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    • In innovation and technology diffusion literature R D expend

    2018-10-30

    In innovation and technology diffusion literature, R & D expenditures play a decisive role. R & D expenditures are considered as flows and measured by the R & D intensity, i.e., R & D expenditures divided by the product of the firm. Therefore, the R & D intensity is considered a measure of technological effort of the industrial sector itself. However, as the process of technology diffusion depends on the rac protein capacity of the technological knowledge that is produced outside the sector (Griliches, 1992), it is necessary to use the technical coefficients of production of input–output matrices to weigh the amount of R & D expenditures that a sector borrows from one another in the process of technological diffusion (Wolff, 2007). In this sense, we used the input–output matrices for the same years of the bases PINTEC and PIA, matching up the different sectoral aggregation of databases. As innovation depends on the past trajectory of the R&D intensity (Song et al., 2003), both the measure of innovation and the diffusion consider the R & D intensity of the year you want to analyze it and the previous years. Thus, the measure of the last year will correspond to the sum of all the available years (2000, 2003 and 2005). It is also worth noting that will be considered as sectorial measure of production the value added of the industry, obtained from the PIA. Thus, the measure of innovation flow in sector i in period t is: In order to check the technological diffusion among sectors, it is necessary to measure the indirect R&D, i.e., the amount of R & D imported by the sector i from the rest of the economy, which depends on the amount of R & D performed by the sectors. Therefore, from the flow method and based on the diffusion of embodied technology, this will be measured by means of indirect R & D taken from the transactions of intermediate and capital goods. Thus, the total indirect R & D will be:being the R & D embodied in intermediate goods flowing into the sector i in period t, and that embodied in capital goods. Given that R & D expenditures are a proxy for technology and that is transported to other sectors through interindustry transactions, we use the inverse Leontief matrix for the construction of the variables that make up the measure of indirect R & D. Thus, since the intensity of the direct R & D (R) per unit of product (X) of sector i is , the indirect R & D embodied in the final demand for sector j is obtained as follows:where F is the final demand for product from sector j and b are the elements of the Leontief inverse matrix, or matrix B. Following the same principle, the R & D embodied in capital goods is calculated from the investment in the p-th product realized by j sector which would be embodied into the input of capital and of total inputs required by investment goods, ,of industry j () of product of sector i: In the present study, sector investment is measured from the amount invested in acquisitions, improvements and reductions in fixed assets, extracted from the PIA. Thus, if k=1, Eq. (13) becomes: From there, we get the measure of technological diffusion flow: Given the previous definitions, Table 1 displays the variables used in the model, along with their expected signs and their sources. The variable wage should present a positive relationship with the dependent variable since the greater the difference between the average wage of the individual\'s occupation (proxy of expected wage prior to mobility) and the individual\'s own wage, the greater the incentive to job changes among firms and mainly sectors. This proxy for expected wage differential is constructed according to the methodology found in Mendes et al. (2012). Furthermore, it is expected that the variable age shows a negative relationship with intersectoral mobility (Parrado et al., 2007), since their cost of job change would be greater. The same occurs for the case of the experience at the origin, measured by the time in which the employee remained in employment, which had before migrating, should show a negative relationship with mobility, consistent with the human capital models that point to a negative relationship between the mobility of workers and of workers’ experience (Parrado et al., 2007).