Figure 4. Information sources for innovative firms <!-- Start of picture text --> Most important source of information for innovation<br>70% 63%<br>60% 52%<br>49% 49%<br>50% 42%<br>40%<br>27% 26% 26%<br>30% 23%<br>19%<br>20% 11%<br>6%<br>10% 0%0.1% 2.5%3% 1.9%0.1% 0%0.6%<br>0%<br>Bangladesh India Nepal Pakistan<br>In‐house Private Sector Government Academia Customers<br><!-- End of picture text --> Source: Author’s elaboration from Enterprise Survey (2014)

# 5.7 Innovation Patterns across Regions

It is widely recognized that innovation activity is spatially concentrated, and that R&D investments and innovations are more spatially concentrated than employment (see Carlino and Kerr, 2014 for an excellent empirical and theoretical review). In terms of outputs, the creation of new products is more spatially concentrated than the introduction of new patents (Acs et. al., 1994); while the origination of the latter tends to be concentrated in few and large cities (Forhnahl and Brenner, 2009; Bairoch, 1988, respectively). Theories explaining concentration of R&D and innovation activities allude to agglomeration advantages in three dimensions: (i) sharing of common specialized inputs and factors of production, (ii) improvement of the quality of the matching process between specialized inputs or factors of production and innovating firms, and (iii) presence of externalities such as knowledge spillovers that facilitate the flow of tacit knowledge and new ideas.

Why is innovation spatially concentrated? “Sharing” theories highlight the importance of thick local factor or input markets that allow innovating firms which are spatially concentrated in a particular location to have access and share specialized workers or inputs that are essential for innovative activities (Porter, 1990); reducing at the same time production costs (Porter, 1998). “Matching theories” rely on the argument that agglomeration is associated with thicker markets; which in turn, increases opportunities for specialized workers, reduces their waiting costs of finding the best partner, and raises the likelihood of assortative matching with positive effects in terms of innovation success. “Knowledge-spillover theories” rely on the characteristics of knowledge as a public good, that is non-rivalry and non-excludable, to emphasize the benefits of agglomeration in terms of facilitating the flow of new ideas, which are an important input for innovation.

A comparison of innovation rates by region shows a lot of variation in innovation patterns across different areas within a particular SAR country (Table 17). In Bangladesh, Dhaka and Chittagong are the most innovative regions, with rates of technological innovation near 80 percent; while Chittagong and Khulna are innovative in terms of non-technological innovations. In India, there are five regions with innovation rates above 90 percent for product and/or process innovation: Andhra Pradesh, Arunachal Pradesh et al., Bihar, Punjab, Rajasthan, and Uttaranchal; while organizational innovation rates fluctuate between 31 percent in Rajasthan and 74 percent in Andhra Pradesh. In Nepal, the West area presents the highest innovation rates for both technological and non-technological innovations; while Sindh and Punjab in Pakistan display the highest innovation rates.

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