEL: J20 O18 Introduction Information and communication technology (ICT) refers to the software technologies needed to run information devices and perform information management (Kozma & Vota, 2014), which are integrated into industries or services or combined with new technologies in various fields such as robotics and nanotechnology, thereby connecting all products and services to the network (Kim, 2021). Since ICT technology applications are not yet fully popular in rural China, they are mostly embodied at the level of Internet technology (hereafter referred to as Internet applications). It is observed that modern technology with the Internet at its core has a dual effect of Schumpeterian creative destruction, which can both "destroy" the traditional smallholder production and management system and change the allocation of agricultural factors to create new agricultural production methods (Wan, 2015). This creative destruction mechanism formed by technological progress has successfully promoted the modernization of agriculture in rural areas and has become an important catalyst for small farmers to actively or passively transition from the traditional agricultural sector to the modern agricultural sector as new farmers or to completely leave agriculture for nonfarm occupations (Ma & Ning, 2017). This paper explores the "productivity effect" of Internet applications in the rural sector and the "transmission" mechanism from Internet applications to this effect. In the early stage, Strassmann and Paul (1990) surveyed 292 companies and found that there was no direct correlation between companies’investment in the Internet and return on investment (ROI), a situation that was later called "Solow’s paradox" by academics. 1 It was not until the mid-1990s that the literature confirmed the existence of the productivity effects of the Internet and the information industry (Amendola et al., 2005;Czernich et al., 2011;Jorgenson & Stiroh, 1999;Martínez et al., 2008;Oliner & Sichel, 2000). Solow admitted in 2000 that the "Solow paradox" had been solved. It is evident that even in developed countries, Internet development needs * Corresponding author at: Guangzhou University City, 712, Wenqing Building, Teaching Area, Panyu District, Guangzhou City, Guangdong Province, China. E-mail address: [email protected] (H. Deng). 1 Also known as the "productivity paradox". Proposed in 1987 by Robert Solow, a Nobel Prize−winning economist, it means that although companies invest heavily in IT, they achieve little in terms of productivity. https://doi.org/10.1016/j.jik.2023.100450 2444-569X/© 2023 Published by Elsevier España, S.L.U. on behalf of Journal of Innovation & Knowledge. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Journal of Innovation & Knowledge 8 (2023) 100450 Journal of Innovation &Knowledge https://www.journals.elsevier.com/journal-of-innovation-and-knowledge to go through a very long period of "Solow’s paradox" (Ping, 2001). In this stage, even if the Internet has been popularized, it does not mean that productivity has increased substantially. As shown in Fig. 1, in the context of the urban-rural dual structure, the gap between Internet applications in the urban and rural sectors has only increased since 2007, and although there has been an inflection point of growth in rural Internet penetration since 2018, the gap between the two sectors has not changed substantially. Moreover, studies have shown that the consumption effects triggered by Internet applications differ significantly between urban and rural areas (Xiang, 2018). The income returns to rural households are much lower than those to urban residents (Tan et al., 2017). Both individual and urban-rural income disparities are increasing (Tan et al., 2017), and both individual and urban-rural income gaps are widening. Studies by Britz and Blignaut (2001) and Bonfadelli (2002) also suggest that ICT is beneficial only for the higher-income and higher social classes and that the introduction of ICT may not have a positive impact on farm households in developing countries. A synthesis of the literature reveals that the effects generated by the Internet are different in the urban and rural sectors. Due to the large gap between urban and rural areas in terms of infrastructure development, human capital reserves, industrial patterns, and economic development stages, the formation and transmission mechanisms of the Internet effect in urban and rural areas are also very different. Based on the above points, and in order to further explore the digital transformation of the rural sector and the organizational skills of agricultural production, this paper makes a systematic and in-depth analysis of the "productivity effect" of the agriculture and rural sectors. The Fourth Plenary Session of the nineteenth Central Committee of the Communist Party of China proposed to improve the mechanism of labor, capital, land, knowledge, technology, management, data and other contributing production factors by market evaluation and remuneration based on contribution, raising data to the strategic level of production factors for the first time. However, as the foundation of the national economy and the "basic plate" for the comprehensive revitalization of rural areas in the new era, the combination of the digital economy and the revitalization of rural areas is still very low. In 2019, China’s agricultural digital economy accounted for only 8.2 % of the industrial added value, which has not yet reached 25 % of the service industry and 50 % of the industry. It is urgent to carry out more theoretical research and practical exploration on the effective combination of modern agriculture and digital economy (Zhong & Liu, 2021). Therefore, there is a big gap in the current research in terms of exploring the relationship between the digital technology and modern agricultural development, especially with respect to the analysis of how the Internet applications lead to the transformation of agricultural digitalization. Thus, this research is necessary. In order to fill the research gap in the existing literature, this paper will expand and deeply analyze the existing studies in the following