Digital economy and the urban–rural income gap: Impact, mechanisms, and spatial heterogeneity
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"apellidos" => "Nalmpanti" ] 1 => array:2 [ "nombre" => "Chee Yew" "apellidos" => "Wong" ] 2 => array:2 [ "nombre" => "Pejvak" "apellidos" => "Oghazi" ] ] ] ] ] "idiomaDefecto" => "en" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2444569X2400043X?idApp=UINPBA00004N" "url" => "/2444569X/0000000900000003/v2_202409040527/S2444569X2400043X/v2_202409040527/en/main.assets" ] "en" => array:18 [ "idiomaDefecto" => true "titulo" => "Digital economy and the urban–rural income gap: Impact, mechanisms, and spatial heterogeneity" "tieneTextoCompleto" => true "autores" => array:1 [ 0 => array:4 [ "autoresLista" => "He Xia, Haijing Yu, Senhao Wang, Hong Yang" "autores" => array:4 [ 0 => array:3 [ "nombre" => "He" "apellidos" => "Xia" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0001" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn1" ] ] ] 1 => array:3 [ "nombre" => "Haijing" "apellidos" => "Yu" "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">b</span>" "identificador" => "aff0002" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">1</span>" "identificador" => "fn1" ] ] ] 2 => array:3 [ "nombre" => "Senhao" "apellidos" => "Wang" "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0001" ] ] ] 3 => array:4 [ "nombre" => "Hong" "apellidos" => "Yang" "email" => array:1 [ 0 => "xjauyh@xjau.edu.cn" ] "referencia" => array:2 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">a</span>" "identificador" => "aff0001" ] 1 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0001" ] ] ] ] "afiliaciones" => array:2 [ 0 => array:3 [ "entidad" => "College of Economics and Management, Xinjiang Agricultural University, Urumqi, China" "etiqueta" => "a" "identificador" => "aff0001" ] 1 => array:3 [ "entidad" => "Business School, Xiangtan University, Xiangtan, Hunan, China" "etiqueta" => "b" "identificador" => "aff0002" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0001" "etiqueta" => "⁎" "correspondencia" => "Corresponding author at: 311 Nongda East Road, Shayibak District, Urumqi City, Xinjiang Uygur Autonomous Region, China." ] ] ] ] "resumenGrafico" => array:2 [ "original" => 0 "multimedia" => array:8 [ "identificador" => "fig0001" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 589 "Ancho" => 1250 "Tamanyo" => 38199 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0002" "detalle" => "Fig " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spara001" class="elsevierStyleSimplePara elsevierViewall">Diagram of the chain mediating effect model.</p>" ] ] ] "textoCompleto" => "<span class="elsevierStyleSections"><span id="sec0001" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0004">Introduction</span><p id="para0002" class="elsevierStylePara elsevierViewall">With changes in major social contradictions, the issue of imbalanced and insufficient development has become the main factor restricting people's growing need for a better life. Among these issues, the urban–rural income gap remains severe (<a class="elsevierStyleCrossRef" href="#bib0070">Zhu & Liu, 2022</a>). Although the income gap between urban and rural areas in China narrowed over the past decade, it remains much higher than the international average (<a class="elsevierStyleCrossRef" href="#bib0064">Yuan et al., 2020</a>; <a class="elsevierStyleCrossRef" href="#bib0048">Sicular et al., 2007</a>). In 2023, the per capita disposable income of urban households in China was 51,800 yuan, whereas the per capita disposable income of rural residents was only 21,700 yuan, much lower than that of urban residents. Narrowing the urban–rural income gap and promoting coordinated development between urban and rural areas as well as developed and underdeveloped areas can promote social stability, reduce social inequality, improve people's living standards, and promote the economic prosperity of the entire country (<a class="elsevierStyleCrossRef" href="#bib0054">Wen et al., 2014</a>).</p><p id="para0003" class="elsevierStylePara elsevierViewall">Simultaneously, China's policies to promote digital transformation and digital economy development are continuously deepening. The nation's digital economy development has had a strong influence on a global scale (<a class="elsevierStyleCrossRef" href="#bib0029">Li et al., 2020</a>; <a class="elsevierStyleCrossRef" href="#bib0040">Murthy et al., 2021</a>), becoming a new driving force for promoting urban–rural development (<a class="elsevierStyleCrossRef" href="#bib0071">Zhao et al., 2023</a>). However, this has established development gaps between urban and rural areas due to technological iterations and differing growth effects, affecting urban and rural residents’ income distribution patterns. The digital divide can result in income disparities between regions and groups (<a class="elsevierStyleCrossRef" href="#bib0046">Qiu et al., 2021</a>; <a class="elsevierStyleCrossRef" href="#bib0065">Yin et al., 2021</a>), and controversy exists concerning the impact of digital technology on rural development (<a class="elsevierStyleCrossRef" href="#bib0010">Erdiaw–Kwasie & Alam, 2016</a>), raising the question of whether digital economy development bridges or expands urban–rural income inequality (<a class="elsevierStyleCrossRef" href="#bib0044">Peng & Dan, 2023</a>). Therefore, it is essential to investigate whether digital economy development can effectively narrow the urban–rural income gap and its impact path. This study has important practical significance for developing strategically targeted policies to narrow the income gap between urban and rural residents and advance common prosperity.</p><p id="para0004" class="elsevierStylePara elsevierViewall">Currently, research on the impact of digital economy on the urban–rural income gap has been increasing and enriching the literature. Although scholars have reached a consensus on the perspective that digital economy development can raise incomes, differences remain regarding the relationship between digital economy development and the urban–rural income gap, which can be summarized into three categories: the digital economy expands and shrinks urban–rural income; the relationship between the two is nonlinear, exhibiting a U-shaped relationship of first shrinking and then expanding; or the relationship has an inverted U-shaped trend of first increasing and then decreasing (<a class="elsevierStyleCrossRef" href="#bib0030">Li & Li, 2022a</a>; <a class="elsevierStyleCrossRef" href="#bib0066">Yu, 2022</a>; <a class="elsevierStyleCrossRef" href="#bib0011">Fan et al., 2022</a>; <a class="elsevierStyleCrossRef" href="#bib0049">Si & Li, 2023</a>). This controversy is attributed to incomplete research on the mechanisms of impact of digital economy development on the urban–rural income gap. A majority of studies have neglected to consider regional heterogeneity (<a class="elsevierStyleCrossRef" href="#bib0055">Wang & Xiao, 2021</a>). Furthermore, traditional regression analysis methods generally assume that individuals are independent and identically distributed, when, in reality, digital economy development and urban–rural income gaps have spatial autocorrelation. Currently, research on the impact of digital economy on urban–rural income inequality is limited.</p><p id="para0005" class="elsevierStylePara elsevierViewall">To compensate for this deficiency, the main contributions of this study are as follows. First, in contrast to previous studies, we adopt a new variable coefficient spatial Durbin model (MGWPR–SDM) proposed by <a class="elsevierStyleCrossRef" href="#bib0067">Yu et al. (2021)</a>, which relaxes the traditional implicit assumptions of independent homogeneous distribution and all regions being equal and can simultaneously consider spatial correlation and heterogeneity to reflect the actual impact of digital economy on the urban–rural income gap more accurately. Second, previous research has largely ignored the relationships between mediating variables and does not consider spatial heterogeneity, which can reduce the credibility of the conclusions obtained by producing biased and inconsistent results. This study broadens the scope of policy recommendations by including these considerations. We embed the MGWPR–SDM into a chain–mediating effect model to examine the impact mechanisms and spatial heterogeneity of digital economy development on the urban–rural income gap, effectively addressing this issue. Third, this study responds to the current academic debates and enriches the research on the impact of digital economy development on the urban–rural income gap, providing a theoretical reference for relevant government departments to develop strategically targeted policies to narrow the urban–rural income gap that consider local conditions. Therefore, this study has academic and practical significance.</p><p id="para0006" class="elsevierStylePara elsevierViewall">The remainder of the paper is structured as follows. The second part presents the theoretical mechanism and research hypotheses based on the review of relevant literature, and the controversies and shortcomings of relevant research viewpoints are summarized. The third part is model construction, which mainly introduces the process of embedding the MGWPR-SDM into the chain–mediated effect model. The fourth part is about variable selection and data description, which introduces the selection of variables and data description. Among them, we calculate the level of digital economy development in various regions by constructing a digital economy development index system. The fifth part is empirical result analysis, which displays spatial heterogeneity results through maps. The last part is the conclusion and policy implications.</p></span><span id="sec0002" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0005">Theoretical analysis and research hypotheses</span><p id="para0007" class="elsevierStylePara elsevierViewall">The digital economy is a new form of the economy that emerged with the development and application of digital technology. In 1996, Don Tapscott, the father of the digital economy, first proposed the concept of “digital economy” (<a class="elsevierStyleCrossRef" href="#bib0052">Tapscott, 1996</a>). The official interpretation of the digital economy in China can be traced back to 2016 when the G20 Hangzhou Summit passed the G20 Digital Economy Development and Cooperation Initiative, providing a clear definition of the digital economy. The digital economy refers to the use of digital knowledge and information as key production factors, a series of economic activities that use modern information networks as an important carrier, and the effective use of information and communication technology as an important driving force for efficiency improvement. Economic structure optimization has been widely recognized by various sectors of society.</p><span id="sec0003" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0006">Impact of digital economy on the urban–rural income gap</span><p id="para0008" class="elsevierStylePara elsevierViewall">Currently, no consensus has emerged in the academic community regarding the impact of digital economy development on the urban–rural income gap. As briefly described above, the research conclusions can be roughly divided into three categories.<ul class="elsevierStyleList" id="celist0001"><li class="elsevierStyleListItem" id="celistitem0001"><span class="elsevierStyleLabel">(1)</span><p id="para0009" class="elsevierStylePara elsevierViewall">Research on the expanding effect of the digital economy on the urban–rural income gap. <a class="elsevierStyleCrossRef" href="#bib0012">Furuholt and Kristiansen (2007)</a> emphasized that the digital economy, which is characterized by the internet, big data, artificial intelligence, and other forms, may result in imbalanced development between urban and rural areas, resulting in a digital divide (<a class="elsevierStyleCrossRef" href="#bib0031">Lorence et al., 2006</a>), which is unfavorable for low–income rural populations (<a class="elsevierStyleCrossRef" href="#bib0015">Gorski & Clark, 2002</a>). <a class="elsevierStyleCrossRef" href="#bib0053">Tan et al. (2017)</a> argued that a gap is evident between urban and rural residents in digital economy acceptance and application. Rural residents have limited resources, education, and business environments than urban residents (<a class="elsevierStyleCrossRef" href="#bib0019">He & Xu, 2019</a>). Furthermore, rural residents cannot identify and use information effectively or fully enjoy the advantages of internet diffusion (<a class="elsevierStyleCrossRef" href="#bib0032">Lv, 2021</a>). In contrast, urban residents are in a position of information advantage and can more conveniently access and use digital information through the internet. Furthermore, urban residents’ digital awareness and capabilities are continuously enhanced, which ultimately widens urban–rural wealth disparities, particularly income (<a class="elsevierStyleCrossRef" href="#bib0033">Li & Ke, 2021</a>). This viewpoint highlights the possibility that the digital economy may widen the income gap between urban and rural areas, reminding policymakers to pay attention to the potential inequality issues. However, this viewpoint may be too pessimistic and may overlook some of the positive impacts of the digital economy.</p></li><li class="elsevierStyleListItem" id="celistitem0002"><span class="elsevierStyleLabel">(2)</span><p id="para0010" class="elsevierStylePara elsevierViewall">Research on the reduction effect of digital economy on the urban–rural income gap. This perspective argues that digital economy development contributes to narrowing the urban–rural income gap. Some scholars have demonstrated that the digital economy can increase farmers’ income and narrow the urban–rural income gap (<a class="elsevierStyleCrossRef" href="#bib0004">Bhavnani et al., 2008</a>; <a class="elsevierStyleCrossRef" href="#bib0001">Aker & Dial, 2011</a>). <a class="elsevierStyleCrossRef" href="#bib0002">Acemoglu and Restrepo (2016)</a> proposed that digital technologies such as the internet break down spatiotemporal barriers, providing laborers with more employment choices and enhancing market resource allocation efficiency. <a class="elsevierStyleCrossRef" href="#bib0034">Liu et al. (2021)</a> argued that digital economy development has facilitated significant achievements worldwide, improving impoverished population's living conditions. Moreover, the digital economy has been found to directly reduce the urban–rural income gap (<a class="elsevierStyleCrossRef" href="#bib0035">Liu, 2021</a>). Digital economy development can leverage inclusiveness and resource sharing through deep integration of production, life, and ecology (<a class="elsevierStyleCrossRef" href="#bib0013">Fu, 2020</a>). The digital economy functions as an endogenous driving force that overcomes geographical constraints, eliminates information barriers, expands employment opportunities, and advances growth in underdeveloped regions. It also drives optimized factor allocation, promotes the division of labor and collaboration, improves labor productivity, enhances economies of scale, and facilitates coordinated regional development (<a class="elsevierStyleCrossRef" href="#bib0009">Duan et al., 2020</a>; <a class="elsevierStyleCrossRef" href="#bib0020">Hu et al., 2017</a>; <a class="elsevierStyleCrossRef" href="#bib0050">Song, 2012</a>). This viewpoint emphasizes that the digital economy may provide opportunities for narrowing the urban–rural income gap and a possible way to achieve income balance. It can motivate governments and enterprises to increase their investment in related fields and promote the coordinated development of urban and rural economies. However, this viewpoint may be too idealistic, ignoring the new inequality issues that the development of the digital economy may bring; it may also overlook the complex and diverse impact of the digital economy on different regions and populations, which may not directly lead to the narrowing of the urban–rural income gap.</p></li><li class="elsevierStyleListItem" id="celistitem0003"><span class="elsevierStyleLabel">(3)</span><p id="para0011" class="elsevierStylePara elsevierViewall">Research on the nonlinear effect of the digital economy on the urban–rural income gap. A nonlinear relationship exists between digital economy development and the urban–rural income gap. <a class="elsevierStyleCrossRef" href="#bib0003">Ashraf et al.( 2021)</a> argued that as information and communications technology (ICT) is promoted and adopted, income inequality decreases over time. <a class="elsevierStyleCrossRef" href="#bib0024">Jiang et al. (2022)</a> revealed a U-shaped relationship between the digital economy and the urban–rural income gap, with the gap narrowing in early stages and widening in later stages. <a class="elsevierStyleCrossRef" href="#bib0072">Zheng and Li (2022)</a> proposed that the impact of digital economy on the urban–rural income gap can have simultaneous expanding and narrowing effects, and the overall impact depends on the relative magnitude of these effects. Considering that the digital economy and the urban–rural digital divide are in a continuous dynamic development process, their relationship is likely to exhibit time–varying characteristics, exhibiting a complex nonlinear relationship. <a class="elsevierStyleCrossRef" href="#bib0055">Wang and Xiao (2021)</a> found that the development of the digital economy has a U-shaped relationship with the urban–rural income gap, initially narrowing and then widening. A similar perspective was presented by <a class="elsevierStyleCrossRef" href="#bib0006">Chen and Wu (2021)</a>, who proposed that while the initial stages of digital economy development can promote urbanization and narrow the urban–rural income gap, further development and the resulting digital divide negatively impact rural surplus labor migrating to cities for employment, widening the urban–rural income gap. Conversely, <a class="elsevierStyleCrossRef" href="#bib0036">Li and Li (2022a)</a>, and <a class="elsevierStyleCrossRef" href="#bib0007">Cheng and Zhang (2019)</a> opined that the impact of digital economy on the urban–rural income gap shows an overall inverted U-shaped trend, with an initial increase and subsequent decrease. They proposed that in the initial stage of digital economy development, due to the disparity in human capital and the existence of the digital divide between urban and rural areas, rural residents cannot fully utilize digital economy resources to increase their income compared to urban residents, resulting in an expanding urban–rural income gap. However, later on, due to the law of diminishing marginal utility, the marginal impact of urban digital economy development on income will gradually be surpassed by rural “latecomers”, leading to a narrowing of the urban–rural income gap. Supporting this view, <a class="elsevierStyleCrossRef" href="#bib0030">Li and Li (2022b)</a> argued that the impact of digital economy development on the urban–rural income gap follows an inverted U-shaped pattern, and a threshold effect exists in the relationship between digital economy development and the urban–rural income gap. This view is closer to reality, recognizing that the impact of digital economy on urban and rural incomes is complex and varied, not single–linear, and may show different stages of changing trends. However, grasping the specific pattern of nonlinear relationships accurately is difficult, and more empirical research and data support are required.