Infrastructure, Space Spillover and Manufacturing Cost Effect Zhang Guangnan Hong Guozhi Chen Guanghan1 Panel data of industrial enterprises in China's provinces, through spatial measurement and seemingly irrelevant regression (SUR) empirical analysis of the cost effects of China's infrastructure space spillovers and industry spillovers. The study finds that the cost effect of infrastructure space spillover on manufacturing industry is greater than the cost effect of local infrastructure, and the cost elasticity of the east is higher than that of the central and western regions; the imbalance of infrastructure regions leads to the reduction of network effects, and the infrastructure of industrial competition areas exists. Space spillovers and dense market spillovers have led to increased marginal cost and factor inputs in local manufacturing; in addition, the cost-effectiveness of infrastructure space spillovers depends on the concentration of manufacturing.
I. INTRODUCTION In recent years, China has increased infrastructure investment as an important policy measure to expand domestic demand, promote employment and economic growth, such as the central government's 4 trillion yuan investment focus and capital measurement, railways, highways, airports, water conservancy, etc. Major infrastructure construction and urban power grid transformation accounted for 37.5%, a total of about 1.5 trillion yuan. In fact, infrastructure investment can not only stimulate domestic demand to affect macroeconomic growth, but also affect the production cost and factor input structure of enterprises at the micro level. For example, a well-developed transportation network can reduce transportation costs and production depreciation. The characteristics of the infrastructure network structure often lead to its influence beyond the region, and ignoring this spatial spillover effect will lead to bias in the estimation of infrastructure effects.
Although there have been attempts to analyze the spatial spillover effects of infrastructure through spatial measurement methods, how to deeply study the impact of infrastructure space spillovers on local manufacturing production costs and factor inputs still has many problems, and there is a lack of empirical research based on China. . If endogenous problems exist in the production function, ignoring the inter-regional space spillovers and “market dense effects†of infrastructure
The resulting deviations in the conclusions, the impact of differences in the degree of agglomeration on the extent of infrastructure network utilization, and the inability to assess the cross-border benefits of infrastructure, all affect the assessment of government investment decisions to some extent. Therefore, based on the existing problems, this paper is based on the variable cost function and external factor analysis framework, using panel data of industrial enterprises in China, and empirically analyzing China's infrastructure space spillover through spatial measurement and seemingly unregressive regression (SUR). The cost effect and its industry spillovers. This is particularly important for assessing the cross-border strategic benefits of China's infrastructure investment and adjusting the spatial distribution and regional coordination of China's infrastructure investment.
The rest of the paper is arranged as follows: The second part reviews, reviews the infrastructure space spillover, and proposes the improved method for the problem; the third part theoretical model establishes the variable cost function and external factor analysis framework; fourth Some empirical methods, including spatial weight construction and spatial correlation test methods; Section 5 data and variable descriptions; Section 6 empirical analysis of the impact of infrastructure space spillovers on cost and factor inputs, and the relationship between industrial agglomeration and infrastructure space spillovers, and International comparison of estimates of infrastructure effects; conclusions of Part VII.
2. Looking back (1989), many of them regard infrastructure as an “external inputâ€.
To analyze its impact on output, growth, and productivity, but the spatial spillover effects of infrastructure network structures were generally ignored in the early days (Morenoeia Z., 2004). With the development of spatial measurement methods, it is found that the use of spatial correlation methods will reduce the size and significance of the estimation of infrastructure output effects, and the regional level research results are smaller than the national level research. It can be seen that the geographical scope and measurement methods of the research objects are more Sensitive; in addition, missing space spillovers will lead to estimated bias in infrastructure effects (Cohen and Paul, 2004; Cohen, 2009), and “thick-marketeffects†such as agglomeration, technology spillovers, demand and supply will lead to model design. Fixed errors, even if the spatial error autocorrelation model is used to distinguish the source of spillover effects, so it is necessary to measure the spatial spillover of infrastructure and the dense flooding of industries between industries (MorenoeiaZ., although more and more research on the cost effect of infrastructure space spillovers However, the following problems still exist: First, the problem of benefit distribution caused by infrastructure cost effects is often ignored.
Infrastructure reduces the cost of transportation and communication and leads to the redistribution of benefits between regions. Therefore, the regional distribution effect of infrastructure spillover cost benefits must be analyzed to balance the efficiency and equity objectives of infrastructure planning (L6peZeiaZ., 2009). Second, related research cannot assess the cross-border benefits of infrastructure.
