Upgrading Channels of Indian IT Clusters

Clusters – and cities with clusters - are long established in the literature as important places for learning, innovation and economic development (Glaeser, 1999; Porter, 1998; Maskell and Malmberg, 1999). Besides the traditional Marshallian externalities, external economies like knowledge spillovers (Almeida and Kogut, 1999) derive from collective efficiency, social capital or some other form of social cohesiveness (Uzzi, 1997; Nahapiet and Ghoshal, 1998). There is evidence from very heterogeneous developing country clusters having faced and mastered economic challenges that the higher the level of local cooperation the higher the success and future performance. Social networks and ties at the local level allow for collective social action and knowledge spillovers that lead to the collective efficiency emphasized by Schmitz (1995) as an important ingredient of cluster upgrading.

A significant role is played by geographical and social proximities: firms and people located in the same region possess some form of a shared culture or collective identity. Geographical proximity enables the creation of common cultural contexts which, in turn, facilitate interactive learning processes crucial for innovation, because regional cultures tend to become institutionalized as rules of conduct which govern the relations and interactions of economic agents within the geographical area (Dosi, 1988; Storper, 1995; Lorenzen & Mahnke, 2004, forthcoming). Other authors stress the importance of learning from competitors in clusters (Malmberg and Maskell, 2002) and the predominance of vertical links (Humphrey and Schmitz, 2000).

According to Humphrey and Schmitz (2000) there are four types of upgrading: 1) process upgrading, which is a more efficient way of production, 2) product upgrading, which means selling similar products in higher market segments, 3) functional upgrading, that is assuming roles with a higher value-added within the production process, and 4) intersectoral upgrading, using competences acquired in one sector for production in other sectors. Typical upgrading channels are all kinds of (knowledge) spillovers, for instance, the mobility of human capital (Franco and Filson, 2000), spin-offs (Klepper, 2001), or knowledge transfer from MNCs (Fromhold-Eisebith, 2002). Schmitz (1995) introduced the concept of collective efficiency, defined as the competitive advantage derived from local external economies and joint action in order to assess the impact on the competitiveness of firms located in clusters. This implies that the capabilities that are required to upgrade are supposedly built up through local processes. It is the concept of absorptive capacity that analyzes the “ability to exploit external knowledge” (Cohen and Levinthal, 1990, 128). Originally developed for analysis at firm level, this concept has been extended to the analysis of countries – developing countries in particular – where education and infrastructure are among the most critical factors (Dahlman and Nelson, 1995) and clusters (Giuliani, 2004, forthcoming).

The success of the Indian software industry is well-researched, with factors like first-class higher education and research institutions, both public and private, low labor costs and stimulating policies commonly accepted as systemic components.

Since the late 1990s, an increasing number of studies on its upgrading potential has been published (e.g. Krishnan and Prabhu, 2002; D’Costa, 2002), which usually focus on functional or product upgrading. Nowadays, MNCs increasingly locate not only low-level programming but also research and development (R&D) centers or laboratories in India; many already have more than one research lab (Fromhold-Eisebith, 2002). Generally, the quality of software-exporting firms is assessed at high levels. Nevertheless, the innovative capabilities of the industry are viewed rather skeptically as being still rather low in the value chain (Arora et al., 2001, Tschang, 2001).

India has reached stage 4 of Yourdon’s (1992) “stages of development”-model (see figure 1). However, this kind of upgrading is still rather of the product or functional type. Bhatnagar and Madon (1997) argue that reaching a higher stage

necessarily requires certain technological competencies as well as an understanding of international markets. Furthermore, they attribute the maturity the Indian software industry has already achieved to acceptance and reputation in international markets (cf. Banerjee and Duflo, 2000), being endowed with technical competence and capability building among other factors (Fromhold-Eisebith, 2002; Taeube, 2004a).

Stage Objective Description

1 Build reputation Low value-added body shopping

2 Onshore to offshore Offshore customized software development 3 Improve value addition Starting up offshore package development 4 Product development Total offshore product development

5 Innovation Identify new software-intensive products

Figure 1: Stages of Development (Source: Yourdon, 1992)

A relatively recent tendency in the software industry is to venture into Business Process Outsourcing (BPO) which sometimes even refers to processes at the core of a firm’s activities. It started with captive centers founded by MNCs which basically converted data from one kind of medium (paper) to another (digitized) (Aron and Singh, 2003). This involves a high degree of human intervention, since in many cases the documents cannot be reasonably transformed without interpretation. Thus, it also embodies a certain extent of learning (by doing) and capability building in terms of client-specific as well as more generic project-management knowledge (Ethiraj et al., 2004). Financial services already compose the largest part of the business of Indian software companies and are still gaining in its share; in 2003 the financial sector made up for about 39% of the software industry revenues, followed by manufacturing (12%) and telecommunication (9%) (Nasscom, 2003). Having accumulated knowledge and capabilities through supplying intermediate, rather technical, inputs to the financial services industry some of the companies venture into new domains by providing financial services themselves (Economist, 2004, page 9). We assess this potential for upgrading existing IT clusters in India intersectorally in order to undertake financial

research, which would provide another opportunity for some parts of the developing world.

6.4 Methods

The empirical evidence for the analysis presented here draws on qualitative interview data as well as on quantitative data on Foreign Direct Investment (FDI) transactions of financial firms in India. Information pertaining to analysts stems from the findings of interviews carried out during certain former research projects of the authors on the spatial organization of the financial industry (see Grote, 2004; Grote et al., 2002; Lo and Grote, 2002; Taeube, 2004c). Six in-depth interviews with analysts that lasted from one to two and a half hours were conducted between Winter 2003 and Fall 2004 in Frankfurt. The interviews were open ended; notes were taken during the process. The interviewees are research analysts and senior analysts in investment banks based in Frankfurt and London.

The interviews focused on the frequency of contacts to any other actors and information sources and the communication method used. We do not claim to provide a representative overview in the statistical sense. However, we asked the interview partners to reflect on other industry practices and former work experiences, which yielded no different results. This part of the survey is not intended to furnish a quantitative analysis of the spatial restructuring of financial research activities, but to reveal the underlying rationales, possibilities and targets for outsourcing and offshoring different research activities in banks.

The interviews with financial analysts are supplemented by background information from 33 semi-structured interviews with senior executives of IT firms, universities and public sector entities conducted in Bangalore and Mumbai in Fall 2003. The questions in these interviews were centered on general business and company information, social, regional and international networks, and policy.

The qualitative evidence from 16 semi-structured interviews with senior executives of small, medium, and large Indian IT companies in Frankfurt conducted in October and November 2002 complements the findings from Bangalore (see Täube, 2004c).

In order to test the hypothesis whether Indian software clusters are attractive destinations for offshoring such activities by means of FDI, a multivariate

regression analysis is used. Foreign direct investment can either take place as Brownfield (M&A) or as Greenfield investment; we have compiled data on both.

Data on Brownfield investment of financial firms in India are regressed on several characteristics of target locations. The results show that M&A activities of financial institutions are highly concentrated in very few cities with IT clusters in India. Since the data set does not allow for distinguishing between offshoring and other investments, self-collected newspaper articles on Greenfield investments in India by banks corroborate the evidence from the regression analysis.