</p></li></ul></p><p id="para0012" class="elsevierStylePara elsevierViewall">The divergent findings of the aforementioned studies can be attributed to three main reasons. First, the impact of digital economy development on the urban–rural income gap exhibits spatial heterogeneity, while existing research models often assume a mean regression, assuming that the impact coefficients are the same for all regions. For example, <a class="elsevierStyleCrossRef" href="#bib0055">Wang and Xiao (2021)</a> studied 30 provinces in China from 2013 to 2019 and found that the impact of digital economy development on narrowing the urban–rural income gap was mainly concentrated in the central and western regions, with a slight widening effect in the eastern region and unclear effects in the northeastern region. Second, traditional econometric models assume that research subjects are independently and identically distributed, but the levels of digital economy development and the urban–rural income gap in different regions may exhibit spatial spillover effects, violating this assumption. The development of the digital economy and the urban–rural income gap in each province, city, and autonomous region is not only influenced by local factors but also interacts with the development of the digital economy or the urban–rural income gap in neighboring regions. Several studies have demonstrated spatial autocorrelation (<a class="elsevierStyleCrossRef" href="#bib0049">Si & Li, 2023</a>; <a class="elsevierStyleCrossRef" href="#bib0056">Wei & Chen, 2020</a>), indicating the need to use spatial econometric models instead of traditional regression models. Third, most studies only consider the direct impact of digital economy development on the urban–rural income gap, with less consideration of its indirect effects; even fewer studies involve the spatial heterogeneity of these indirect effects. If the directions of the direct and indirect effects are opposite, the overall effect after aggregation is uncertain.</p><p id="para0013" class="elsevierStylePara elsevierViewall">In summary, a comprehensive understanding of the impact of digital economy development on the urban–rural income gap requires simultaneous consideration of spatial correlation, spatial heterogeneity, and direct and indirect effects. However, limited studies have taken this approach. Therefore, this study proposes the following hypothesis:<ul class="elsevierStyleList" id="celist0002"><li class="elsevierStyleListItem" id="celistitem0004"><p id="para0014" class="elsevierStylePara elsevierViewall">H1. The influence of the digital economy on the income gap between urban and rural areas is spatially heterogeneous, and the influence in different regions has expanding, narrowing, or uncertain relationships.</p></li></ul></p></span><span id="sec0004" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0007">Impact mechanisms of the digital economy on the urban–rural income gap</span><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0008">Digital economy, human capital, and the urban–rural income gap</span><p id="para0015" class="elsevierStylePara elsevierViewall">Contemporary rapid digital economy development, represented by the mobile internet, and the role of internet and ICT development in correcting resource misallocation, expanding rural residents’ access to information channels, and enhancing rural residents’ human capital has been confirmed by a growing number of scholars. Knowledge and information are non–competitive, and the establishment and improvement of digital infrastructure exposes the rural population to high–quality online education. The rural labor force can access a wealth of information and knowledge through the internet, which can improve the breadth and depth of the rural knowledge base, enhancing rural residents’ human capital (<a class="elsevierStyleCrossRef" href="#bib0070">Zhu & Liu, 2022</a>; <a class="elsevierStyleCrossRef" href="#bib0041">Mi & Qu, 2022</a>) and improving rural residents’ ability to increase incomes, effectively bridging the urban–rural income gap (<a class="elsevierStyleCrossRef" href="#bib0030">Li & Li, 2022a</a>; <a class="elsevierStyleCrossRef" href="#bib0025">Jin & Deng, 2022</a>; <a class="elsevierStyleCrossRef" href="#bib0061">Xu & Feng, 2022</a>). Conversely, some scholars have argued that the increasing coverage and depth of the digital economy generates new employment opportunities that favor knowledge-based talent, placing higher demand on human capital. Non-agricultural employees may encounter structural unemployment risks, further widening the urban–rural income gap (<a class="elsevierStyleCrossRef" href="#bib0011">Fan et al., 2022</a>).</p><p id="para0016" class="elsevierStylePara elsevierViewall">Additionally, studies have shown that primary rural human capital is mostly concentrated in western provinces, whereas intermediate rural human capital is mostly concentrated in provinces with relatively developed modern agriculture. Areas with high concentrations of advanced rural human capital have experienced a dynamic shift from the Northeast region to the Yangtze River Delta region (<a class="elsevierStyleCrossRef" href="#bib0068">Yao & Deng, 2020</a>). <a class="elsevierStyleCrossRef" href="#bib0014">Fan and Cui (2018)</a> showed that human capital has regional heterogeneity in its impact on the urban–rural income gap; an increase in the proportion of high–skilled human capital widens the urban–rural income gap in the eastern and central regions, while effectively narrowing the urban–rural income gap in the western regions.</p><p id="para0017" class="elsevierStylePara elsevierViewall">In summary, although a debate remains, the internet and other digital technologies are important influencing factors in improving human capital, which is a key factor of the urban–rural income gap (<a class="elsevierStyleCrossRef" href="#bib0051">Song & Gao, 2022</a>). Rural human capital has a significant moderating role in the impact of digital economy development on the urban−rural income gap (<a class="elsevierStyleCrossRef" href="#bib0062">Xu et al., 2023</a>). Furthermore, <a class="elsevierStyleCrossRef" href="#bib0037">Li and Zhang (2024)</a> pointed out that the upgrading of human capital structure can narrow the income gap between urban and rural areas. Therefore, ample evidence has suggested that the digital economy affects the urban–rural income gap by influencing human capital development. Considering this mediating effect, we propose the following hypothesis:<ul class="elsevierStyleList" id="celist0003"><li class="elsevierStyleListItem" id="celistitem0005"><p id="para0018" class="elsevierStylePara elsevierViewall">H2. Human capital development mediates the impact of digital economy on the urban–rural income gap, affecting the income gap through human capital and exhibiting spatial heterogeneity.</p></li></ul></p></span><span id="sec0006" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0009">Digital economy, industrial structure, and the urban–rural income gap</span><p id="para0019" class="elsevierStylePara elsevierViewall">From the impact mechanism perspective, the digital economy gives rise to new economic models, production factors, and technological support for industrial upgrades (<a class="elsevierStyleCrossRef" href="#bib0026">Jing & Sun, 2019</a>). It reduces information asymmetry and transaction costs (<a class="elsevierStyleCrossRef" href="#bib0038">Liu et al., 2023</a>), strengthens industrial links, promotes industrial integration, and establishes a cooperative and mutually beneficial industrial ecosystem (<a class="elsevierStyleCrossRef" href="#bib0021">Hui & Yang, 2022</a>). The digital economy can effectively optimize the tertiary industries in the national economy and coordination between industries<span class="elsevierStyleCrossOut">,</span> significantly promoting industrial structure rationalization (<a class="elsevierStyleCrossRef" href="#bib0039">Liu & Chen, 2021</a>), while also exhibiting obvious regional heterogeneity (<a class="elsevierStyleCrossRef" href="#bib0016">Gao et al., 2023</a>).</p><p id="para0020" class="elsevierStylePara elsevierViewall">The digital economy development has significantly promoted the upgrading of industrial structure (<a class="elsevierStyleCrossRef" href="#bib0039">Liu & Chen, 2021</a>), and exhibits regional heterogeneity, with a more pronounced effect on the upgrading of industrial structure in the central and western regions (<a class="elsevierStyleCrossRef" href="#bib0057">Wang, 2023</a>). An optimized industrial structure can narrow the urban–rural income gap by absorbing the rural labor force (<a class="elsevierStyleCrossRef" href="#bib0073">Zhou & Wang, 2013</a>). However, some scholars argue conversely, suggesting that because of enormous scientific research and innovation activities in cities, digital technology continues to penetrate secondary and modern service industries, greatly improving their production efficiency. For cities with the main industrial structure of the second and third industries, the urban income level will also increase, which may lead to further expansion of the urban–rural income gap (<a class="elsevierStyleCrossRef" href="#bib0066">Yu, 2022</a>). Some scholars also argue that the impact of industrial structure upgrading on the urban–rural income gap exhibits a U-shaped characteristic, but in the eastern region, there is a tendency to widen the urban–rural income gap, while in the western region, it is beneficial to narrow the urban–rural income gap, while in the central region, it is generally located near the transition point (<a class="elsevierStyleCrossRef" href="#bib0063">Xu & Liu, 2015</a>).</p><p id="para0021" class="elsevierStylePara elsevierViewall">Previous research has widely recognized that the digital economy can affect the urban–rural income gap by altering the industrial structure; therefore, this study proposes the following hypothesis concerning this mediating effect:<ul class="elsevierStyleList" id="celist0004"><li class="elsevierStyleListItem" id="celistitem0006"><p id="para0022" class="elsevierStylePara elsevierViewall">H3. The industrial structure mediates the impact of digital economy on the urban–rural income gap; however, the spatial impact of the digital economy on the income gap is uncertain.</p></li></ul></p></span></span></span><span id="sec0007" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0010">Model construction</span><p id="para0023" class="elsevierStylePara elsevierViewall">The baseline and mediation effect models in this study are based on the Mixed Geographically Weighted Panel Regression model (MGWPR–SDM) proposed by <a class="elsevierStyleCrossRef" href="#bib0067">Yu et al. (2021)</a>. This model combines the advantages of the semiparametric spatially varying coefficient Geographically Weighted Panel Regression model (MGWPR) and the Spatial Durbin Model (SDM). Whereas, the MGWPR–SDM is a generalized model that can be degenerated into various spatially varying coefficient panel models, such as Spatial Autoregressive (SAR) and Spatial Error models (SEM), to meet different research needs. However, this model is an improvement over traditional regression and spatial econometric models. Classic spatial econometric models, such as the SDM, only consider the spatial correlation of variables and add spatial lag terms of dependent and independent variables as explanatory variables. However, they believe that the coefficients between each region are equal, ignoring spatial heterogeneity. Geographically Weighted Regression (GWR) models focus on spatial heterogeneity and neglect spatial stationarity and correlations. The MGWPR–SDM used in this study combines the advantages of the classical SDM and GWR class models while considering spatial stationarity, spatial correlation, and spatial heterogeneity (<a class="elsevierStyleCrossRef" href="#bib0069">Yu & Zhu, 2023</a>).</p><span id="sec0008" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0011">Spatial weight matrix</span><p id="para0024" class="elsevierStylePara elsevierViewall">The spatial weight matrix is not only the biggest difference between spatial econometric models and traditional mean reversion models but also a key factor in geographically weighted regression and its extension models. In both models, spatial weights are used to represent the interdependence between individuals or regions, which requires a uniform spatial weight setting.</p><p id="para0025" class="elsevierStylePara elsevierViewall">Unlike previous spatial weight settings, this study draws on the concept of gravitational potential to construct an asymmetric spatial weight matrix that incorporates geographical and economic distances. Potential gravitational energy (F=Gmi·mjdij<span class="elsevierStyleItalic">)</span> is used to express the mutual gravitational force between two objects, where F is the value of the potential gravitational energy between the two objects, G is a gravitational constant value of 6.67×10−11Nm2/kg2, mi and mj denote the masses of objects i and j, respectively, and dij is the distance between the central mass of the two objects. If the numerator is replaced by an economic total of the two regions, it combines economic and geographical distance. Using this approach to construct a spatial weight matrix is more accurate than only using economic or geographical distances. However, the spatial weight matrix is symmetric, and the mutual influence between the two regions is the same, which obviously does not conform to reality. In reality, the impact of regions with large economic output on regions with small economic output is greater than that of regions with small economic output on regions with large economic output. Therefore, we reference <a class="elsevierStyleCrossRef" href="#bib0043">Newey and Powell (1987)</a>, constructing asymmetric least-squares estimators, rewriting the potential gravitational energy formula, and establishing an asymmetric spatial weight. The specific forms are as follows:<elsevierMultimedia ident="eqn0001"></elsevierMultimedia>where wij is the influence of spatial unit j on i at time t in the <span class="elsevierStyleItalic">i th</span> row of the spatial weight matrix. To maintain generality, the constant G in the original potential gravitational energy formula is set to 1. |ω−I(mi‾>mj‾)| can be called the asymmetric coefficient, whereω=max(mi‾,mj‾)/(mi‾+mj‾), and mi‾ and mj‾ respectively represent the means of mi and mj over the entire time interval. I(mi‾>mj‾) for an indicative function, when the index condition is met, the value of the function is 1, otherwise it is 0.</p><p id="para0026" class="elsevierStylePara elsevierViewall">Verifying the superiority of the asymmetric potential gravitational energy spatial weight proposed in this study requires comparison with traditional spatial weights constructed based on the reciprocal of economic distance (1/|mi−mj|) and geographical distance (1/dij). <a class="elsevierStyleCrossRef" href="#tbl0001">Table 1</a> estimates the values of the Akaike information criterion (AIC) and Bayesian information criterion (BIC) corresponding to the three weights based on the direct effects model in <a class="elsevierStyleCrossRef" href="#eqn0002">Eq. (2)</a>. The results reveal that the AIC and BIC values of the asymmetric potential gravitational energy spatial weight are the smallest, indicating that they are superior to the traditional spatial weight matrix constructed based on geographic and economic distances.</p><elsevierMultimedia ident="tbl0001"></elsevierMultimedia></span><span id="sec0009" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0012">Direct effects model</span><p id="para0028" class="elsevierStylePara elsevierViewall">According to the MGWPR–SDM, we construct the following global smooth SDM:<elsevierMultimedia ident="eqn0002"></elsevierMultimedia>where Gapit represents the urban–rural income gap, <span class="elsevierStyleItalic">WGap<span class="elsevierStyleInf">it</span></span> represents the spatial lag terms of the dependent variable. Digiit represents digital economy development, Xk represents the <span class="elsevierStyleItalic">k-th</span> mediating variable, and Cr represents the <span class="elsevierStyleItalic">r-th</span> control variable. The corresponding coefficients for mediating and control variables are αk and λr,respectively;WXq represents the spatial lag terms of the explanatory variables, including core, mediating, and control variables; therefore, <span class="elsevierStyleItalic">q</span><span class="elsevierStyleItalic">=</span><span class="elsevierStyleItalic">k</span><span class="elsevierStyleItalic">+</span><span class="elsevierStyleItalic">r</span><span class="elsevierStyleItalic">+</span><span class="elsevierStyleItalic">1</span>. θq represents the coefficient of the spatial lag term of the <span class="elsevierStyleItalic">q-th</span> explanatory variable, ui represents provincial fixed effects, νt represents year fixed effects, and εit represents the randomized perturbation term. All coefficients in the above formula can be fixed or variable and require testing to determine the specific form of the MGWPR–SDM.</p></span><span id="sec0010" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0013">Mediating effect model</span><p id="para0029" class="elsevierStylePara elsevierViewall">Previous research has demonstrated that human capital exerts a decisive influence on upgrading industrial structure (<a class="elsevierStyleCrossRef" href="#bib0047">Romalis, 2004</a>; <a class="elsevierStyleCrossRef" href="#bib0023">Hausmann et al., 2007</a>). <a class="elsevierStyleCrossRef" href="#bib0008">Chen and Yang (2014)</a> determined that human capital significantly contributes to upgrading China's industrial structure. Additionally, regional differences are evident concerning the impact of human capital heterogeneity on industrial structure upgrading. High skilled human capital significantly promotes the upgrading of industrial structure in the eastern region, while its promotion effect is not significant in the western region (<a class="elsevierStyleCrossRef" href="#bib0074">Zhang et al., 2011</a>). To further analyze the impact mechanism of the digital economy on the urban–rural income gap, we reference <a class="elsevierStyleCrossRef" href="#bib0069">Yu and Zhu (2023)</a> constructing a chain-mediating effect model.<elsevierMultimedia ident="eqn0003"></elsevierMultimedia><elsevierMultimedia ident="eqn0004"></elsevierMultimedia>where Hcit and Struit represent the mediating variables, Hcit quantifies human capital, and Struit represents the industrial structure. <span class="elsevierStyleItalic">C</span> is the matrix of control variables, and its corresponding coefficient matrix is λ; WX in <a class="elsevierStyleCrossRef" href="#eqn0003">Eqs. (3)</a> and (<a class="elsevierStyleCrossRef" href="#eqn0004">4</a>) denote the matrix of spatially lagged variables for explanatory variables other than the spatially lagged terms of the explanatory variables, and θ represents the corresponding coefficient matrix; ui represents provincial fixed effects; νt represents year fixed effects; andεit represents the randomized perturbation term.</p><p id="para0030" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0001">Fig. 1</a> shows that the indirect impact of digital economy development (<span class="elsevierStyleItalic">Digi</span>) on the urban–rural income gap (<span class="elsevierStyleItalic">Gap</span>) has three paths. (1) <span class="elsevierStyleItalic">Digi</span> has an impact on the <span class="elsevierStyleItalic">Gap</span> by affecting human capital (<span class="elsevierStyleItalic">Hc</span>), with an effect size of β1*α2. (2) <span class="elsevierStyleItalic">Digi</span> affects the <span class="elsevierStyleItalic">Gap</span> by influencing the industrial structure (<span class="elsevierStyleItalic">Stru</span>), with an effect size of γ1*α3. (3) <span class="elsevierStyleItalic">Digi</span> has an impact on the <span class="elsevierStyleItalic">Stru</span> by influencing <span class="elsevierStyleItalic">Hc</span>, which affects the <span class="elsevierStyleItalic">Gap</span>, with a magnitude of β1*γ2*α3.