three aspects. First, Jiang (2017) and Cheng et al. (2020) did not pay attention to the special characteristics of the data elements and digital technologies generated behind the Internet application, and this paper focuses on the promotion effect while also focusing on the crowding out effect of the Internet application. Second, although Zhu et al. (2019),Li and Yin (2017)) have noted that the productivity effect of the Internet applications is limited by the allocation of labor resources, and the impact of agricultural informatization on total factor productivity in agriculture has a nonlinear effect with respect to the difference in the level of rural human capital. However, no study has yet given a general explanation for the biased technological changes in the direction of labor factors by the Internet applications in relation to consistency with their productivity effects. In this paper, we will compare the current status of Internet adoption in the rural and urban sectors in China, specifically verify the biased technological change in the direction of labor factors for Internet adoption, and respond positively to the key question of whether Internet adoption in rural China is still at the "Solow’s paradox" stage. Third, Acemoglu (2014) re-examines the Solow paradox based on the U.S. manufacturing sector, and similarly based on doubts about the Solow paradox in the Chinese agricultural sector; this paper will investigate the "creative destruction effect" of the Internet in the agricultural and rural sectors at both the theoretical model and empirical evidence levels, and, on this basis, it provides a clear, economic analysis of the impact of the Internet applications on farmers’labor productivity theoretical logic and economical empirical framework as a way to provide scientific support for focusing on the cost effectiveness and impact of these technologies on small and large scale agriculture, as well as the socioeconomic impact and security issues of applying the Internet Fig. 1. China’s Internet penetration profile, 2007−2020. Note: The information is obtained from the statistical survey on the development status of the Internet in China by the China Internet Network Information Center. Since the data for December 2019 were not published, they were replaced by the data published in March 2020. F. Zhou and H. Deng Journal of Innovation & Knowledge 8 (2023) 100450 2 applications such as artificial intelligence and Internet of Things (Patel, 2023). Thus, to achieve this goal, we explore the effect of the Internet applications on the labor productivity of Chinese farmers from the perspective of creative destruction. This study followed the following steps and methods. First, we review the relevant literature based on ICT studies. Second, we draw on the theoretical framework of Han et al. (2011) and Mittal and Nault (2009) to explore the possible paths when Internet applications exert productivity effects in the rural sector, separating the direct path from the indirect path. Third, we apply ordinary least squares (OLS) and Probit model quantitative methods to empirically test the Chinese General Social Survey data. The database is currently a rare and nationally representative data on individual Internet use in China, and thus it can provide highly relevant data for this study. After processing, we finally obtain 3047 rural samples and 5467 urban samples, covering 28 provinces (autonomous regions and municipalities) in China. Finally, the empirical results are discussed and insights are provided. Literature review Most of the existing studies on ICT adoption focus on two levels, one is the firm level, for example, Kijek et al. (2019) studied the relationship between ICT investment and firm productivity through structural equation modeling, clarifying the Solow paradox and finding that process innovation is an important moderator between firm labor productivity and ICT technology adoption. Torrent et al. (2022) also found, through a study of Spanish firms, that when firm development focuses more on ICT investment, R&D activities, and product innovation, such firms develop a significant productivity advantage over other firms as a result; another is the agricultural level, where Yueh et al. (2013) found, based on a study of the geospatial attributes of farmers’associations, that the organizational digitization of farmers’associations effectively activates agricultural innovation and development, and Guo et al. (2018) found, through the analysis of micro data in suburban Beijing, that ICT technology changed the service model in the rural sector, and that rural distance education programs had a significant contribution to both farm household income and productivity levels. A number of scholars have also confirmed the beneficial effects of ICT technology applications in agriculture based on many case studies such as mushroom farm management (Kassim et al., 2019), modern pig farming (Mahfuz et al., 2022), aquaculture (Mustapha et al., 2021), and agricultural systems (Nayak et al., 2020). However, there are also studies that confirm that the application of ICT technology is a great challenge for those farmers who cannot cope with the digital transformation and that it does not have only positive effects although the rural sector is undergoing a digital transformation process (Ferrari et al., 2022). This view coincides with the findings of Ogutu et al. (2014), who, in assessing the impact of ICTbased market information service programs on agricultural inputs in Kenya, found that while the interventions had significant boosts on seed, fertilizer, and labor productivity, they had negative effects on labor factor allocation. Among the existing studies on Internet applications in the rural sector in China, Zhu et al. (2019) also confirm that labor resource allocation is closely related to the productivity effect of Internet applications, and Han and Zhang (2015) similarly show that the effect of agricultural informatization on total factor productivity in agriculture has a nonlinear effect on differences in rural human capital levels through a study of panel data from 2002 to 2010. Although some studies show that