For example, the spillover effect of transportation infrastructure is critical to the European integration process, but traditional methods cannot assess strategic benefits such as cross-border integration effects (European Commission, 2001). The existing research on infrastructure issues in China focuses on its growth and cost effects and its determinants, and less on the spatial spillover effects of infrastructure (Ma Shucai and Sun Changqing, 2001; Fan Qiang et al., 2004; Sihong, 2004; Feng and Li Jing, 2006; Wang Renfei and Wang Jinjie, 2007; Zhang Guangnan et al., 2010; Zhang Jun et al., 2007). It is worth noting that the estimation bias caused by the neglect of infrastructure space spillover effects also affects the evaluation of government investment decisions, leading to the failure of government infrastructure investment policies. For example, Delgado and Alvarez (2007) found that highway investment in various regions of Spain is a competitive tool for attracting foreign investment. Rather than relying on optimal social returns, it leads to over-investment in road infrastructure across the country. Therefore, in the current situation of increased infrastructure investment in China, it is particularly urgent and important to study the impact of spatial spillovers on manufacturing production costs and factor inputs. In view of the existing problems, this paper attempts to improve in the following aspects: First, for the adoption of the empirical model, the existing analysis of its marginal product or marginal cost effect is mainly based on the production function or cost function method including infrastructure. In the production function, on the one hand, infrastructure affects the output of the firm, and on the other hand, the increase in the output of the firm increases the government's tax revenue, which in turn affects infrastructure investment, and thus has endogenous problems. Moreover, the production function assumes that production inputs such as labor and private capital are exogenously given. Empirical estimates are based on assumptions that output disturbances are not related to inputs, so their estimates of marginal output of private inputs are unbelievable (Haughwout, 2002; Pereira and Roca). -Sagal6s, 2003). In the cost function theory framework, infrastructure affects vendor costs, but vendor costs do not affect government infrastructure investment, avoiding the endogenous bias of the production function that has been criticized (Morrison and Schwartz, 1996). Therefore, this paper solves the endogeneity problem of production function based on the elastic variable cost function, and gradually increases the external factors according to the spatial correlation method such as spatial correlation test to solve the externality problem and improve the rigor and robustness of the empirical analysis. In addition, considering the differences in spatial weight construction methods, such as the “geographical proximity†approach based on the assumption that “the capital and technology spillover effects of adjacent regions are more significantâ€, the “competition matrix†approach can distinguish between positive and negative spillovers that may exist in the infrastructure. Therefore, based on the analysis of spatial weights of “geographical proximityâ€, this paper uses the “competition matrix†method to further analyze the impact of manufacturing structural similarity on infrastructure space spillover effects.
Second, the total cost of infrastructure effects includes the sum of the cost effects of “local infrastructure†and “infrastructure space spilloversâ€, which were generally ignored in the early days, leading to an overestimation of the effects of the former. In this case, Morenoeia Z. (2004) uses the geometric mean method of the two to introduce the “global public capital†variable KkPG1%, where 0 and (1 0) measure the impact of local infrastructure and infrastructure space spillovers on the manufacturer's production costs. . This setting method is based on the multi-collinearity problem caused by the excessive number of variables, but the nonlinear method is needed to estimate the parameters, which often leads to convergence to local optimum and cannot achieve global optimization. Therefore, in order to avoid the local optimization problem of parameter nonlinear estimation, this paper uses the linear combination of “local infrastructure†and “infrastructure space overflow†to construct “global infrastructure†as the external input of the vendor to introduce the cost function. Third, the basis Facility spillover effects include both “space spillovers†between regions, as well as “market dense effects†such as agglomeration, technology spillovers, demand and supply. Studies have shown that ignoring market dense factors will lead to incorrect model setting (Morenoa/., 2004). . So this article will control "market thick spills"
Factor analysis of the spatial spillover effects of the infrastructure.
Fourth, China's manufacturing industry and infrastructure spatial distribution are consistent, but the market range of products and factors of different aggregation industries is different, and the degree of utilization of infrastructure networks is also different. Therefore, it is necessary to further analyze the industrial agglomeration and foundation of manufacturing industry. The relationship between facility space overflow.
3. Empirical Models: Variable Cost Function and External Factor Analysis Framework (2004) and Cohenand Paul (2001) study the spatial weight and spatial correlation test methods for improving spatial spillovers to control the impact of “market dense spilloversâ€. Define the variable cost function as: where Y is the total output and K is the fixed investment private capital. P includes the variable input factor labor L and the price P of the intermediate product M and the corpse Â¥. E is a vendor externality factor, including local infrastructure, infrastructure space spillover G, market dense spillover Y, and technological changes expressed in terms of time trends. Existing local infrastructure is commonly introduced as a cost component of the vendor's external input factors (Conradand Seitz, 1992). CohenandPaul (2001), this paper considers that infrastructure networks such as roads, railways, and electric power can create inter-regional space spillover effects, so the model also includes infrastructure space spillovers. G. In addition, in “geographically adjacent†and “social economy†There are also spatial spillover effects such as agglomeration effects, technology spillover effects, and supply demand between regions with similar characteristics or “input-output linkagesâ€, that is, “market dense effectsâ€. Studies have shown that ignoring market-dense factors will lead to erroneous settings of the model, with a “close to regional†output level Y, which is significant for measuring the market's dense spillover equations (Morenoeia Z., 2004). Therefore, based on the treatment method of MorenoeiaZ. (2004), this paper introduces the market dense effect, which will be: Beijing 7821, Tianjin 6552, Hebei 4957, Shanxi 9938, Inner Mongolia 3123, Liaoning 208.80, Jilin 1740, Heilongjiang 7906, Shanghai 8563, Jiangsu 9602, Zhejiang 40.74 , Anhui 41.20, Fujian 3683, Jiangxi 760, Shandong 171.77, Henan 3460, Hubei 3728, Hunan 3230, Guangdong 37307, Guangxi 4145, Hainan 1424, Chongqing 23.87, Sichuan 48.66, Guizhou 1277, Yunnan 51.71, Tibet 11.96, Shaanxi 35.69, Gansu 15.87,6 Because the private capital element of this model is not variable, it is impossible to determine the relationship between local infrastructure and capital through the factor demand function, but the degree of influence of the local infrastructure on the cost of capital and the impact of capital on cost. The direction of £VCK examines its complementary alternative relationship. Similarly, the relationship between infrastructure space spillovers and capital also depends on its impact on £VCK (Cohenand Paul, 7 Zhao Wei and Zhang Cui (2007) measure the degree of geographic agglomeration of the industry through the Krugman space Gini coefficient formula. It is found that the high-gathering industries in China's manufacturing industry include: chemical fiber, petroleum coking, metal products, instrumentation, electrical machinery and electronic communication. Low-concentration industries include: non-metallic minerals, beverage manufacturing, pharmaceuticals, chemical raw materials, non-ferrous metals and Food manufacturing.
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