</p><elsevierMultimedia ident="fig0001"></elsevierMultimedia></span></span><span id="sec0011" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0014">Variable selection and data description</span><span id="sec0012" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0015">Variable selection</span><span id="sec0013" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0016">Explained variable</span><p id="para0031" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Gap</span> is the explained variable in this study, referencing <a class="elsevierStyleCrossRef" href="#bib0055">Wang and Xiao (2021)</a>, who adopted the per capita disposable income ratio of urban and rural residents as an alternative indicator of the income gap between urban and rural residents.</p></span><span id="sec0014" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0017">Core explanatory variable</span><p id="para0032" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Digi</span> is the core explanatory variable. Currently, no common authoritative indicator has been developed for measuring digital economy development. The measurement of the digital economy development index in the existing literature generally includes the “Internet plus” digital economy index designed by the Tencent Research Institute (<a class="elsevierStyleCrossRef" href="#bib0027">Jiang & Sun, 2020</a>) and self–constructed indicators. Although the “Internet plus” digital economy indicators cover a relatively comprehensive range, due to the dynamic adjustment of subdivision indicators and weights every year, historical data is incomparable and can only be used as cross–sectional data, which cannot be applied to panel data. Referring to <a class="elsevierStyleCrossRef" href="#bib0069">Yu and Zhu (2023)</a>, <a class="elsevierStyleCrossRef" href="#bib0017">Guo et al. (2020)</a>, <a class="elsevierStyleCrossRef" href="#bib0045">Pan et al. (2021)</a>, and <a class="elsevierStyleCrossRef" href="#bib0058">Wang et al. (2023)</a>, and considering the data availability, this study constructs a China Provincial digital economy index that includes digital infrastructure, digital industry development, and digital financial services. The model includes 3 primary indicators, 7 secondary indicators, and 53 tertiary indicators, as shown in <a class="elsevierStyleCrossRef" href="#tbl0002">Table 2</a>. Drawing on <a class="elsevierStyleCrossRef" href="#bib0059">Wang et al. (2021)</a>, the entropy method is used to determine the weights of the indicators, and a comprehensive score is calculated using the weighting function method to indicate the level of digital economy development.</p><elsevierMultimedia ident="tbl0002"></elsevierMultimedia></span><span id="sec0015" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0018">Mediating variables</span><p id="para9001" class="elsevierStylePara elsevierViewall"><ul class="elsevierStyleList" id="celist0005"><li class="elsevierStyleListItem" id="celistitem0007"><span class="elsevierStyleLabel">(1)</span><p id="para0033" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Hc</span>: We measure per capita education years, referencing <a class="elsevierStyleCrossRef" href="#bib0030">Li and Li (2022a)</a>. <a class="elsevierStyleCrossRef" href="#bib0022">Hu and Lu (2019)</a> suggested that farmers can enhance their learning capabilities through internet information technology, which is beneficial for improving overall <span class="elsevierStyleItalic">Hc</span> and increasing rural incomes.</p></li><li class="elsevierStyleListItem" id="celistitem0008"><span class="elsevierStyleLabel">(2)</span><p id="para0034" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleItalic">Stru</span>: Referencing <a class="elsevierStyleCrossRef" href="#bib0055">Wang and Xiao (2021)</a>, we use the ratio of the output value of the tertiary industry to that of the secondary industry to measure <span class="elsevierStyleItalic">Stru</span>. The service-oriented economic structure driven by information technology is an essential feature of <span class="elsevierStyleItalic">Stru</span> upgrading as the growth rate of the tertiary industry is faster than that of the secondary industry, which is a typical fact in the process of China's service-oriented economic transition (<a class="elsevierStyleCrossRef" href="#bib0060">Wu, 2005</a>). As a measure of industrial structure upgrading, the ratio of the output value of the tertiary industry to that of the secondary industry clearly reflects the service–oriented tendencies of the economic structure. If the ratio is upward, the economy is advancing in a service–oriented direction, and the industrial structure is upgraded (<a class="elsevierStyleCrossRef" href="#bib0018">Gan et al., 2011</a>). <a class="elsevierStyleCrossRef" href="#bib0028">Ji (2023)</a> showed that although the development of an advanced industrial structure will not directly have a significant widening effect on the urban–rural income gap, it will have a certain reverse regulatory effect on the process of digital economy development, affecting the urban–rural income gap.</p></li></ul></p></span><span id="sec0016" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0019">Control variables</span><p id="para0035" class="elsevierStylePara elsevierViewall">(1) We quantify economic development (<span class="elsevierStyleItalic">Pgdp</span>) using the per capita GDP of each region. (2) The study measures urbanization level (<span class="elsevierStyleItalic">Urban</span>) as the ratio of the number of permanent urban residents at the end of the year to the total population. (3) We measure the degree of openness to the outside world (<span class="elsevierStyleItalic">Open</span>) using the proportion of total imports and exports to GDP. (4) Transportation development (<span class="elsevierStyleItalic">Traf</span>) is measured as average road mileage, which is the total road mileage/land area of each province, referencing <a class="elsevierStyleCrossRef" href="#bib0059">Wang et al. (2021)</a>. (5) The study quantifies agricultural mechanization (<span class="elsevierStyleItalic">Agri_rate</span>) as the ratio of the total machinery input in each region to the number of rural residents. (6) We measure fiscal support for agriculture (<span class="elsevierStyleItalic">Sub_rate</span>) as the proportion of fiscal expenditure on agriculture, forestry, and water to the total fiscal expenditure at the corresponding level.</p></span></span><span id="sec0017" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0020">Data description and explanation</span><p id="para0036" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0003">Table 3</a> presents the statistical indicators, including minimum value, interquartile, median, mean, interquartile, maximum value, and standard deviation of variables. The minimum value of <span class="elsevierStyleItalic">Gap</span> is 1.840 and the maximum value is 3.800, indicating significant differences in <span class="elsevierStyleItalic">Gap</span> across China's regions. The minimum value <span class="elsevierStyleItalic">Digi</span> is 0.015 and the maximum value is 0.509, which also indicates a digital divide in digital economy development in various regions of China.</p><elsevierMultimedia ident="tbl0003"></elsevierMultimedia><p id="para0038" class="elsevierStylePara elsevierViewall">Data related to e–commerce have only been available since 2013 and do not include data from Hong Kong, Macao, Taiwan, and Xizang. Therefore, this study uses panel data from 30 provinces in China from 2013 to 2021 for empirical analysis. The data are sourced from the “China Population and Employment Statistical Yearbook” from 2014 to 2022, the official website of the National Bureau of Statistics, and the “Peking University Digital Inclusive Finance Index Report” published by the Peking University Digital Finance Research Center.</p></span></span><span id="sec0018" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0021">Empirical results analysis</span><span id="sec0019" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0022">Model selection</span><p id="para0039" class="elsevierStylePara elsevierViewall">We first determine whether a spatial econometric model is required. Moran's Index (Moran's I) is an important indicator for measuring spatial correlation, and uses the Moran's I statistic to test whether sample individuals are spatially independent of one another. The Moran's I test results in <a class="elsevierStyleCrossRef" href="#tbl0004">Table 4</a> that when <span class="elsevierStyleItalic">Gap, Hc</span>, and <span class="elsevierStyleItalic">Stru</span> are used as explanatory variables, the p-values corresponding to the Moran's I statistic are all 0.000, which significantly rejects the null hypothesis that the variables do not have spatial correlation at the 5 % level. All three variables have significant spatial autocorrelation; therefore, a spatial lag term should be added to the model as an explanatory variable, employing a spatial econometric model.</p><elsevierMultimedia ident="tbl0004"></elsevierMultimedia><p id="para0041" class="elsevierStylePara elsevierViewall">Second, spatial econometric models have various forms such as the spatial autoregression (SAR), the spatial error model (SEM), and the SDM. Model selection requires appropriate testing. This study uses the likelihood ratio approach to test whether the SDM degenerates into SAR or SEM, and <a class="elsevierStyleCrossRef" href="#tbl0005">Table 5</a> summarizes the results. The p-values corresponding to the chi-square statistics of Models (1)– (3) are all less than 5 %, indicating that all three models reject the original assumption that SAR and SEM models are true; therefore, the SDM with bidirectional fixed effects should be chosen.</p><elsevierMultimedia ident="tbl0005"></elsevierMultimedia></span><span id="sec0020" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0023">Variable selection: spatial varying or fixed coefficient variable</span><p id="para0042" class="elsevierStylePara elsevierViewall">As some variable coefficients may occur in Models (1)– (3), it is necessary to identify the coefficients that change with spatial variation through testing. Regarding the variable selection problem in the mixed geographic weighted regression model, <a class="elsevierStyleCrossRef" href="#bib0042">Mei et al. (2016)</a> proposed applying a bootstrap method for variable coefficient selection, which is more robust than the F-test proposed by <a class="elsevierStyleCrossRef" href="#bib0005">Brunsdon et al. (1999)</a>. <a class="elsevierStyleCrossRef" href="#tbl0006">Table 6</a> presents the test results obtained using this method.</p><elsevierMultimedia ident="tbl0006"></elsevierMultimedia><p id="para0044" class="elsevierStylePara elsevierViewall">Combining the results of variable selection in <a class="elsevierStyleCrossRef" href="#tbl0006">Table 6</a> and compared with the model in Equation (11) from <a class="elsevierStyleCrossRef" href="#bib0067">Yu et al. (2021)</a> reveals that Models (1) and (2) have some explanatory variables with insignificant bootstrap tests, and the corresponding p-values are more significant than 0.05, indicating that they are fixed coefficients and the other explanatory variables are variable coefficients. This confirms suitability for employing the MGWPR–SDM because the spatial lag term WY of the explanatory variables in Model (1) is a variable coefficient, and suitable for applying the MGWPR–SDM (1, kc, kv), while the spatial lag term WY of the explanatory variables in Model (2) is a fixed coefficient, which is suitable for applying the MGWPR–SDM (0, kc, kv). Similarly, the p-values corresponding to the explanatory variables in Model (3) are less than 0.05, indicating that all the explanatory variables, including WY, are variable coefficients; therefore, the MGWPR–SDM (0, 0, kv) is applicable.</p></span><span id="sec0021" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0024">Varying coefficient regression results</span><p id="para0045" class="elsevierStylePara elsevierViewall">As this study focuses on the core explanatory variable <span class="elsevierStyleItalic">Digi</span> and the mediating variables of <span class="elsevierStyleItalic">Hc</span> and <span class="elsevierStyleItalic">Stru</span>, we do not interpret the remaining control variables. <a class="elsevierStyleCrossRef" href="#tbl0007">Table 7</a> reveals that only <span class="elsevierStyleItalic">Stru</span> in Model (1) has a fixed coefficient that is statistically significantly positive at the 1 % level, suggesting that upgrading the <span class="elsevierStyleItalic">Stru</span> will widen the <span class="elsevierStyleItalic">Gap</span>, and its impact varies less across regions. The <span class="elsevierStyleItalic">Stru</span> of neighboring regions (<span class="elsevierStyleItalic">w_Stru</span>) in Model (1) and the core explanatory variable <span class="elsevierStyleItalic">Digi</span>, the mediating variable <span class="elsevierStyleItalic">Hc</span>, and <span class="elsevierStyleItalic">Stru</span> in the other two models are variable coefficients (<a class="elsevierStyleCrossRef" href="#tbl0007">Table 7</a>).</p><elsevierMultimedia ident="tbl0007"></elsevierMultimedia></span><span id="sec0022" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0025">Varying coefficient regression results</span><p id="para0047" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0008">Table 8</a> presents the mediation results for Model (1), revealing that the coefficients of <span class="elsevierStyleItalic">Digi</span> are both positive and negative, with a maximum value of 0.094 and a minimum value of −0.145, indicating that digital economy development widens the <span class="elsevierStyleItalic">Gap</span> in some regions, while the opposite is true for other regions. Similarly, the coefficients of <span class="elsevierStyleItalic">Hc</span> are both positive and negative, suggesting that the enhancement of human capital in some regions reduces the <span class="elsevierStyleItalic">Gap</span>, while the opposite is true for other regions.</p><elsevierMultimedia ident="tbl0008"></elsevierMultimedia><p id="para0048" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0009">Table 9</a> presents the results of mediation Model (2), revealing that the coefficients of <span class="elsevierStyleItalic">Digi</span> are both positive and negative, with a maximum value of 1.698 and a minimum value of −1.196, indicating that digital economy development in some regions enhances human capital, while the opposite is true for other regions. Furthermore, digital economy development in the neighboring regions (<span class="elsevierStyleItalic">w_Digi</span>) also exhibits both a positive and negative impact on the region's human capital, revealing that some neighboring regions can promote human capital improvement, whereas others have the opposite effect.</p><elsevierMultimedia ident="tbl0009"></elsevierMultimedia><p id="para0049" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#tbl0010">Table 10</a> presents the mediation results for Model (3), revealing that the coefficients of <span class="elsevierStyleItalic">Digi</span> are both positive and negative, with a maximum value of 0.130 and a minimum value of −0.303. This indicates that digital economy development in some regions can promote the <span class="elsevierStyleItalic">Stru</span> integration and upgrading and the opposite occurs in other regions. Furthermore, the impact of the digital economy development in neighboring regions (<span class="elsevierStyleItalic">w_Digi</span>) on <span class="elsevierStyleItalic">Stru</span> is positive and negative. <span class="elsevierStyleItalic">Digi</span> in neighboring regions (<span class="elsevierStyleItalic">w_Digi</span>) has positive and negative effects on the local <span class="elsevierStyleItalic">Stru</span>; that is, some neighboring regions promote local <span class="elsevierStyleItalic">Stru</span> upgrading, whereas others have the opposite effect. The coefficient of <span class="elsevierStyleItalic">Hc</span> is both positive and negative, with a maximum value of 0.058 and a minimum value of −0.055, indicating that enhancing human capital in some regions promotes <span class="elsevierStyleItalic">Stru</span> integration and upgrading, while the opposite is true in other regions. The influence of <span class="elsevierStyleItalic">Hc</span> in neighboring regions (<span class="elsevierStyleItalic">w_Hc</span>) on <span class="elsevierStyleItalic">Stru</span> upgrading is also both positive and negative; that is, some neighboring regions promote <span class="elsevierStyleItalic">Stru</span> upgrading, while others do not.</p><elsevierMultimedia ident="tbl0010"></elsevierMultimedia></span><span id="sec0023" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0026">Mediating effect analysis</span><p id="para0050" class="elsevierStylePara elsevierViewall">To clearly illustrate the spatial heterogeneity of the mechanism of impact of <span class="elsevierStyleItalic">Digi</span> on <span class="elsevierStyleItalic">Gap</span>, this study presents each mediating effect in the form of maps. The left sides of <a class="elsevierStyleCrossRefs" href="#fig0002">Figs. 2–4</a> present the effect values and the right sides show the p-values corresponding to the Sobel (1982) test.</p><elsevierMultimedia ident="fig0002"></elsevierMultimedia><elsevierMultimedia ident="fig0003"></elsevierMultimedia><elsevierMultimedia ident="fig0004"></elsevierMultimedia><p id="para0051" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0002">Fig. 2</a> reveals significant spatial heterogeneity in <span class="elsevierStyleItalic">Digi</span>’s effect on <span class="elsevierStyleItalic">Gap</span> through Pathway 1. The coefficients of Jilin, Inner Mongolia, Shanxi, Hubei, Guangdong, and other regions are positive at a 10 % significance level, indicating that <span class="elsevierStyleItalic">Digi</span> widens the <span class="elsevierStyleItalic">Gap</span> by enhancing <span class="elsevierStyleItalic">Hc</span>. The coefficients of Fujian, Chongqing, and other regions are negative, indicating that <span class="elsevierStyleItalic">Digi</span> can effectively narrow the <span class="elsevierStyleItalic">Gap</span> by enhancing <span class="elsevierStyleItalic">Hc</span>. Most regions in central China are positive, and northwestern regions and Heilongjiang and Liaoning are negative, but none of them are significant.</p><p id="para0052" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0003">Fig. 3</a> reveals that Pathway 2 also exhibits significant spatial heterogeneity. The coefficients in most regions of east, central–west, and northeast China are negative at a 10 % significance level, with Jiangxi and Hubei having the largest effects. This indicates that <span class="elsevierStyleItalic">Digi</span> has narrowed the <span class="elsevierStyleItalic">Gap</span> in these regions by upgrading the industrial structure. In contrast, Beijing, Shanxi, Ningxia, Inner Mongolia, and other regions exhibit positive coefficients, indicating that <span class="elsevierStyleItalic">Digi</span> has widened the <span class="elsevierStyleItalic">Gap</span> in these areas by upgrading the industrial structure.