**1 Introduction**

Recent literature has shown that 1I.tiis and economic density are important drivers of productivity and economic growth (e.g., Ciccone<sup>andHall</sup><sup>**(1996),**Cingano andSchivardi</sup> (2004), Lall et al. (2004), Brulhart and Sbergami **(2009)).** However, the clustering of production may lead to increased regional concentration of income if labor is imperfectly mobile (Hanson and Harrison **(1999),** Topalova (2004)). This in turn may lead to a regional concentration of poverty and increasing inter-regional disparities, particularly when labor mobility across regions is limited. Both these factors are extremely relevant for India where the spatial distribution of manufacturing has several interesting and peculiar features.' Firstly, the distribution of industries across Indian states is highly skewed. In **1985,** the locational gini coefficient for Indian manufacturing was **0.7** as compared to **0.25** for **(Ili1. %.** manufacturing.<sup>2</sup> Secondly, the spatial distribution of manufacturing industries in India experienced significant changes in the 1980s and 1990s. The growth rate of the various states' share of manufacturing employment exhibits differing trends not only across the two decades but also across smaller sub-periods. Thirdly, there is significant heterogeneity in **-** 1- ation patterns across Indian industries.' Fourth, from **1950** to **1990** industrial policy **-** in particular, licensing **-** was explicitly used **by** the government to make the<sup>spatialdistribution</sup> of industry "less unequal". These features gain even more significance in face of the major 'Regional trends in **in, 1 dif-.** in India have been a cause of concern, particularly in view of the massive economic changes the country experienced since the mid-1980s. Deaton and Dreze (2002) show evidence of divergence in per capita consumption across states and of growing urban-rural inequalities, both within and across states. See Pal and Ghosh **(2007)** for a detailed review of evidence of increasing regional disparities in India.

> 2For each country, the locational gini coefficient measures the in, **I.11** _i_ **-.** in a measure of regional specialization of industry **_j_** and its formula is given **by qj** **___** _Ginij(rj,)_ where _rj,_ z **_,L_** 1 L3 with ~ **_Lj,_** beingin

employment of industry j in geographic area s and _L,_ being total manufacturing employment in geographic area _s, s_ = **1, ..M.** The coefficient for China is taken from Ge **(2009)** while that for India is the average across the Gini coefficients for 2-digit industries calculated **by** the authors.

'Unreported figures (available from the authors) show that for example employment shares in the wool, silk and man-made fiber industry **(NIC** 24) grew at fairly similar rates across Indian states between **1980** and **1990** providing no evidence of increasing agglomeration. However from **1990** to **1999,** most states experienced negative growth rates while a handful experienced extremely high positive growth rates of employment share of this industry. Thus the industry agglomerated from **1990** to **1999.** On the other hand, it is difficult to say whether employment in the chemical and chemical products industry **(NIC 30)** became relatively more agglomerated during the 1990s compared to the 1980s. What happened was a change in the **i.1], if-.** of the states with high growth rates of employment share of this industry from 1980s to the 1990s.

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