the Solow paradox in the urban sector has been resolved, the above studies suggest that there are still differences on whether the Internet application in the rural sector has a positive impact on agricultural labor productivity, and no study has yet given a general explanation for the biased technological change towards labor factors by Internet applications that is consistent with its productivity effect. Previous studies on the productivity effects of the Internet in agriculture have mostly focused on the total factor productivity perspective (Han & Zhang, 2015;Yu & Zhu, 2011;Zhu et al., 2019). However, it is important to understand that the reason why developed countries reflect technological progress through TFP (Total Factor Productivity) is because their technological innovation relies on a large amount of R&D investment, which can then be measured through the residual term. As a developing country, China’s latecomer advantage in technological innovation means that the new technologies it has introduced, including ICT, are generally solidified in infrastructure with public capital attributes, which is reflected more in labor and capital factors than in TFP. In particular, the data factors and digital technologies behind Internet applications have special characteristics that are different from traditional factors, and they are associated not only with the promotion effect but also with the crowding-out effect (Jiang & Sun, 2020;Zuo & Ai, 2021). This is especially true in rural areas. This paper argues that existing studies precisely ignore the indirect role played by the biased technological changes of the Internet on other factors in the Internet productivity effect (Kong et al., 2015). It is worth noting that the verified productivity effect of Internet applications from a labor factor allocation perspective based on provincial panel data, and the findings indicated that the structural shift in labor force employment plays a greater mediating role than that of rural population urbanization. Zhao (2015) proposed that Internet 2.0 has realized the decentralization of the industrial structure and has advanced from the "resource" nature of Internet 1.0 to the "capability" incarnation that can lead to the transmutation of the industrial competition pattern. The digital economy based on the Internet provides information support for highquality agricultural development and promotes the process of the modernization of agriculture and rural areas (Wang et al., 2018). However, as noted above, there are significant differences in the penetration of Internet applications between rural and urban areas. Whether or how the shift in labor employment plays the same mediating role in rural areas, and the specific impact of this mediating role on Internet adoption, remains to be explored. The development of the existing agricultural business model based on Internet technology means that new technology replaces the original agricultural model, and there is a demand for labor force with high human capital value and information technology. Some traditional smallholder agricultural operators have withdrawn, modern agricultural operators have entered. Additionally, agricultural information technology has pushed the modernization of agriculture while impacting traditional agricultural production methods, which is reflected in the decrease in traditional agricultural jobs and the increase in modern agricultural jobs. It should be noted that the jobs created by the creation effect not only accommodate the labor force transferred from traditional agriculture but are also an important alternative for the labor force eliminated by the destruction effect in the urban sector. If the productivity of the newly created jobs in the rural sector is not as high as that of the eliminated jobs in the urban sector, the inevitable result will be labor force inversion. Therefore, attention to the creative disruptive effects of Internet applications in the rural sector has direct implications for preventing labor force inversion (Cai, 2021). Next, this paper will analyze the creative destructive effects of Internet applications in China’s rural sector and their impact on the labor productivity of farm households at the theoretical and empirical levels, respectively. Theoretical model Before proceeding to the empirical study, we will theoretically explore the possible paths through which Internet applications exert their productivity effect in the rural sector and, more importantly, F. Zhou and H. Deng Journal of Innovation & Knowledge 8 (2023) 100450 3 logically analyze the specific and practical aspects of the effect at both the direct and indirect levels. In Fig. 2, the solid line indicates the direct effect of Internet applications on the labor productivity of farmers, where the Internet itself acts as ICT capital input to promote enhanced technological progress, which directly affects agricultural production and operational processes and directly increases the labor productivity of farmers. The dotted line indicates the gradual change from traditional agricultural production methods to modern agricultural subsistence methods in the agricultural sector under the impact of the digital economy. The dashed line shows the indirect impact of the digital economy on the productivity of farm households, which is the result of the change from the traditional agricultural production method to the modern agricultural subsistence method. In this section, we explore whether Internet applications in rural China are in the "Solow paradox stage" and their paths of action. Drawing on the theoretical framework of Han et al. (2011) and Mittal and Nault (2009), this paper sets the baseline production function in the form of a Cobb-Douglas function in the following form: Y¼AKaLbTgZ’ð1Þ where K represents non-ICT capital factor inputs other than ICT capital, Z is ICT capital factor input, L is labor factor input, T is land factor input, Y is total output of farm households, a;b;g;and ’are the output elasticities of non-ICT capital, labor, land and ICT capital factor inputs, respectively. A is the Hicks neutrality function. It can be seen that A in this equation