</p><p id="para0053" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0004">Fig. 4</a> illustrates the magnitude and significance of the chain-mediated effects, indicating that <span class="elsevierStyleItalic">Digi</span> influences the <span class="elsevierStyleItalic">Gap</span> by altering <span class="elsevierStyleItalic">Hc</span>, which subsequently affects <span class="elsevierStyleItalic">Stru</span>. The coefficients in regions such as Jilin, Tianjin, Hubei, and Guangxi are positive at the 10 % significance level, whereas regions such as Shanxi, Inner Mongolia, Henan, and Fujian exhibit negative coefficients. The coefficients in other regions have both positive and negative values, but are not statistically significant.</p></span><span id="sec0024" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0027">Direct and total effects analyses</span><p id="para0054" class="elsevierStylePara elsevierViewall">In addition to examining the indirect effects mediating variables such as <span class="elsevierStyleItalic">Hc</span> and <span class="elsevierStyleItalic">Stru</span> on the <span class="elsevierStyleItalic">Gap</span> through, <span class="elsevierStyleItalic">Digi</span> also directly impacts the <span class="elsevierStyleItalic">Gap</span>. <a class="elsevierStyleCrossRef" href="#fig0005">Fig. 5</a> presents the direct effects and their significance, revealing that the <span class="elsevierStyleItalic">Digi</span> in Jiangxi, Chongqing, and Shaanxi has significantly widened the urban–rural income gap. In contrast, the <span class="elsevierStyleItalic">Digi</span> in Jilin, Shandong, Zhejiang, Yunnan, Hebei, Sichuan, Guizhou, Liaoning, Hainan, and Xinjiang significantly narrowed the <span class="elsevierStyleItalic">Gap. Digi</span> had no significant impact on the <span class="elsevierStyleItalic">Gap</span> in other areas.</p><elsevierMultimedia ident="fig0005"></elsevierMultimedia><p id="para0055" class="elsevierStylePara elsevierViewall">The analysis above reveals that the three indirect pathways through which <span class="elsevierStyleItalic">Digi</span> affects the <span class="elsevierStyleItalic">Gap</span> exhibit significant spatial heterogeneity, with both positive and negative effects. These effects tend to offset each other, making it necessary to investigate the weight of the three mediating effects to examine overall indirect impact.</p><p id="para0056" class="elsevierStylePara elsevierViewall"><a class="elsevierStyleCrossRef" href="#fig0006">Fig. 6</a> presents the weights of the indirect and total effects. The left graph in <a class="elsevierStyleCrossRef" href="#fig0006">Fig. 6</a> shows that the total mediating effect of <span class="elsevierStyleItalic">Digi</span> on the <span class="elsevierStyleItalic">Gap</span> is negative in most regions. Only 12 provinces and cities exhibit a positive total mediating effect, and Jilin Province in the northeast had the largest effect, followed by Beijing, Inner Mongolia, and Shanxi in north China; Henan, Hubei, and Jiangxi in the central region; and Shandong, Jiangsu, Guangdong, and Hainan in the east coast area, with relatively smaller effects. The graph on the right of <a class="elsevierStyleCrossRef" href="#fig0006">Fig. 6</a> presents the spatial distribution of the total effects after combining the total and direct mediating effects. Notably, the impact of <span class="elsevierStyleItalic">Digi</span> on the <span class="elsevierStyleItalic">Gap</span> varies across regions, with some regions exhibiting a reduction in the income gap, while others show an expansion. Additionally, the spatial distribution of the total effects closely resembles the total mediating effects on the left graph, indicating that the total mediating effects are significantly larger than the direct effects.</p><elsevierMultimedia ident="fig0006"></elsevierMultimedia></span></span><span id="sec0025" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0028">Conclusions and policy implications</span><span id="sec0026" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0029">Conclusions</span><p id="para0057" class="elsevierStylePara elsevierViewall">This study constructs a digital economy development index to measure the <span class="elsevierStyleItalic">Digi</span> of 30 provinces and cities in China from 2013 to 2021. Embedding the MGWPR–SDM into a chain–mediated effect model and considering spatial autocorrelation and spatial heterogeneity, we analyze the mechanism of <span class="elsevierStyleItalic">Digi</span>’s impact on the <span class="elsevierStyleItalic">Gap</span> and its spatial heterogeneity. The results demonstrate that <span class="elsevierStyleItalic">Digi</span> can affect the <span class="elsevierStyleItalic">Gap</span> through one direct path and three indirect paths, all of which exhibit obvious spatial heterogeneity. The effects of each path cancel each other out, resulting in the same spatial heterogeneity for <span class="elsevierStyleItalic">Digi</span>’s total effect on the <span class="elsevierStyleItalic">Gap</span>. For example, <span class="elsevierStyleItalic">Digi</span> in areas such as Jilin, Inner Mongolia, and Shanxi widened the <span class="elsevierStyleItalic">Gap</span>, while <span class="elsevierStyleItalic">Digi</span> in areas such as Chongqing and Zhejiang narrowed the <span class="elsevierStyleItalic">Gap</span>.</p><p id="para0058" class="elsevierStylePara elsevierViewall">Regarding the mechanism of <span class="elsevierStyleItalic">Digi</span>’s impact on the <span class="elsevierStyleItalic">Gap, Digi</span> widens the <span class="elsevierStyleItalic">Gap</span> in most regions by upgrading human capital, with the largest effect in Jilin Province in Northeast China, followed by Inner Mongolia and Shanxi in North China, and a relatively small effect in most provinces in Central and East China (<a class="elsevierStyleCrossRef" href="#fig0002">Fig. 2</a>). Furthermore, <span class="elsevierStyleItalic">Digi</span> narrows the <span class="elsevierStyleItalic">Gap</span> in the vast majority of regions by promoting the upgrading of industrial structure. Among them, Hubei and Jiangxi provinces in the central region had the largest effect, followed by central, southern, northeastern, and northwestern China; only Beijing, Inner Mongolia, and Shanxi in northern China and Ningxia in northwestern China had a positive path 2 effect (<a class="elsevierStyleCrossRef" href="#fig0003">Fig. 3</a>). <span class="elsevierStyleItalic">Digi</span> affects the <span class="elsevierStyleItalic">Gap</span> by upgrading human capital and thus promoting industrial structure upgrading (i.e., the chain mediation effect) in the eastern region of Tianjin, Hebei, Shandong, Jiangsu, Zhejiang, Anhui, and Hunan in the central region, Ningxia, and Shaanxi in the western region, and most of the southwestern region is positive, indicating that <span class="elsevierStyleItalic">Digi</span> widens the <span class="elsevierStyleItalic">Gap</span> through Path 3, while other regions narrow the <span class="elsevierStyleItalic">Gap</span> through Path 3 (<a class="elsevierStyleCrossRef" href="#fig0004">Fig. 4</a>). Additionally, <span class="elsevierStyleItalic">Digi</span>’s direct impact reduces the <span class="elsevierStyleItalic">Gap</span> in most regions (<a class="elsevierStyleCrossRef" href="#fig0005">Fig. 5</a>). Combining the above four paths shows that there is also significant spatial heterogeneity in the total impact of <span class="elsevierStyleItalic">Digi</span> on the <span class="elsevierStyleItalic">Gap. Digi</span> in Beijing, Inner Mongolia, Shanxi, Henan, Hubei, Jiangxi, Jiangsu, Guangdong, and other provinces has widened the <span class="elsevierStyleItalic">Gap</span>, resulting in a digital divide effect. However, in most areas of the Northeast, eastern coastal regions, and western regions, <span class="elsevierStyleItalic">Digi</span> has narrowed the <span class="elsevierStyleItalic">Gap</span>, resulting in a digital dividend effect (<a class="elsevierStyleCrossRef" href="#fig0006">Fig. 6</a>).</p></span><span id="sec0027" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0030">Policy implications</span><p id="para0059" class="elsevierStylePara elsevierViewall">Based on these findings, this study presents the following policy implications. First, interregional cooperation and coordination must be strengthened. Due to the spatial autocorrelation of the <span class="elsevierStyleItalic">Gap</span>, local governments should collaborate to address the associated externalities rather than developing policies independently. This means that local governments should collaborate with neighboring regions and other local governments to jointly develop policies that can coordinate income disparities between different areas. Second, some regions—especially Fujian and Chongqing—should increase efforts to enhance <span class="elsevierStyleItalic">Hc</span>. It is possible to consider integrating digital education resources, remote vocational training, digital talent matching platforms, data–driven talent cultivation, and digital innovation and entrepreneurship support to fully leverage digital economy dividends, effectively improve rural labor's <span class="elsevierStyleItalic">Hc</span>, promote rural economic development, and narrow the <span class="elsevierStyleItalic">Gap</span>. We also focus on the integrating and upgrading <span class="elsevierStyleItalic">Stru</span>. Hubei and Jiangxi provinces in the central region and local governments in central, southern, northeastern, and northwestern regions can promote <span class="elsevierStyleItalic">Stru</span> upgrading and transformation by advancing digital agricultural technology, digital industry upgrading, and digital service industry development. This will improve rural employment opportunities and incomes, promote sustainable development of the regional economy, achieve integrated development of urban and rural economies, and balance the growth of society by narrowing the <span class="elsevierStyleItalic">Gap</span>. Additionally, policies should be strategically tailored to local conditions. Each region should develop corresponding policies based on its socioeconomic conditions and natural resource endowments, reasonably develop the digital economy, and narrow the <span class="elsevierStyleItalic">Gap</span> in a context–specific manner. Regional disparities must be considered, and policies must be targeted and feasible to achieve the optimal results.</p></span></span><span id="sec0027a" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="cesectitle0030a">CRediT authorship contribution statement</span><p id="para0059a" class="elsevierStylePara elsevierViewall"><span class="elsevierStyleBold">He Xia:</span> Writing – original draft, Funding acquisition, Conceptualization. <span class="elsevierStyleBold">Haijing Yu:</span> Software, Methodology, Formal analysis. <span class="elsevierStyleBold">Senhao Wang:</span> Resources, Data curation. <span class="elsevierStyleBold">Hong Yang:</span> Writing – review & editing, Supervision.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:12 [ 0 => array:3 [ "identificador" => "xres2235326" "titulo" => "Abstract" "secciones" => array:1 [ 0 => array:1 [ "identificador" => "abss0001" ] ] ] 1 => array:2 [ "identificador" => "xpalclavsec1871003" "titulo" => "Keywords" ] 2 => array:2 [ "identificador" => "xpalclavsec1871004" "titulo" => "JEL Classification" ] 3 => array:2 [ "identificador" => "sec0001" "titulo" => "Introduction" ] 4 => array:3 [ "identificador" => "sec0002" "titulo" => "Theoretical analysis and research hypotheses" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0003" "titulo" => "Impact of digital economy on the urban–rural income gap" ] 1 => array:3 [ "identificador" => "sec0004" "titulo" => "Impact mechanisms of the digital economy on the urban–rural income gap" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Digital economy, human capital, and the urban–rural income gap" ] 1 => array:2 [ "identificador" => "sec0006" "titulo" => "Digital economy, industrial structure, and the urban–rural income gap" ] ] ] ] ] 5 => array:3 [ "identificador" => "sec0007" "titulo" => "Model construction" "secciones" => array:3 [ 0 => array:2 [ "identificador" => "sec0008" "titulo" => "Spatial weight matrix" ] 1 => array:2 [ "identificador" => "sec0009" "titulo" => "Direct effects model" ] 2 => array:2 [ "identificador" => "sec0010" "titulo" => "Mediating effect model" ] ] ] 6 => array:3 [ "identificador" => "sec0011" "titulo" => "Variable selection and data description" "secciones" => array:2 [ 0 => array:3 [ "identificador" => "sec0012" "titulo" => "Variable selection" "secciones" => array:4 [ 0 => array:2 [ "identificador" => "sec0013" "titulo" => "Explained variable" ] 1 => array:2 [ "identificador" => "sec0014" "titulo" => "Core explanatory variable" ] 2 => array:2 [ "identificador" => "sec0015" "titulo" => "Mediating variables" ] 3 => array:2 [ "identificador" => "sec0016" "titulo" => "Control variables" ] ] ] 1 => array:2 [ "identificador" => "sec0017" "titulo" => "Data description and explanation" ] ] ] 7 => array:3 [ "identificador" => "sec0018" "titulo" => "Empirical results analysis" "secciones" => array:6 [ 0 => array:2 [ "identificador" => "sec0019" "titulo" => "Model selection" ] 1 => array:2 [ "identificador" => "sec0020" "titulo" => "Variable selection: spatial varying or fixed coefficient variable" ] 2 => array:2 [ "identificador" => "sec0021" "titulo" => "Varying coefficient regression results" ] 3 => array:2 [ "identificador" => "sec0022" "titulo" => "Varying coefficient regression results" ] 4 => array:2 [ "identificador" => "sec0023" "titulo" => "Mediating effect analysis" ] 5 => array:2 [ "identificador" => "sec0024" "titulo" => "Direct and total effects analyses" ] ] ] 8 => array:3 [ "identificador" => "sec0025" "titulo" => "Conclusions and policy implications" "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0026" "titulo" => "Conclusions" ] 1 => array:2 [ "identificador" => "sec0027" "titulo" => "Policy implications" ] ] ] 9 => array:2 [ "identificador" => "sec0027a" "titulo" => "CRediT authorship contribution statement" ] 10 => array:2 [ "identificador" => "xack770919" "titulo" => "Funding" ] 11 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "fechaRecibido" => "2023-11-20" "fechaAceptado" => "2024-06-09" "PalabrasClave" => array:1 [ "en" => array:2 [ 0 => array:4 [ "clase" => "keyword" "titulo" => "Keywords" "identificador" => "xpalclavsec1871003" "palabras" => array:5 [ 0 => "Digital economy" 1 => "Urban–rural income gap" 2 => "Spatial heterogeneity" 3 => "MGWPR–SDM" 4 => "Chain–mediated effect" ] ] 1 => array:4 [ "clase" => "jel" "titulo" => "JEL Classification" "identificador" => "xpalclavsec1871004" "palabras" => array:4 [ 0 => "O11" 1 => "O18" 2 => "R11" 3 => "R12" ] ] ] ] "tieneResumen" => true "resumen" => array:1 [ "en" => array:2 [ "titulo" => "Abstract" "resumen" => "<span id="abss0001" class="elsevierStyleSection elsevierViewall"><p id="spara022" class="elsevierStyleSimplePara elsevierViewall">This study examines how digital economy development in China impacts the urban–rural income gap. We construct a digital economy development index using panel data from 30 provinces and cities in China from 2013 to 2021. By combining a spatially varying coefficient model with a chain–mediated effect model, we quantify the impact of digital economy on the urban–rural income gap and examine its spatial heterogeneity. The results show that the digital economy influences the urban–rural income gap through four different pathways, each of which exhibits significant spatial variation. As these paths offset each other, the digital economy development in Beijing, Inner Mongolia, Shanxi, Henan, Hubei, Jiangxi, Jiangsu, Guangdong, and other provinces has widened the urban–rural income gap, resulting in a digital divide effect. However, in most areas of China's northeast, east coast, and western regions, the digital economy development has narrowed the income gap, resulting in a digital dividend effect. This study investigates the relevant debates among scholars and provides valuable insights and foundations for strategic decision making to reduce the urban–rural income gap in various regions.</p></span>" ] ] "NotaPie" => array:1 [ 0 => array:3 [ "etiqueta" => "1" "nota" => "<p class="elsevierStyleNotepara" id="notep0001">These authors contributed equally to this work.</p>" "identificador" => "fn1" ] ] "multimedia" => array:20 [ 0 => array:8 [ "identificador" => "fig0001" "etiqueta" => "Fig. 1" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr1.jpeg" "Alto" => 589 "Ancho" => 1250 "Tamanyo" => 38199 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0002" "detalle" => "Fig " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spara001" class="elsevierStyleSimplePara elsevierViewall">Diagram of the chain mediating effect model.</p>" ] ] 1 => array:8 [ "identificador" => "fig0002" "etiqueta" => "Fig. 2" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr2.jpeg" "Alto" => 1568 "Ancho" => 3000 "Tamanyo" => 270467 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0012" "detalle" => "Fig " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spara002" class="elsevierStyleSimplePara elsevierViewall">Mediating effect and statistical significance of path 1.</p>" ] ] 2 => array:8 [ "identificador" => "fig0003" "etiqueta" => "Fig. 3" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr3.jpeg" "Alto" => 1563 "Ancho" => 3000 "Tamanyo" => 264404 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0013" "detalle" => "Fig " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spara003" class="elsevierStyleSimplePara elsevierViewall">Mediating effect and statistical significance of path 2.</p>" ] ] 3 => array:8 [ "identificador" => "fig0004" "etiqueta" => "Fig. 4" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr4.jpeg" "Alto" => 1571 "Ancho" => 3000 "Tamanyo" => 270587 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0014" "detalle" => "Fig " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spara004" class="elsevierStyleSimplePara elsevierViewall">Chain mediating effect and statistical significance of path 3.</p>" ] ] 4 => array:8 [ "identificador" => "fig0005" "etiqueta" => "Fig. 5" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr5.jpeg" "Alto" => 1565 "Ancho" => 3000 "Tamanyo" => 257671 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0015" "detalle" => "Fig " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spara005" class="elsevierStyleSimplePara elsevierViewall">Direct effect and statistical significance of digital economy on the urban–rural income gap.</p>" ] ] 5 => array:8 [ "identificador" => "fig0006" "etiqueta" => "Fig. 6" "tipo" => "MULTIMEDIAFIGURA" "mostrarFloat" => true "mostrarDisplay" => false "figura" => array:1 [ 0 => array:4 [ "imagen" => "gr6.jpeg" "Alto" => 1566 "Ancho" => 3000 "Tamanyo" => 267696 ] ] "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0016" "detalle" => "Fig " "rol" => "short" ] ] "descripcion" => array:1 [ "en" => "<p id="spara006" class="elsevierStyleSimplePara elsevierViewall">Total indirect effect and total effect of the digital economy on the urban–rural income gap.</p>" ] ] 6 => array:8 [ "identificador" => "tbl0001" "etiqueta" => "Table 1" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0001" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spara008" class="elsevierStyleSimplePara elsevierViewall">Notes: Wgrav represents the asymmetric gravitational potential energy spatial weight matrix proposed in this article, Wdist and Wecon represent spatial weight matrices constructed based on geographic distance and economic distance, respectively.