PROTOCOLO DE INTERVENCIÓN PSICOPEDAGÓGICA
FICHA TÉCNICA
B) TECNICAS PARA DISMINUIR CONDUCTAS NO-DESEABLES
A recurring theme in the measurement related literature is a need for better indicators on innovation impacts. The range of readily available published indicators has good coverage of input activities and innovation outcomes, though there are few available impacts measures. Various econometric studies explore the link between innovation characteristics and impacts in terms of innovation sales shares or productivity improvements (based on sales per employee over time), involving multi-staged regression analyses, and there is a need for better simple impact indicators. This final section briefly considers how weighting of indicators using employment data might improve understanding of the impact and distribution of innovations.
4.3.1 EMPLOYMENT WEIGHTED PRODUCT AND PROCESS INDICATORS
Employment weighting of selected innovation indicators is considered as a means of measuring impacts. As covered in section 2.4.2 on new indicators, authors such as Bloch and Lopez-Bassols (2009) recommend employment weighting for measuring impacts using common innovation indicators. Here the relevance of employment weighting is considered for novelty indicators, by sector. Employment weighting uses data on the reported number of employees, to describe the number of total employees working for firms with a given characteristic, which in this case includes innovation characteristics. Employment data is of very high quality, with negligible question non response rates (0.3%).
Table 4.30 presents three innovation indicators, using firm frequencies and employment weights for comparison: The share of technologically innovative firms, the share of novel product innovators, and the share of novel process innovators (all based on definitions and methods of construction explained in the previous section). Weighting can provide a picture of the relative impact or reach of respective innovation types, and of the distribution in activities across firms by size.
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Table 4.30 Employment weighted indicators by sector, 2010 TIC
Percent of technologically innovative firms 2010 A Percent of employees that work for technologically innovative firms 2010 B Percent of novel product innovators 2010 C Percent of employees that work for novel product innovators 2010 D Percent of novel process innovators 2010 E Percent of employees that work for novel process innovators 2010 F Natural resources, N=95 66.3% 79.3% 24.2% 39.9% 42.1% 58.2% Manufacturing, N=291 79.0% 88.2% 41.2% 37.8% 34.0% 46.6% Infrastructure, N=197 61.9% 81.9% 23.9% 41.5% 23.9% 33.0% Retail, wholesales, accommodation & food services, N=363 62.8% 75.1% 29.2% 51.4% 16.0% 31.6% Knowledge intensive business services, N=305 72.8% 79.6% 34.4% 35.9% 31.5% 32.9% Other services, N=150 70.0% 70.0% 30.0% 26.3% 20.0% 27.2% All sectors 69.2% 80.1% 31.8% 40.3% 26.4% 37.6%
The firm level indicator is calculated based on the frequency of firms with a given characteristic, while employment weighting shows the share of total employees that work for firms with a given characteristic. For all sectors combined, employment weighting increases the rates of innovation on each indicator. Column A shows that for all respondent firms (N=1401), 69.2% are technologically innovative, while the weighted indicator in column B shows that 80.1% are employed by technologically innovative firms. Similarly, in column C there are 31.8% of firms with novel product innovation, though these account for 40.3% of all employment, shown in column D. A larger effect is observed for novel process innovation: 26.4% of firms report this type of innovation in column E, while they account for 39.8% of all employment in column F. This implies that innovative activities have a wider impact across the labour force than suggested by frequency based indicators, and are more common in larger firms.
These impacts could be either positive or negative. For example, process innovations that reduce the need for labour inputs might negatively affect employment levels, or require higher skill levels and workforce training, while new product innovations with high levels of success might increase production and stimulate demand for skilled
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employees. The relationship between innovation, skills and employment, however, is subject to ambiguity and debate in the literature in this regard (Pianta, 2005; Tether et al., 2005).
Nevertheless, weighted indicators may be of particular use for understanding shifting skill requirements, as high performance on weighted innovation indicators can direct attention to sectors where the rate of technological change might yield wider impacts on employment. For example, natural resources has the highest weighted share of novel process innovation (58.2%). This suggests that firms with novel process innovation tend to be larger, and technological change impacting on processes in natural resources might have a wider impact on employees in this sector than such changes in other services.
4.3.2 SUMMARY DISCUSSION
This section sought to address the research question by briefly considering how indicators based on existing survey data might improve understanding of innovation impacts. This was approached using employment weighting for selected novelty indicators, drawing on recent measurement related literature (Bloch et al., 2008; Bloch and Lopez-Bassols, 2009).
Employment weighting increased the share of innovation for each innovation indicator, and results provide an indication of the size distribution of activities across firm populations. This may have policy relevance in terms of indicating potential impact and reach of innovation on the labour force, with implications for skills needs based on technological change. These indicators could be useful for informing policies directed at firms of a particular size range for instance.
In summary, this brief section answered the research question by showing how weighted indicators can provide an indication of the distribution of impacts within different firm populations, and these are used to complement complex indicators presented in the following chapter.
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