includes but does not effectively explain the biased technological change of ICT capital towards other factors, in particular, ICT capital input is not equivalent to the R&D input as directly reflected by total factor productivity. Therefore, the indirect spillover effect of Internet applications directly measured by total factor productivity is likely to cause bias. The multiplicatively separable exponential form set by Mittal and Nault (2009), which can visualize the biased technical change of ICT capital on other factors, provides the possibility for this paper to portray the indirect effect of ICT capital on farm productivity. Specifically, the biased technical change of ICT capital on non-ICT capital, labor, and land is defined as a function of ICT capital, whereby KξðZÞ;LtðZÞ;TyðZÞ, respectively, deKZnoted by, LZ, and TZ. Where ξ0ðZÞ>0;t0ðZÞ>0;y0ðZÞ>0, assuming that ξð0Þ¼tð0Þ¼1, i.e., when there is no ICT capital input, there is no impact on factors such as non-ICT capital, labor, and land, which are therefore set to KZ,LZand TZ. This form, in fact, does not lose its generalizability. By substituting it into Eq. (1) and correcting it, we can finally obtain the expanded Cobb-Douglas function form as follows. Yc¼SKZaLZbTZgZ’ð2Þ In Eq. (2), K, L, T, and Z meanings remain unchanged, as they were defined before. Ycdenotes the total output of farm households after the augmentation, and a,b;g, and ’denote the parameters in the augmentation function that distinguish it from the baseline functional form; however, they all correspond to the baseline functional form, a;b;g;and ’. In addition, the S in the extended form is compared with the A in Eq. (1), and it is clear that the former no longer contains the biased technological change of ICT capital of other elements. To more precisely portray the indirect effects of ICT capital, we set the functional forms KξðZÞ;LtðZÞ;TyðZÞof non-ICT capital, labor and land, respectively, to the exponential forms Kehz ;Lemz ;Terz. Substituting this into Eq. (2), the specific expression of the augmented functional form is obtained as follows. Yc¼SKe hz ½aLemz ½bTerz ½gZ’¼SKaLbTgZ’eGZ ð3Þ In Eq. (3),G¼ah þbm þgr is the weighted average sum of the output elasticities of non-ICT capital, labor, and land in the incremental Eq, which means that when investment in ICT capital such as the Internet increases, non-ICT capital, labor, and land will each change in ah;bm;and gr;which is the proportion of biased technological change. To prove that the separation of the indirect effects by equation (3) is scientific, this paper finds the cross-partial derivatives of each factor on the ICT capital factor for Eqs. (1) and (3) and obtains. @2Y @K@Z¼AaK1aLbTg’Z1’ @2Y @L@Z¼AbL1bKaTg’Z1’ð4Þ Fig. 2. The path of the effect of Internet applications on the labor productivity of farm households. F. Zhou and H. Deng Journal of Innovation & Knowledge 8 (2023) 100450 4 @2Y @T@Z¼AgT1gLbKa’Z1’ @2Yc @K@Z¼SaK1aLbTg’Z1’þGZ’  eGZ @2Yc @L@Z¼SKabL1bTg’Z1’þGZ’  eGZ ð5Þ @2Yc @T@Z¼SKaLbgT1g’Z1’þGZ’  eGZ A comparison of the above two equations indicates that the value of the proportional change in ICT capital’s bias toward other factors is zero, i.e., h¼m¼r¼G¼0;when Eq. (4) is equal to Eq. (5), which means that the incremental function obtained after the portrayal of the indirect effect of ICT capital does compensate for the possible bias in the original benchmark production function’s measurement of the productivity effect of ICT capital. In addition, in keeping with the research theme of this paper, this paper assumes that h¼r¼0; i.e., Internet adoption has not yet had a significant biased technological effect on land and non-ICT capital. 2 To more intuitively show the separation of direct and indirect effects by the incremental function setting in this paper, we denote the logarithmic form of each variable in lowercase letters and take the logarithm of Eqs. (1) and (3), respectively, to obtain the resultant equations as follows. y¼aþakþblþgtþ’zþe1ð6Þ y¼sþakþblþgtþ’zþGZ þe2ð7Þ In the above equation, a denotes the factor-neutral technological progress of the benchmark Cobb-Douglas model, l, t, and z denote the logarithms of L, T, and Z, respectively, the coefficients are the output elasticities of each factor, e1and e2are error terms that are consistent with independent identical distributions. GZ means the broadening effect of ICT capital on the labor factor and other factors separated from a. Therefore, the s in Eq. (7) is different from the a in Eq. (6). And since G¼ah þbm þgr,Eq. (7) can be further be written as the following equation. y¼sþakþhZðÞþblþmZðÞþgtþrZðÞþ’zþe2ð8Þ In Eq. (8), the direct effect of ICT capital on farmers’output is ’z, and the indirect effects on the labor factor and other factors are ahZ; bmZand grZ. Theoretical basis of the model Intuitively, LtðZÞdenotes the biased technological change exhibited by Internet applications in the direction of labor factor allocation, which is an important manifestation of the creative destruction role of the Internet in the rural sector. Next, we will theoretically and logically analyze the specific manifestations of the creative destruction role of Internet applications in the rural sector and LtðZÞ. What are the specific implications? The key rubric for creative destruction is that the new structure creates a stronger incremental payoff effect than the old structure (Zhao, 2015). Specifically, in the rural sector, traditional agricultural production is strongly dispersed in space and time, and there is also a strong dependence on natural conditions, which makes it difficult to match agricultural output with labor inputs and to implement hourly or piecework wages, thus making it difficult to improve agricultural production efficiency. Digital agriculture based on the Internet, on the other hand, makes it possible to visualize and express the entire agricultural production process and allows farmers to become important nodes in modern agriculture, production fields, or distribution fields. In the context of upgraded consumer demands, the production model has successfully changed from a production orientation to a sales orientation, and production efficiency