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0001"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0002"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Wgrav \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0003"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Wdist \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0004"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Wecon \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0005"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">AIC</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0006"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">11,712.28 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0007"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">12,624.54 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0008"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">12,292.91 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0009"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">BIC</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0010"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">30,312.52 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0011"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">31,224.78 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0012"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">30,893.15 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643741.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara007" class="elsevierStyleSimplePara elsevierViewall">Comparison of fitting effects of three weight matrices.</p>" ] ] 7 => array:8 [ "identificador" => "tbl0002" "etiqueta" => "Table 2" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0003" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0013"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Primary indicators \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0014"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="left" valign="top" scope="col" style="border-bottom: 2px solid black">Secondary indicators \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0015"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Tertiary indicator \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0016"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="9" align="left" valign="top">Digital Infrastructure</td><a name="en0017"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="5" align="left" valign="top">Traditional digital infrastructure</td><a name="en0018"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Internet broadband access ports \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0021"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Length of fiber optic lines \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0024"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Number of Internet domain names \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0027"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Number of Internet sites \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0030"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Number of Internet pages \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0032"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="4" align="left" valign="top">New digital infrastructure</td><a name="en0033"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Local exchange capacity \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0036"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Mobile Internet access traffic \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0039"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Number of cell phone base stations \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0042"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Ipv4/Ipv6 address number \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0043"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="11" align="left" valign="top">Digital industry development</td><a name="en0044"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="7" align="left" valign="top">digital industrialization</td><a name="en0045"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Revenue from telecommunication services \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0048"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Mobile Phone Production \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0051"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">IC production \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0054"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Production of microcomputing equipment \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0057"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Revenue from software operations \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0060"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Revenue from information technology services \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0063"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Percentage of Information Technology Employees \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0065"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="4" align="left" valign="top">Industrial digitization</td><a name="en0066"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">express delivery volume \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0069"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">E–commerce transaction value \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0072"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Number of websites per 100 enterprises \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0075"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Percentage of enterprises with e–commerce trading activities \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0076"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="3" align="left" valign="top">Digital financial services</td><a name="en0077"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="top">Breadth of coverage \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0078"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Refer to <a class="elsevierStyleCrossRef" href="#bib0017">Guo et al. (2020)</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0080"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="top">Depth of application \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0081"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Refer to <a class="elsevierStyleCrossRef" href="#bib0017">Guo et al. (2020)</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0083"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="left" valign="top">Digitization level \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0084"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">Refer to <a class="elsevierStyleCrossRef" href="#bib0017">Guo et al. (2020)</a> \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643737.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara009" class="elsevierStyleSimplePara elsevierViewall">Indicator System for the Development Level of the Digital Economy.</p>" ] ] 8 => array:8 [ "identificador" => "tbl0003" "etiqueta" => "Table 3" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0004" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spara011" class="elsevierStyleSimplePara elsevierViewall">Note: Q1 and Q3 represent the 25th and 75th percentiles, respectively, with SD being the abbreviation for standard deviation.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0085"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0086"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Min \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0087"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Q1 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0088"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Median \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0089"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Mean \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0090"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Q3 \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0091"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Max \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0092"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">SD \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0093"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Gap</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0094"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.840 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0095"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.293 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0096"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.485 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0097"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.549 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0098"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.758 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0099"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">3.800 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0100"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.383 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0101"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0102"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.015 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0103"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.039 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0104"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.068 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0105"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.098 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0106"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.126 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0107"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.509 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0108"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.087 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0109"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Hc</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0110"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">7.474 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0111"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">8.824 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0112"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">9.253 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0113"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">9.348 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0114"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">9.618 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0115"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">12.681 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0116"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.906 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0117"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Stru</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0118"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.570 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0119"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.940 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0120"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.150 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0121"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.324 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0122"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.380 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0123"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">5.300 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0124"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.717 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0125"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Pgdp</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0126"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.315 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0127"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">4.185 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0128"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">5.398 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0129"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">6.223 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0130"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">7.213 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0131"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">18.398 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0132"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.943 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0133"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Urban</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0134"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.379 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0135"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.530 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0136"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.593 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0137"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.609 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0138"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.665 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0139"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.896 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0140"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.115 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0141"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0142"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.010 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0143"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.090 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0144"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.140 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0145"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.241 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0146"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.308 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0147"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.270 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0148"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.244 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0149"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0150"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.097 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0151"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.567 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0152"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.946 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0153"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.986 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0154"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.336 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0155"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.237 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0156"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.523 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0157"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0158"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.330 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0159"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.170 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0160"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.610 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0161"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.806 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0162"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.220 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0163"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">6.450 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0164"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.961 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0165"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"><span class="elsevierStyleItalic">Sub_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0166"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.040 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0167"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.093 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0168"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.120 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0169"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.116 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0170"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.140 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0171"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.200 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0172"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.034 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643732.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara010" class="elsevierStyleSimplePara elsevierViewall">Descriptive statistics of variables.</p>" ] ] 9 => array:8 [ "identificador" => "tbl0004" "etiqueta" => "Table 4" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0005" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spara013" class="elsevierStyleSimplePara elsevierViewall">The p–values corresponding to Morans'I statistic are in parentheses.