The rest of the paper is organized as follows. We describe our data in section 2. We discuss specification, identification, and estimation issues in section 3. Details of estimation results are presented in section 4. Section 5 concludes.

# **2.  Data: regional gaps in performance and institutional environment**

The data on which we have estimated business growth equations come from the 2002 and 2005 waves of the FACS survey. The 2002 wave covered some 1856 predominantly small and medium sized enterprises sampled from 11 two-digit industries and 40 cities in 12 states. The states were Andra Pradesh, Delhi, Gujarat, Karnataka, Kerala, Maharashtra, Madhya Pradesh, Punjab, Tamil Nadu, West Bengal and Uttar Pradesh. The industries were garments, textiles, leather goods, pharmaceuticals, consumer electronics, white goods, machinery, auto parts, metal products, chemicals and plastics, food processing. The 2005 wave successfully revisited 975 of the enterprises surveyed in the 2002 wave and expanded the full sample to 2287 by including more than a dozen new cities from four additional states. The newly added states were Bihar, Jharkhand, Orissa, and Rajasthan. Although the survey instrument diverges significantly between the two waves, there was large enough overlap to generate a panel dataset on a wide range of economic indicators and business environment variables. Also, each wave collected production statistics and financial data on the preceding three years. The length of the panel for production statistics is therefore six years rather than two. It is this aspect of the data that we have exploited in estimating a dynamic specification of sales growth.

In order to bring regional differences in industrial performance and business environment into sharp relief, we have classified the 16 states covered by the FACS survey into a smaller set of categories based industrial development and growth. The idea is to estimate the relationship between growth and business environment variables for each category separately. This allows us take into account the possibility of the heterogeneity or effects of institutions across regions. It is also an essential element of our strategy for the identification of the effects of access to finance on growth. The strategy relies on our interpretation of the coefficient of financial variables in the estimated growth equation. While, there are no unambiguous rules for identifying such effects in the context of a single growth equation, identification is possible through cross equation comparison of coefficients of financial variables.

In view of the small size of the over all sample, we would like to keep the number of categories to the minimum so as to maximize the degrees of freedom available for inference on the sub sample within each category. We also would like to make our categories match as much as possible those already established in the policy literaturethat is, with what are already grouped as ‘lagging states’ and those called ‘advanced states’. Following Purfield (2006) we make the classification in two dimensions: level of development and recent growth performance. We measure level of development by per capita income in 1994 and recent growth performance by average annual growth rate in real GDP in the first decade since then. We divide states first by level of development into high-income states and low-income states with the average state level per capita GDP of 1994 as the dividing line. Each of these two groups is then further classified into 5