has greatly improved. Therefore, this change from traditional agricultural production methods to modern agricultural production methods driven by Internet applications is undoubtedly a creative destruction realization process. Currently, Internet applications are no longer simply a factor of agricultural production itself but have become a derivative of agricultural production efficiency. The platform characteristics have successfully broken the barriers between small farmers and large markets and helped the development of agricultural product e-commerce platforms while achieving the integration of logistics, capital flow and information flow in agricultural production (Wang et al., 2020). The theory of creative destruction emphasizes that the new production structure with new means of competition will overturn the traditional production structure, which means that the modern agricultural production method will directly impact traditional agriculture, and the traditional production structure will be replaced by modern agricultural business entities if they cannot quickly adapt and master the new means of competition. As mentioned earlier, many elements are involved in this change, and the change in jobs is an important microcosm of this process. In terms of job creation effects, at the organizational level, the Internet provides natural connectivity for various agricultural organizations in the industrial chain. These organizations can be connected to new organizations on a larger scale based on various forms of cooperation in the production chain, providing a basis for the emergence of more leading enterprises. On the other hand, the relatively low-skilled labor force eliminated from the urban sector due to the development of the Internet may also become new farmers in this way. The destructive effect on jobs is manifested mainly in the passive withdrawal and independent withdrawal of traditional agricultural operators. Passive withdrawal is manifested in two aspects: theincreaseinproductionefficiency and organic composition of capital in traditional agriculture under Internet applications, which helps traditional agriculture achieve a reduced demand for labor, and the replacement of traditional agriculture by modern agriculture. The main operators can choose to withdraw only when they are unable to keep up with the pace of development. Independent withdrawal refers to the increase in the human capital of labor under Internet applications, and independent exit refers to the decision of the labor force to leave traditional agriculture and transfer to modern agriculture or nonagricultural work, driven by the effect of Internet applications on the improvement in the human capital level of labor (Ma & Ning, 2017), the expansion of information channels and the diversification of social networks (Wang & Zhou, 2013). Under the dual effect of the creation effect and the destruction effect of Internet applications, the quality of jobs in the rural labor market is improved, and the labor force shifts from traditional agriculture with lower productivity to modern agriculture or work in the nonagricultural sector with higher productivity, which is a process of achieving the optimization of labor factor allocation and "Pareto improvement". The above studies are combined to derive the following hypotheses in this paper. Hypothesis 1. : Internet applications have directly contributed to the increase in labor productivity of farmers in the form of enhanced technological progress based on the absolute marginal output of factors, and the rural sector is not currently in the "Solow paradox" stage. 2 Although this hypothesis does not correspond to the actual situation, it does not undermine the basic logic of this study, specifically the indirect role played by land and capital, which the authors will examine further in subsequent studies. F. Zhou and H. Deng Journal of Innovation & Knowledge 8 (2023) 100450 5 Hypothesis 2. : Internet applications promote the change from traditional to modern agricultural production methods and, indirectly increase the productivity level of farmers under the dual effect of creation and destruction. Materials and methods Further empirical tests are needed to determine whether the economically logical conclusions obtained from the theoretical derivation strictly fit the empirical facts. Next, based on available data, we will further verify the biased technological change in labor factors under the effect of enhanced technological progress and creative destruction caused by Internet applications through an empirical analysis of the productivity effect of Internet applications in the rural sector. Data sources The data used in this paper come from the 2017 data released by the China General Social Survey (CGSS). The selection of these data is based mainly on the following considerations. First, the data are jointly executed by Renmin University of China and other academic institutions across China, making this the first national and comprehensive large-scale social survey project in China and giving it important reference value for exploring real social issues. Second, the CGSS collected a total of 12,582 valid samples from 28 provinces (autonomous regions and municipalities) in 2017, and the A and C modules of the year data both contain questions related to residents’use of the Internet. These are rare and nationally representative data on current individual Internet use in China that are highly relevant for this study. Third, the Internet tends to mature in all aspects of operation and use as the application time is extended; therefore, this paper does not pursue incremental sample sizes but selects the latest published 2017 data with a view to reflecting the most up-to-date situation of Internet applications in rural areas at the current stage. Before the study, to ensure the quality of the sample, the data were processed as follows. (a)Residents of rural and urban areas were classified according to their places of residence, and the relevant variables were identified and processed separately. (b) The study population was controlled to the rural labor force population aged 18−65 and their