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0173"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0174"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Gap \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0175"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Hc \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0176"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Stru \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0177"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">2013 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0178"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.100 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0179"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.063 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0180"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.009 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0181"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0182"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0183"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0184"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0185"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">2014 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0186"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.084 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0187"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.056 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0188"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.007 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0189"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0190"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0191"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0192"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0193"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">2015 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0194"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.087 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0195"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.052 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0196"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.008 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0197"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0198"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0199"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0200"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0201"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">2016 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0202"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.087 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0203"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.052 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0204"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.015 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0205"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0206"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0207"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0208"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0209"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">2017 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0210"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.086 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0211"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.041 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0212"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.016 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0213"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0214"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0215"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0216"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0217"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">2018 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0218"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.085 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0219"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.041 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0220"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.020 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0221"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0222"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0223"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0224"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0225"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">2019 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0226"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.084 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0227"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.044 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0228"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.007 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0229"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0230"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0231"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0232"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0233"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">2020 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0234"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.086 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0235"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.049 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0236"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.007 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0237"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0238"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0239"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0240"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0241"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">2021 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0242"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.087 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0243"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.049 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0244"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.011 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0245"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0246"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0247"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0248"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643740.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara012" class="elsevierStyleSimplePara elsevierViewall">Morans'I test results.</p>" ] ] 10 => array:8 [ "identificador" => "tbl0005" "etiqueta" => "Table 5" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0006" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0249"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black"> \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0250"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Model (1) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0251"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Model (2) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0252"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Model (3) \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0253"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">SDM or SAR \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0254"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">27.57 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0255"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">75.76 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0256"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">24.29 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0257"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0258"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.002) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0259"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0260"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.004) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0261"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top">SDM or SEM \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0262"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">31.32 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0263"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">61.07 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0264"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">30.65 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0265"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowhead " align="" valign="top"> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0266"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0267"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0268"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643735.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara014" class="elsevierStyleSimplePara elsevierViewall">LR test results.</p>" ] ] 11 => array:8 [ "identificador" => "tbl0006" "etiqueta" => "Table 6" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0007" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spara016" class="elsevierStyleSimplePara elsevierViewall">WY is the spatial lag term of the explained variables of each model, the variable starting with “w_” represents the spatial lag term of the variable, and “-” represents the missing value. Same as the following tables.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0269"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0270"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Model (1) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0271"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Model (2) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0272"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Model (3) \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0273"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Intercept</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0274"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.051 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0275"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0276"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0277"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">WY</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0278"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.008 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0279"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.088 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0280"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0281"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0282"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0283"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.001 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0284"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0285"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Hc</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0286"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.033 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0287"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0288"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0289"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Stru</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0290"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.24 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0291"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0292"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0293"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Pgdp</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0294"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.194 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0295"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.004 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0296"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0297"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Urban</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0298"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.062 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0299"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0300"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0301"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0302"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.008 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0303"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0304"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0305"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0306"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.032 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0307"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0308"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0309"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0310"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.027 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0311"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0312"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0313"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Sub_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0314"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.051 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0315"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0316"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.011 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0317"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0318"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0319"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0320"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0321"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Hc</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0322"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.025 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0323"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0324"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.004 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0325"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Stru</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0326"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0327"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0328"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0329"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Pgdp</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0330"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.148 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0331"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.007 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0332"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0333"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Urban</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0334"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.109 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0335"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0336"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0337"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0338"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0339"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0340"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0341"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0342"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.01 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0343"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0344"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0345"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0346"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.025 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0347"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.004 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0348"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0349"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Sub_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0350"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.033 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0351"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0352"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643736.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara015" class="elsevierStyleSimplePara elsevierViewall">Bootstrap test results (P–value).</p>" ] ] 12 => array:8 [ "identificador" => "tbl0007" "etiqueta" => "Table 7" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0008" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:2 [ "leyenda" => "<p id="spara018" class="elsevierStyleSimplePara elsevierViewall">“†” indicates varying coefficient variables. “–” represents the missing value. *, **, and *** indicate statistically significance at the 0.1, 0.05, and 0.01 levels, respectively, with standard deviations in parentheses.</p>" "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0353"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0354"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Model (1) \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0355"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Model (2) \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0356"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top"><span class="elsevierStyleItalic">WY</span></td><a name="en0357"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top">†</td><a name="en0358"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.769*** \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0361"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.003) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0362"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0363"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0364"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0365"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Hc</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0366"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0367"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0368"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top"><span class="elsevierStyleItalic">Stru</span></td><a name="en0369"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.164*** \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0370"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top">–</td></tr><tr title="table-row"><a name="en0372"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000)) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0374"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top"><span class="elsevierStyleItalic">Pgdp</span></td><a name="en0375"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.01 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0376"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top">†</td></tr><tr title="table-row"><a name="en0378"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.310) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0380"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top">Urban</td><a name="en0381"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–3.231*** \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0382"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top">†</td></tr><tr title="table-row"><a name="en0384"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.000) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0386"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0387"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0388"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0389"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0390"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0391"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0392"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0393"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0394"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0395"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top"><span class="elsevierStyleItalic">Sub_rate</span></td><a name="en0396"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.043 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0397"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top">†</td></tr><tr title="table-row"><a name="en0399"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.938) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0401"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0402"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0403"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0404"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Hc</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0405"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0406"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0407"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Stru</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0408"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0409"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">– \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0410"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top"><span class="elsevierStyleItalic">w_Pgdp</span></td><a name="en0411"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.105 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0412"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top">†</td></tr><tr title="table-row"><a name="en0414"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.123) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0416"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top"><span class="elsevierStyleItalic">w_Urban</span></td><a name="en0417"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–2.697 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0418"></a><td class="td-with-role" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t ; entry_with_role_rowgroup " rowspan="2" align="left" valign="top">†</td></tr><tr title="table-row"><a name="en0420"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">(0.335) \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0422"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0423"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0424"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0425"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0426"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0427"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0428"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0429"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0430"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0431"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Sub_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0432"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0433"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">† \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0434"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">R<span class="elsevierStyleSup">2</span></span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0435"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.304 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0436"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.038 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643739.