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In the process of analysis, these factors were grouped, and regression analysis results of different groups were obtained. Therefore, the effects of these factors on spillover effect could be tested, which offer empirical evidence for related policy suggestions.4.2.1 Objective of researchThe objective is to study the impacts of “foreign capital” on the overall technical level of the industry, namely technology spillover effect, based on wholesale and retail trade data from many provinces in China. In addition, impacts of openness degree, infrastructure construction level, level of scientific and technological development on spillover effect will also be analyzed.4.2.2 The establishment of the modelIn theory, at micro level, technology spillover effect is closely related to strength and number of related technical activities of foreign enterprises,there is a positive correlation between them. From the perspective of industry, proportion and size of domestic and foreign capital have great influence on technology spillover effect of foreigncapital. In this dissertation, it is necessary to assume that other conditions are unchanged, and impact of change of foreign and domestic capital on technology level of the industry. In this dissertation, with reference to Caves’ (1974) basic model, the following multivariate linear function is built:(4.1) Due to the lack of statistical data, the author could only consider impacts of foreign capital on the overall technical level of the industry, therefore, variables that reflect the overall conditions of the industry are selected. Specifically, labor productivity in retail and wholesale industry is regarded as an explanatory variable, which is represented by LP and used to reflect the industry's technology level. As Jeffrey writes in in his classic econometric textbook: "Introduction to Econometrics: Modern View", "Although the standard economic modeling is sometimes the starting point for empirical analysis, in most cases, the use of economic theory is not so standard, and is even based on intuition,” the explanatory variables of the function, namely which factors have impacts on technology spillover, should be separated out. In this paper, three factors that reflect the characters of the industry and market are selected as control variables, according to internalization theory and industrial organization theory, which is shown as follows.Capital intensity is often an indirect factor that reflect characters of technologies and products(services) in the industry, it can reflect whether technologies and products in the industry have capital-intensive tendency, it is represented by per capital fixed capital. Human capital in the industry affects a company's technology R & D and absorbing capacity, thus affect spillover effect, it should be used as an explanatory variable. But due to the lack of related data, in this dissertation, measurement basis for labor quality is whether suitable or not, given the existence of logistics talents gap in China, it is assumed that current practitioners are suitable; Scale of enterprise is very important, on the one hand, labor productivity changes with the change of enterprise scale due to scale effect, on the other hand, scale of enterprise reflect its market position and affect its productivity and technological transformation, as well as its technological innovation and absorption ability. Here, it is used to reflect enterprise features and degree of concentration in the market.The above three factors and "foreign capital" (represented by the proportion of foreign capital in the industry) are used as explanatory variables in equation 4.1, in the following regression analysis, capital intensity, human capital in the industry, enterprise scale and foreign capital are represented by KI、LQ、ES and FDI. Based on the above, equation 4.1 can be transformed as:(4.2)4.2.3 Selection of sample dataSince statistical data related to retail and wholesale industry are not sufficient, in order to ensure the number of samples, in this dissertation, panel data model will be used for analysis based on equation 4.1. The so-called panel data model is behavior equation model which is based on time series, it is more close to the reality than simple cross-sectional data or time-series data. In the context of this dissertation, the use of panel data increase the amount of estimable data, enlarge the degree of freedom, and has certain positive impacts on the reduction of multicollinearity of explanatory variables, thus improving the accuracy of regression estimation.In this dissertation, panel data involve different regions, in current statistics, "China Statistical Yearbook" for foreign investment in retail and wholesale trade involve statistics information at national level, and "Large and medium-sized wholesale & retail accommodation and catering enterprises Statistical Yearbook" involve data at regional level, but it was published since 2007 and only have data of enterprises after 2006. Therefore, in this dissertation, data related to 31 provinces, municipalities and autonomous regions for 2006-2007 are analyzed, the data are from "China statistical Yearbook" for 2007-2008 and "Large and medium-sized wholesale & retail and accommodation and catering enterprises statistical Yearbook" for 2007-2008. Since foreign capital involvement in Gansu and Tibet is quite small, the 2 regions are ruled out in the empirical analysis, the number of regression samples- N is 58.There are individual difference(regional difference in this dissertation)e and time difference in the panel model. As for regional difference, in the analysis of impacts of factors on spillover effect, the factors are grouped according to degree of factors, and then discussed based on regions. As for time difference, in this dissertation, the model is regressed in form of logarithmic to reduce heteroscedasticity. In summary, the regression equation is:(4.3)Among them: labor productivity is measured by the ratio between GDP generated in the industry and the number of employees in the industry; Capital-intensity is the ratio between total fixed assets in the industry and the number of above-quota enterprises; Human capital is measured by average number of employees in the industry; Enterprise scale is measured by the ratio between total sales in the industry and average annual sales of the enterprise. Foreign investment involvement is measured by the proportion of foreign capital in the industry; In the empirical test, assuming capital intensity, human capital, enterprise size and other factors remain unchanged, the impacts of foreign capital on labor productivity in industry考察外资对行业劳动生产率的影响，也就是行业的溢出效应，若外资的系数为正且统计结果显著则说明溢出效应是正向的，为负或者统计结果不显著，则说明不存在正向溢出效应。