mix. Hence product creation and destruction can be tracked at firm level. This unique feature of the data allows me to evaluate the relationship between firms’ adjustment of their product lines and policy changes.

There are several innovation strategies that firms can follow. They can introduce process innovation by significantly improving their production processes or they can introduce product innovation by introducing a new product variety that they did not produce before. Studies like Bertschek (1995), Parisi et al. (2006), and Fritsch and Meschede (2001) highlight the differences between these two innovation strategies. The model introduced in this study and the data from Prowess are appropriate for analyzing product innovation strategy which will be measured by the change in product mix of firms.<sup>5</sup> The database contains information on income statements and balance sheets of relatively large Indian firms from 1989 to 1995. This dataset is well suited for the particular purpose of the study as large firms contribute more to aggregate product creation compared to small firms. As discussed in Goldberg et al. (2010b) the dataset is not well suited to analyze entry and exit dynamics because firms are not obliged to report to the data collecting agency. Prowess database accounts for 60% to 70% of the economic activity in the organized industrial sector. The panel is unbalanced and the number of firms included in the data increases from 752 in 1989 to 2293 in 1995.

In the data product level information is available for around 85% of the firms in manufacturing sector. Products are defined according to CMIE’s internal product classification. Goldberg et al. (2010a) present a detailed description of the data, product classification, and product mix change. They identify 1,886 products linked to 108 four digit 1998 version of National Industrial Classification (NIC) industries in manufacturing sector and they find that products that can be mapped to four or five digit NIC codes account for 99 percent of total output. They also show that the number of products classified in India is quite comparable to the number found in Bernard et al. (2006) for US manufacturing sector.

The Prowess database has been used in a series of papers by Goldberg et al. (2010a, 2010b), Chari and Gupta (2008), and Krishna and Mitra (1998) among many others. From this database I use the number of products owned by a firm, total sales, and an indicator for firms that spent on R&D. Descriptive statistics for these variables are presented in Table 1. An average firm has 1.9 products and average product growth rate in the economy is 6%. The data shows that average product addition is more frequent than product dropping. A discussion of this high product creation is presented in Goldberg et al. (2010a). Finally 9% of the firms in the data invested in R&D. The table also shows the overall variation, over time variation for each firm (within), and variation between firms (between) for each variable. Between firm variation is higher than within firm variation for the number of products which is expected due to short time period. For product growth, there is reasonable variation in both over time and between firms. Multi-product firms make around 45% of the firm population in the data. An average multi-product firm owns around 3 products and these firms make around 80% of total sales. A descriptive analysis of evolution of multi-product and single product firms is presented in Table 2. The table shows the percentage of firms that showed various product evolution patterns annually, over

> 5 The model can be easily expanded to include process innovation by adding a quality attribute to the products innovated and firms can be allowed to invest in both quality improvement and product innovation. An older version of Seker (2009) which is available upon request provides such a setup.

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