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara017" class="elsevierStyleSimplePara elsevierViewall">Fixed coefficient results.</p>" ] ] 13 => array:8 [ "identificador" => "tbl0008" "etiqueta" => "Table 8" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0009" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0437"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0438"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Min \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0439"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">1st Qu \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0440"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Median \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0441"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">3rd Qu \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0442"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Max \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0443"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">WY</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0444"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.794 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0445"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.020 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0446"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.245 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0447"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.511 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0448"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.683 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0449"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0450"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.145 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0451"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.078 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0452"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.020 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0453"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.014 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0454"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.094 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0455"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Hc</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0456"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.938 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0457"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.115 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0458"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.011 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0459"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.107 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0460"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.477 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0461"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0462"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.244 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0463"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.087 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0464"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.153 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0465"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.193 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0466"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.310 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0467"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0468"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.073 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0469"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.046 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0470"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.012 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0471"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.003 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0472"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.081 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0473"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0474"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–2.493 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0475"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.465 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0476"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.013 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0477"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.632 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0478"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">6.261 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0479"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0480"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.034 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0481"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.294 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0482"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.450 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0483"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.637 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0484"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.011 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0485"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Hc</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0486"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.569 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0487"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.345 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0488"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.157 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0489"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.009 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0490"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.433 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0491"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Stru</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0492"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–2.729 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0493"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.597 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0494"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.401 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0495"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.066 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0496"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.913 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0497"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0498"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.551 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0499"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.809 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0500"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.971 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0501"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.111 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0502"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.778 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0503"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0504"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.509 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0505"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.151 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0506"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.053 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0507"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.053 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0508"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.365 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0509"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0510"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–9.667 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0511"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–4.872 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0512"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–2.228 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0513"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.391 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0514"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">7.486 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0515"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Sub_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0516"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.747 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0517"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.215 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0518"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.456 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0519"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.751 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0520"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.251 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0521"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">R<span class="elsevierStyleSup">2</span></span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0522"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.009 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0523"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.083 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0524"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.104 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0525"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.118 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0526"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.148 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643734.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara019" class="elsevierStyleSimplePara elsevierViewall">Varying coefficient results of model (1).</p>" ] ] 14 => array:8 [ "identificador" => "tbl0009" "etiqueta" => "Table 9" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0010" "detalle" => "Table " "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0527"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0528"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Min \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0529"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">1st Qu \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0530"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Median \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0531"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">3rd Qu \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0532"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Max \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0533"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0534"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.196 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0535"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.564 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0536"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.171 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0537"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.074 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0538"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.698 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0539"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Pgdp</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0540"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.055 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0541"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.035 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0542"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">−0.013 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0543"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.003 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0544"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.055 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0545"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Urban</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0546"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–4.244 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0547"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–2.189 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0548"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.551 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0549"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.730 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0550"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">3.913 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0551"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0552"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.032 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0553"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.669 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0554"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.331 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0555"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.179 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0556"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.355 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0557"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0558"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.342 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0559"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.040 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0560"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.117 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0561"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.331 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0562"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.620 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0563"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0564"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.078 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0565"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.020 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0566"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.018 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0567"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.041 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0568"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.127 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0569"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Sub_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0570"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–3.055 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0571"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.217 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0572"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.887 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0573"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.521 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0574"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">4.514 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0575"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0576"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–3.141 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0577"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.177 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0578"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.901 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0579"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">6.091 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0580"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">14.593 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0581"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Pgdp</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0582"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.612 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0583"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.221 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0584"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.154 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0585"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.061 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0586"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.068 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0587"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Urban</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0588"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.531 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0589"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">12.794 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0590"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">15.895 