corresponding households in order to restrict the data to those relevant to the study, and the individual information of each household member was summarized and analyzed. (c) The outliers of each variable were identified, and (d) the outliers were then identified and eliminated to ensure that all data used were valid samples. Ultimately, 3047 rural samples and 5467 urban samples were obtained, covering 28 provinces (districts and municipalities) in China. Variable description Based on the aforementioned logical derivation, this paper obtained various analytical variables with empirical and testable implications. This section will convert these analytical variables into core and control variables in the empirical analysis based on the available data. We draw on relevant studies (Cheng et al., 2020;Mao et al., 2019) to identify the variables as follows. Labor productivity of farm households. The choice of the indicator of farm household labor productivity in this paper is based on two considerations; first, one of the purposes of this study is to analyze the creative destructive effect of interconnection applications on labor force employment in the rural sector, so for this paper, the choice of labor productivity rather than total factor productivity is more relevant to the main thrust of the analysis and more consistent with the needs of the preceding and following analysis. Second, the indicator is borrowed from the study of Mao et al. (2015), and is portrayed by the logarithm of the ratio of total income to the number of household labor, which includes income from agricultural operations and non-agricultural labor, reflecting both the level of agricultural productivity and non-agricultural production of farm households, so it also fits the theoretical mechanism part of this paper to some extent. Internet applications. Focusing on the rural sector, this paper indicates the ICT capital investment of farm households through the current status of Internet applications. If members of the labor force had accessed the Internet through computers, cell phones or smart devices in the previous six months, the households to which they belong are considered to be connected to the Internet and to have invested in Internet applications. Creation effect. Due to data availability, this paper considers the part of the labor force that used to work in nonagricultural jobs and has now returned home to farm owing to the attraction of new jobs created by modern agriculture, that is, the creation effect of Internet applications in the rural sector. Compared with the labor force that has not migrated, this part of the labor force has higher human capital value and social capital. This indicator reflects the creation effect of the Internet to a certain extent, but we admit that it does not fully reflect the creation effect of Internet applications in the rural sector. After all, the current attraction effect of modern agriculture on the labor force is not intuitively obvious, and the rise of modern agriculture is still emerging. Destruction effect. This paper reflects the decline of traditional agriculture and the withdrawal of some small farmers by shifting the rural labor force in the employment structure; i.e., the value is 1 when a member of the rural labor force is currently engaged in nonagricultural work and 0 otherwise. Other control variables. In addition to the core variables mentioned above, the control variables in this paper portray three main categories: individual characteristics of members of the labor force, the characteristics of the households to which they belong and the characteristics of the region. Individual characteristics include mainly the health status, education level, political outlook, gender and age, marital status and ethnicity of the labor force. Among them, health status is divided into five levels, from very unhealthy to very healthy, with values from 1 to 5. Education level is divided into uneducated, literacy class, elementary school, junior high school, vocational high school, general high school, secondary school, technical school, university specialist (adult formal education), undergraduate (adult formal education) and postgraduate, with values from 1 to 13. Political outlook is assigned a value of 1 if the individual is a member of the Chinese Communist Party and 0 otherwise. Age is calculated according to the actual survey year. Marital status is assigned a value of 0 if unmarried and 1 if married (including remarriage). Ethnicity is assigned a value of 0 for ethnic minorities and 1 for Han Chinese. Family characteristics are portrayed by the economic level of the family, assigned on a scale of 1 to 5 for far below average, below average, average, above average and far above average. Regional characteristics are set according to the region to which the individual belongs, with three dummy variables for the east, central and western regions. Table 1 reports the descriptive statistics of the variables examined in this paper. Although the main object of this paper is rural residents, the urban sample is also analyzed because we want to form a control for the rural sample by examining the urban sample simultaneously to verify whether this paper has practical research significance and necessity and to test the of the findings. As shown in Table 1, not only is the overall labor productivity of the rural sample significantly lower than that of the urban sample but also the average value of the labor productivity of the group with Internet access is lower than that of the group without Internet access in urban areas. Therefore, it is necessary to analyze whether Internet adoption in the rural sector is at the "Solow paradox" stage. Focusing further on the rural sector, F. Zhou and H. Deng Journal of Innovation & Knowledge 8 (2023) 100450 6 we find that the average value of labor productivity of households connected to the Internet is 9.36, higher than that of households not connected to the Internet, which is 8.75. Additionally, the proportion of rural laborers in the group not connected to the Internet who have shifted their employment structure is 11.7 %, much lower than that of the group connected