determined by aggregate creation in the economy by incumbent firms and new entrants. Firms exit when all of their products are destroyed. There is no re-entering once exit occurs.

Based on this setup, dynamics of firm evolution in a stationary equilibrium is modeled as follows. A firm of efficiency type , with a current flow of profits n , faces a Poisson hazard  _n_ of losing a product. By spending in R&D, it influences the Poisson hazard _I_ of becoming a firm with n+1 products. The firm chooses optimal amount of R&D to maximize its expected present value _V_  _n_ . Bellman equation for firm’s dynamic optimization problem is presented below _rV_  _n_  max  0  _n_  _wc_  _n_   _n_  _V_  _n_  1  _V_  _n_  _n_  _V_  _n_  _V_  _n_  1  , where _r_ is the interest rate and _w_ is wage rate. The value function is linear in _n_ which allows us to get an analytically tractable solution to the problem. The solution of this problem which is reproduced in equation 4 is derived in Seker (2009). Lentz and Mortensen (2008) solve the equilibrium for a slightly different setup. In the equilibrium condition presented in equation 4 optimal amount of innovation rate  is determined by setting marginal cost of innovation equal to marginal benefit

> Firms with high values of  introduce new products at a faster rate and they grow faster. Analyzing equation 4, we see that higher profit levels shift up marginal benefit of innovation and lead to higher

> innovation rates (by increasing )<sup>4</sup> . In equation 2, profit level increases in efficiency level. Thus efficient firms innovate at a faster rate. Lower cost of R&D decreases marginal cost and increases marginal

> benefit of innovation and also results in higher values of .

The model allows for entry. From a constant potential pool of entrants, successful ones enter the economy as a result of introducing a new variety. Firms discover their efficiency types immediately after they enter. Entrants face the same innovation cost function as incumbent firms. Although entering and exiting firms contribute to reallocation of resources and aggregate innovation in an economy, Prowess database does not allow us to analyze their dynamics. Firms can exit and re-enter the database. Since it is not possible to identify new entrants and actual exiting firms, the empirical analysis does not discuss the contribution of entrants and exiting firms to aggregate innovation. Goldberg et al. (2010b) also use the Prowess database and provide a similar discussion regarding to entering and exiting firms.

# **III. Data and Policy Reforms in India**

## **Data Description**

Firm level data used in the analysis is obtained from Prowess Database which is constructed by Centre for Monitoring the Indian Economy (CMIE) in India. This dataset has advantages over the Annual Survey of Industries (ASI) which is the India’s manufacturing census. ASI is constructed from a repeated cross section of firms whereas Prowess database includes a panel of firms. The panel feature allows us to tract firms over time. It is also among the few databases that records annual information on firms’ product

> 4 A formal proof of this relationship is presented in Klette and Kortum (2004).

5