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0591"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">18.866 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0592"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">22.850 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0593"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0594"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–6.435 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0595"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–4.545 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0596"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–3.697 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0597"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–2.831 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0598"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.283 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0599"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0600"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.581 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0601"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.681 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0602"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.325 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0603"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.205 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0604"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.365 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0605"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0606"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.290 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0607"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.726 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0608"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.369 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0609"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.200 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0610"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.183 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0611"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Sub_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0612"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–32.820 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0613"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–8.570 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0614"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–3.944 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0615"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.607 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0616"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">13.224 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0617"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">R<span class="elsevierStyleSup">2</span></span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0618"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.070 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0619"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.108 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0620"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.138 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0621"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.154 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0622"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.199 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643738.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara020" class="elsevierStyleSimplePara elsevierViewall">Varying coefficient results of model (2).</p>" ] ] 15 => array:8 [ "identificador" => "tbl0010" "etiqueta" => "Table 10" "tipo" => "MULTIMEDIATABLA" "mostrarFloat" => true "mostrarDisplay" => false "detalles" => array:1 [ 0 => array:3 [ "identificador" => "alt0011" "detalle" => "Table 1" "rol" => "short" ] ] "tabla" => array:1 [ "tablatextoimagen" => array:1 [ 0 => array:2 [ "tabla" => array:1 [ 0 => """ <table border="0" frame="\n \t\t\t\t\tvoid\n \t\t\t\t" class=""><thead title="thead"><tr title="table-row"><a name="en0623"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Variables \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0624"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Min \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0625"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">1st Qu \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0626"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Median \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0627"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">3rd Qu \t\t\t\t\t\t\n \t\t\t\t\t\t</th><a name="en0628"></a><th class="td" title="\n \t\t\t\t\ttable-head\n \t\t\t\t " align="" valign="top" scope="col" style="border-bottom: 2px solid black">Max \t\t\t\t\t\t\n \t\t\t\t\t\t</th></tr></thead><tbody title="tbody"><tr title="table-row"><a name="en0629"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">WY</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0630"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–2.967 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0631"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.999 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0632"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.363 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0633"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.081 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0634"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.836 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0635"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0636"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.303 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0637"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.107 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0638"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.076 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0639"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.019 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0640"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.130 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0641"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Hc</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0642"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.055 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0643"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.030 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0644"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.018 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0645"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.002 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0646"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.058 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0647"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Pgdp</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0648"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–10.649 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0649"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–8.801 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0650"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–6.507 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0651"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–5.673 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0652"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–3.638 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0653"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Urban</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0654"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.652 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0655"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.074 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0656"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.130 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0657"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.382 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0658"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.639 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0659"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0660"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.091 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0661"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.290 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0662"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.405 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0663"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.546 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0664"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.779 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0665"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0666"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.170 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0667"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.034 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0668"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.019 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0669"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.085 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0670"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.161 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0671"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0672"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.896 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0673"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.879 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0674"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">3.138 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0675"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">3.830 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0676"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">4.315 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0677"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">Sub_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0678"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–12.055 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0679"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.031 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0680"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.523 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0681"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">4.030 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0682"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">9.046 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0683"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Digi</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0684"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.467 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0685"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.551 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0686"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.947 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0687"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.204 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0688"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">2.245 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0689"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Hc</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0690"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.301 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0691"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.226 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0692"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.098 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0693"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.041 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0694"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.277 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0695"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Pgdp</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0696"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–14.957 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0697"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–6.947 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0698"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–3.365 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0699"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.772 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0700"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">5.487 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0701"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Urban</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0702"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.732 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0703"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.160 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0704"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.288 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0705"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.986 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0706"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">3.958 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0707"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Open</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0708"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.099 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0709"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.279 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0710"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.619 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0711"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.087 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0712"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">1.868 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0713"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Traf</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0714"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.120 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0715"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.673 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0716"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.450 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0717"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.270 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0718"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.199 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0719"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Agri_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0720"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–10.407 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0721"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">3.848 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0722"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">7.774 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0723"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">14.595 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0724"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">22.178 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0725"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">w_Sub_rate</span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0726"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–2.848 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0727"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.517 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0728"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–1.047 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0729"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">–0.699 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0730"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.151 \t\t\t\t\t\t\n \t\t\t\t</td></tr><tr title="table-row"><a name="en0731"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top"><span class="elsevierStyleItalic">R<span class="elsevierStyleSup">2</span></span> \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0732"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.206 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0733"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.289 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0734"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.318 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0735"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.332 \t\t\t\t\t\t\n \t\t\t\t</td><a name="en0736"></a><td class="td" title="\n \t\t\t\t\ttable-entry\n \t\t\t\t " align="" valign="top">0.365 \t\t\t\t\t\t\n \t\t\t\t</td></tr></tbody></table> """ ] "imagenFichero" => array:1 [ 0 => "xTab3643733.png" ] ] ] ] "descripcion" => array:1 [ "en" => "<p id="spara021" class="elsevierStyleSimplePara elsevierViewall">Varying coefficient results of mediation model (3).</p>" ] ] 16 => array:6 [ "identificador" => "eqn0001" "etiqueta" => "(1)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "wij=|ω−I(mi‾>mj‾)|·mit·mjtdij" "Fichero" => "STRIPIN_si13.jpeg" "Tamanyo" => 183 "Alto" => 32 "Ancho" => 175 ] ] 17 => array:6 [ "identificador" => "eqn0002" "etiqueta" => "(2)" "tipo" => "MULTIMEDIAFORMULA" "mostrarFloat" => false "mostrarDisplay" => true "Formula" => array:5 [ "Matematica" => "Gapit=α0+ρ1WGapit+α1Digiit+ΣkαkXk,it+ΣrλrCr,it+ΣqθqWXq,it+ui+νt+εit(K≥2)" "Fichero" => 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Tang" ] ] ] ] ] "host" => array:1 [ 0 => array:2 [ "doi" => "10.3969/j.issn.1002-2104.2011.10.021" "Revista" => array:6 [ "tituloSerie" => "China Population, Resources and Environment" "fecha" => "2011" "volumen" => "21" "numero" => "10" "paginaInicial" => "138" "paginaFinal" => "146" ] ] ] ] ] ] ] ] ] ] "agradecimientos" => array:1 [ 0 => array:4 [ "identificador" => "xack770919" "titulo" => "Funding" "texto" => "<p id="para0060" class="elsevierStylePara elsevierViewall">This work was supported by the <span class="elsevierStyleGrantSponsor" id="gs0001">Graduate Research and Innovation Projects of Xinjiang Agricultural University</span>: Research on the <span class="elsevierStyleGrantSponsor" id="gs0002">Impact of Digital Rural Construction on Agricultural Economic Resilience in Xinjiang Region</span> [Grant Numbers: No. <span class="elsevierStyleGrantNumber" refid="gs0001">XJAUGRI2024011</span>].</p>" "vista" => "all" ] ] ] "idiomaDefecto" => "en" "url" => "/2444569X/0000000900000003/v2_202409040527/S2444569X24000441/v2_202409040527/en/main.assets" "Apartado" => null "PDF" => "https://static.elsevier.es/multimedia/2444569X/0000000900000003/v2_202409040527/S2444569X24000441/v2_202409040527/en/main.pdf?idApp=UINPBA00004N&text.app=https://www.elsevier.es/" "EPUB" => "https://multimedia.elsevier.es/PublicationsMultimediaV1/item/epub/S2444569X24000441?idApp=UINPBA00004N" ]
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