to the Internet, which is 41.1 %. This finding tentatively supports the hypothesis derived from the previous theoretical analysis that the productivity effect of the Internet applications in the rural sector is likely to be positive and to play a more significant role in creative destruction; i.e., m6¼ 0. However, further empirical analysis is needed to determine whether there is an indirect effect on the labor productivity of farmers through the creative or destructive effect. Model setting Drawing on Baron and Kenny (1986), a test is proposed for the mediating effects and combined with the study reported in this paper, the benchmark model was set as follows. LP ¼a0þb0Inte þgXþe0ð9Þ Labori¼a1þb1Inte þdXþe1ð10Þ LP ¼a2þb2Inte þb3Labor þuXþe2ð11Þ In the above three equations, LP is the main dependent variable of this paper, indicating the labor productivity of the farm households in the sample. Laboriis the mediating variable, with i¼1;2, denoting the creation and destruction effects of Internet applications in the rural sector, respectively. Inte is the core explanatory variable, denoting Internet applications. Xis the control variable that affects the labor productivity and creation-destruction effects of farm households. ais the intercept, andedenotes the random disturbance term. b0;b1;b2and b3are the coefficients to be estimated, and when the model b0is significant, it means that Internet applications have a significant aggregate effect on farmers’labor productivity. Furthermore, if b1;b2;and b3are significant and satisfy b0>b2, then there is a partial mediating effect. When either b1or b3is not significant, then it is necessary to further test the product of coefficients; i.e., if the significance of the above conditions except b2is satisfied, then rural labor factor allocation has a full mediating effect on farm labor productivity. The Internet acts exclusively on farm labor productivity through its creation and destruction effects on agriculture. Considering whether the farmer is connected to the Internet as a binary random variable Intei, we draw on Mao et al. (2015) empirical model, where Intei¼1 indicates that the farmer is connected to the Internet; otherwise Intei¼0 when the household is not connected to the Internet. Correspondingly, LP1idenotes the actual observed labor productivity of the farmer with Internet access, and LP0idenotes the labor productivity of the farmer without Internet access. It is easy to understand that (LP1iLP0i) is the effect of the Internet on the labor productivity of farmers, which is the core subject of this paper. However, since it is not possible in reality to obtain both LP1iand LP0i,we define LPias follows. LPi¼1Intei¢LP0iþIntei¢LP1i¼LP0iþIntei¢LP1iLP0i ðÞð12Þ a1¼EðLP1iLP0iÞshows the effect of Internet applications on the labor productivity of farmers, which is the average treatment effect of Internet applications. Therefore, this paper establishes the following empirical model I(Eq. (13)) on the basis of Eq. (9), which is used to estimate a1. LPi¼a0þa1¢Inteiþa2¢labor þgXiþeið13Þ Considering the possible heterogeneity among different farmers, Model I is further extended, and Model II (Eq 14) is constructed as follows. LPi¼a0þa1¢Inteiþa2¢labor þXiXi ‾ 0d¢InteiþgXiþeið14Þ where Xi ‾is the mean value of the Xi. As shown by the study of Han and Zhang (2015), there may be a nonlinear relationship between the explanatory variables and household labor productivity. Based on this possibility, we draw on Rosenbaum and Rubin (1983). The method of the logit model is used to obtain an estimate of the propensity score for each household. PðXiÞreplaces the linear functions in Models (Ⅰ) and (Ⅱ), which in turn leads to Model (Ⅲ)(Eq. (15))as follows. LPi¼a0þa1¢Inteiþa2¢labor þbi¢PXi ðÞ þPðXiÞPðXi ‾ 0d¢Inteiþeið15Þ In addition, to analyze the creative destruction effect of Internet applications on the rural sector, this paper divides the employment status of the rural labor force (dependent variable y) into farming (=0) and nonfarm employment (=1). Considering that the discrete dependent variable is not suitable for regression by OLS, the Probit model of labor force employment is constructed based on Eq. (10). Pr labor ¼1ðÞ¼ Fa0þa1¢InteiþgXiþei ðÞð16Þ In addition, since the aforementioned models are subject to the conditional independence assumption (CIA, also known as "selection by measurable variables"), potential unobservable variables that lead to higher labor productivity of the Internet-using farmers themselves Table 1 Descriptive statistics of regression variables. Variables Rural Cities and towns No Internet connection Access to the Internet No Internet connection Access to the Internet Mean Sd Mean Sd Mean Sd Mean Sd Labor productivity 8.75 1.12 9.36 0.99 9.62 0.99 10.38 1.02 Destructive effects 0.12 0.32 0.41 0.49 0.33 0.47 0.69 0.46 Creation effect 0.65 0.48 0.419 0.49 0.55 0.50 0.24 0.43 Health status 2.98 1.12 3.761 1.04 3.29 1.06 3.88 0.91 Education level 2.99 1.39 4.696 2.27 3.68 1.77 7.27 3.34 Political affiliation 0.03 0.18 0.069 0.26 0.06 0.24 0.14 0.35 Gender 0.46 0.50 0.507 0.50 0.44 0.50 0.49 0.50 Age 54.27 8.09 39.66 11.11 56.00 7.06 40.55 12.34 Marital status 0.89 0.33 0.840 0.37 0.85 0.35 0.76 0.43 Ethnicity 0.86 0.34 0.868 0.34 0.95 0.22 0.95 0.22 Family economic level 2.30 0.75 2.599 0.71 2.39 0.74 2.69 0.71 Eastern 0.15 0.36 0.214 0.41 0.47 0.50 0.61 0.49 Central 0.54 0.50 0.530 0.50 0.36 0.48 0.24 0.43 Western 0.31 0.46 0.257 0.44 0.17 0.38 0.15 0.36 F. Zhou and H. Deng Journal of Innovation & Knowledge 8 (2023) 100450 7 may be unavoidable, specifically in the analysis of the creative destruction effect of Internet applications. This is due mainly to the higher education level of the group that may use the Internet and other unobservable variables that affect the employment decisions of the labor force, which requires the exclusion of possible "self-selection" problems from the empirical evidence to ensure that the CIA assumptions are satisfied and endogeneity is avoided. Therefore, this paper adopts a counterfactual causal framework for propensity score matching estimation based on the previous model estimation. The basic idea is to estimate the fitted