Data description Dependent variable

Our variable of interest is the number of member firms listed in the directory of the National Association of Software and Services, NASSCOM (as of September 2003) in metropolitan areas (IT). The number of such firms in the full sample is 854 dispersed over 35 locations; however, this sample has to be reduced by a number of firms. For eight firms no exact city location is mentioned; moreover, six locations with only one firm entry are deducted, not for having one firm only, but because they are rather small cities and lack a coherent set of other data. In

one case (the state of Chattisgarh) the state has been recently spun off from another so that no other data are available. Another location (Chandigarh) is simultaneously the capital of two states; hence its seven firms will be removed.

The remaining sample of firms to be used includes 838 firms concentrated in 27 locations. Once we account for the actual number of IT firms in these locations a concentration in even fewer cities is evident. (see map 1)

--- Map 1 about here

--- Independent variables

There are three groups of independent variables representing the components of a basic production function, i.e. capital, labor and institutions. For technology entrepreneurship a specific form of capital is relevant: venture capital. Similarly, with most technology firms being engaged in some form of knowledge-intensive industry, the most relevant form of labor is well educated human capital. One of the main findings from my fieldwork interviews was that Indian software firms do not only look for well-educated manpower, but they specifically target predominantly engineering graduates.

Regarding production technology or institutional framework the issue of identifying appropriate measures is a rather daunting task. Here, the focus is on two variables that have been identified as potentially influencing location decisions, both in theory and practice; and that belong to the rather heterogeneous (and eclectic) construct of institutions: ethnic diversity and cultural openness in terms of gender diversity.

Financial (Venture) Capital

While India has not (yet) reached the stage of big VC industries like US, Western Europe or East Asia, growth from 1998-2002 sees India with the highest increase of all countries with 82% (IVCA, 2004). Data for venture capital have been taken from various sources. The numbers of VC investments (VCINV) at state level in

1998 is from VCline; the number of firms (VCFIRM) is counted at city level and taken from NASSCOM in 2003. One potential endogeneity problem with venture capital in this context is that in India VC might have been attracted to already existing IT clusters. However, this might be more prevalent to foreign VC investors who, interestingly enough, are almost entirely registered with the relevant Securities and Exchange Board of India (SEBI) under a Mauritian address, even though names like Citigroup Venture Capital International or Intel Capital suggest a different country of origin (SEBI, 2005).

Human Capital- Engineering Education

Probably the best indicator for the availability of human capital, or a pooled labor market would be some kind of employment data (Dohse and Schertler, 2003).

Unfortunately, such data are not available – yet; therefore I had to find some approximation for available labor force. As suggested in interviews, human capital is measured as university graduates, more specifically as engineering education, not the more generic literacy or university graduates. This is based upon fieldwork interview findings where in most cases the response on hiring practices was that specialized computer classes are much less valued than a broader technological education in engineering. Here, I deploy statistics from the Ministry of Education and the Census of India 2001. Interestingly, not only is the share of engineering enrolment higher in states that have a larger share of IT and high-tech FDI. More importantly, the difference between the share in engineering enrolment and the share in the national population is revealing (EDURENT).

Similar to Arora et al. (2004) but on the more disaggregated state level, I find those states more actively involved in IT exhibiting higher positive ’education rents’.

Institutions – Diversity

It is rather difficult to find suitable variables representing openness, or for that matter, tolerance. Two indirect measures are used as approximations: ethnic diversity and gender diversity. As a proxy for ethnic diversity we take the number of peoples speaking the main language in the state (from The Joshua Project,);

India is a multi-ethnic society with more than 15 official languages with their own script, hence extremely multi-linguistic. This multiplicity of languages can be

seen at the state level too. We maintain that the higher the number of different groups speaking the main language (or a dialect thereof) indicates a more diverse society (LANG). In order to allow for the non-monotonic effect, ethnic diversity was modelled as a quadratic function (LANG2).

Furthermore, openness, or tolerance, is approximated by gender diversity – the percentage of female enrolment in higher education; not only in engineering but all university enrolment (ENROLFEM). Again, data come from the Ministry of Education and the Census of India 2001. Moreover, there is some anecdotal evidence for cities such as Bangalore being very cosmopolitan, but no data were available to test these assertions.

Control variables

In order to rule out alternative explanations, I controlled for size of the city population and the regional economy. In concordance with literature on urbanization economies (Jacobs, 1969; Glaeser, 1999) I control for city size measured as population (POP) at the metropolitan level. Data are for 1996 have been taken from United Nations statistics division. Another traditional control variable measures GDP. Although this is not very appropriate in the context of an export-oriented or rather export-dominated sector, I use this control for the size of the regional economy. Numbers for state level GDP are for fiscal year 2002-2003 and are taken from the Government of India’s Economic Survey (GSDP).

Arguably, replacing GDP with a measure of software exports as a control approximating pull factors attracting new entrepreneurs into this industry might be more appropriate.

Tables 1 and 2, respectively, present descriptive statistics and pairwise correlations for the variables.

--- Tables 1 and 2 about here

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