values of the conditional probability of the sample using the Internet through the logit model; to match farmers using the Internet (treatment group) and those not using the Internet (control group) through different matching methods; and finally to obtain the average treatment effect (ATT) for the labor force using the Internet (participants), the average treatment effect for the labor force not using the Internet (nonparticipants) and the average treatment effect (ATU) for the total sample, which is also the average treatment effect (ATE) obtained from the aforementioned benchmark model. In comparison, the results of the ATT analysis are clearly more relevant to the effect of Internet applications on the labor productivity of farm households. The specific estimated model is as follows. ATT ¼ELP 1ijIntei¼1ÞEðLP0ijIntei¼1ðÞð17Þ ATE ¼ELP 1ijIntei¼1ÞEðLP0ijIntei¼0ðÞð18Þ ATU ¼ELP 1ijIntei¼0ÞEðLP0ijIntei¼0ðÞð19Þ ATE ¼ATT þELP 0ijIntei¼1ÞEðLP0ijIntei¼0ðÞð20Þ Results and discussion Results of empirical analysis Table 2 shows the regression results of the effect of Internet applications on household labor productivity. The first four columns are obtained from the rural sample, and column (5) is obtained from the urban sample for comparison with the rural sample. Specifically, column (1) is the regression result for the core explanatory variables, while column (2) is the regression result for Model I with the average treatment effect obtained by ordinary least squares (OLS) after the control variables are added. The estimated coefficients decrease from 0.61 to 0.33, which fully indicates that the choice of control variables in this paper is scientifically valid. If these control variables are ignored, the regression results are more likely to be the "cumulative value" of the direct effect of Internet applications on labor productivity and the indirect effect of other variables on labor productivity mediated by Internet applications. The regression results are more likely to be "additive" between the direct effect of Internet applications on labor productivity and the indirect effect of other variables mediated by Internet applications. Column (3) of the table shows the regression results of Model II after different household heterogeneity extensions are considered based on Model I. Columns (4) and (5) are the estimated results of Model III after the nonlinear relationship between the control variables and labor productivity is avoided. Together, columns (3) and (4) show that the estimated coefficient of Internet applications remains significantly positive after further accounting for household heterogeneity and possible nonlinear relationships, and the intermediate value of the estimated coefficient obtained from Model I and Model II is obtained after accounting for nonlinear relationships. That is, for every 10 % increase in the penetration of Internet applications, the labor productivity of farm households will increase by 3.22 percentage points, which indicates that in the rural sector, Internet applications do have a more significant impact on improving household labor productivity. The platform economy based on Internet applications provides a new opportunity for rural agricultural development, which greatly improves the efficiency of rural economic operation by promoting the change from traditional agricultural production methods to modern agricultural production methods and to some extent verifies the hypothesis of this paper that the current development of ICT technology represented by Internet applications in rural China is not at the stage of "Solow’s paradox" (Wang et al., 2022). However, whether the gap between the productivity effects of Internet applications in the rural and urban sectors exists, and if so to what extent, requires further analysis. For this reason, this paper analyzes the urban sample on this basis. Considering that the estimation results of Model III for the rural sample are more robust, we compare the regression results obtained based on Model III (column (5)) with the rural sample. The comparison reveals that the regression coefficient of Internet applications on labor productivity in the urban sector is 0.368, which is larger than the regression coefficient of Internet applications in the rural sector of 0.32. Additionally, the productivity effect of Internet applications is 14.3 percentage points in the urban sector than in the rural sector, a result that also confirms the inference contained in the statistical description of this paper. In the context of the urban-rural dichotomy, there is not only a large gap between urban and rural areas in terms of the Internet penetration rate but also a difference of nearly 15 % in the productivity effect based on it. Therefore, an in-depth discussion of the mechanism of the effect of Internet applications on the labor productivity of rural households in the rural sector will be important for the transformation and long-term economic growth of China’s rural economy. Table 2 Impact of Internet applications on household labor productivity. Variables Labor productivity Rural Cities and towns (1) (2)(3)(4)(5) Internet applications 0.61*** 0.33*** 0.31*** 0.32*** 0.37*** (15.69) (7.19) (6.53) (6.13) (7.18) Health status 0.12*** 0.15*** (7.06) (6.11) Education level 0.09*** 0.08*** (8.46) (4.32) Political appearance 0.07 0.06 (0.85) (0.66) Gender 0.07* 0.06* (1.78) (1.70) Age 0.01*** 0.00 (3.35) (1.00) Marital Status 0.09 0.13** (1.54) (2.18) Ethnicity 0.17*** 0.18*** (3.01) (3.24) Family level 0.38*** 0.37*** (14.33) (9.67) West 0.42*** 0.43*** 0.569*** 0.68*** (7.61) (7.90) (9.97) (17.59) Middle 0.17*** 0.17*** 0.22*** 0.63*** (3.48) (3.55) (4.27) (21.15) _pscore 0.85*** 0.66*** (6.85) (5.33) Interaction items N N Y Y Y Constant 8.75*** 7.04*** 7.62*** 8.83*** 9.64*** (312.62) (43.23) (30.78) (146.24) (139.80) Observations 2955 2955 2955 2955 5399 R-squared 0.07 0.24 0.24 0.13 0.23 Note: ***, **, and * represent significance at the 1 %, 5 %, and 10 % levels, respectively, with robust standard errors in parentheses. Other tables are the same as this one, so they are omitted. F. Zhou and H. Deng Journal of Innovation & Knowledge 8 (2023) 100450 8