metadata: authors: 0: author: Fernandes,Ana Margarida
1: author: Sharma, Gunjan
count: India
docna: 0: docna: Together we stand ? agglomeration in Indian manufacturing
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,State Owned Enterprise Reform,Public Sector Administrative & Civil
            Service Reform,International Trade and Trade Rules,Business Cycles and Stabilization Policies,Transport Services,Democratic Government,Economics and Finance of Public
            Institution Development,Public Sector Administrative and Civil
            Service Reform,De Facto Governments
teratopic: Private Sector Development,Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Water Resources
abstracts: cdata!: This paper uses plant-level data to
            examine the impact of industrial and trade policy reforms on
            the geographic concentration of manufacturing industries in
            India from 1980 to 1999. First, the research shows that
            de-licensing and liberalization in foreign direct investment
            significantly reduced spatial concentration, but trade
            reforms had no significant effect on spatial concentration.
            Second, plants respond differently to policy reforms based
            on their size. Liberalization in foreign direct investment
            and de-licensing caused small plants to disperse, while
            trade liberalization had the opposite effect. However, for
            large plants trade liberalization led to lower spatial concentration.
geo_reg_key: 517191,517198,517212
repnme: repnme: Together we stand ? agglomeration in Indian manufacturing
display_title: Together we stand agglomeration in
            Indian manufacturing
listing_relative_url: /research/2012/05/16258556/together-stand-agglomeration-indian-manufacturing
topicv3: State Owned Enterprises m1326323 721,Manufacturing Sectors m1326521 1281,Transport m1326661 135,Trade m1326924 698,Industry Policy m1327403 1075
publishtoextweb_dt: 2012-05-01T00:00:00Z
historic_topic: Private Sector Development,Macroeconomics and Economic Growth,Industry,Social Protections and Labor,Water Resources
no_of_pages: 61
author_keys: 0: author_key: 658876
model_extractions: **POLICY RESEARCH WORKING PAPER 6062**

# **Abstract**

This paper uses plant-level data to examine the impact of industrial and trade policy reforms on the geographic concentration of manufacturing industries in India from **1980** to **1999.** First, the research shows that delicensing and liberalization in foreign direct investment significantly reduced spatial concentration, but trade reforms had no significant effect on spatial concentration.

Second, plants respond differently to policy reforms based on their size. Liberalization in foreign direct investment and de-licensing caused small plants to disperse, while trade liberalization had the opposite effect. However, for large plants trade liberalization led to lower spatial concentration.

Tbis paper is a product of the Trade and Integration Team, Development Research Group. It is part of a larger effort **by** the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at afernandes@worldbank.org and sharmag@missouri.edu.

> r _he Policy Research Working Paper Series disseminates thefindings ofwork inprogress to encourage the exchange ofideas about development issues. An objective ofthe series is to get the findings out quickly, even ifthe presentations are less than fully polished. The papers carry the names ofthe authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those ofthe authors._ **_They_** _do not necessarily represent the views ofthe International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those ofthe Executive Directors ofthe World Bank or the governments they represent._

Produced **by** the Research Support Team
# Together We Stand? Agglomeration in Indian

# Manufacturing

## Ana M. Fernandes*and Gunjan Sharnat

**JEL** classification:F10, **F15,** 02, R12. Keywords: Trade, Foreign direct investment, delicensing, a , **i:i,** spatial distribution, India

> *World Bank Development Research Group, _af_ ernandes(Dworldbank. org tUniversity of Missouri, sharmagOmissouri. edu

ýSupport froin the governnents of Norway, Sweden and the United Kingdon through the Multi-Donor Trust Fnd for Trade and Development is **-** 1 **0F,fill·.** acknowledged. **We** would like to thank Devashish Mitra and Hiau Looi Kee as well as seninar participants at Uni-, i-i -. of Missouri, the Midwest Trade Meetings, the **IGC** Urbanization Conference, the North Anerican Summer Meeting of the Econometrics **q..** i, **I -.,** and the World Bank. The findings expressed in this paper are those of the authors and do not i., **- --** I; represent the views of the World Bank.
**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.

2
industrial and trade policy reforms and the general shift towards market-oriented economic policies that occurred in the 1980s and 1990s in India. Theory does not offer clear-cut predictions regarding the effect of industrial de-licensing, trade liberalization, and foreign direct investment reforms on spatial concentration of manufacturing industries, and hence the need for empirical analysis.

In this paper we ask the following questions. What are the determinants of the high level of spatial concentration in Indian manufacturing? How much of the changes in the spatial distribution of Indian manufacturing industries can be attributed to policy reforms, after conditioning on the traditional determinants on 1 1 - endowments, Mlarshalliantype linkages and knowledge spillovers? Does the spatial distribution of manufacturing plants of different sizes differ in response to policy changes? To answer these questions, we focus on indices of geographic concentration of manufacturing industries in India constructed following Ellison and Glaeser **(1997) -** henceforth **EG** indices **-** based on plant-level data from the Annual Survey of Industries **(ASI)** for the period **1980-99.** Our results show that industrial de-licensing has a significant negative impact on geographic concentration levels (a one standard deviation increase in the proportion of output de-licensed leads to a 9.; V decline in the **EG** index) and the same is true for foreign direct investment (FDI) liberalization (with a one standard deviation increase in the proportion of industry output open to FDI reducing the average **EG** index **by** 10. ). However, our estimates also show that trade policy has no effect on the spatial concentration of Indian manufacturing. Further we find that there is significant size-based variation in the response of concentration to the policy as well as the traditional determinants **of** :w1iii i:f1ij. Delicensing and FDI liberalization cause small plants to disperse. Trade policy has no effect on the concentration of medium-size plants but a one standard deviation decline in the effective rate of protection leads to a decline in the **EG** index of large plants and a 2.1 **"** increase in the **EG** index of small plants. Our results are robust to heteroskedasticity-correcting **FGLS** techniques and controls for persistence in the h111 i..ij process as well as to a wide variety of fixed effects and specification tests.

**A** large literature provides evidence of a significant positive effect of :' economies on firm-level as well as iiregate-level productivity. Thus, our finding that policy reforms

**3**
in India raised geographic dispersion would imply that there were productivity losses (or at the very least, lower productivity gains as a result of the reforms) in Indian manufacturing. This implication of our results could be one explanation for the finding that only a quarter of the Indian growth miracle can be attributed to the policy reforms shown **by** Bollard et al. (2010). That is, the tendency of Indian manufacturing to disperse after trade and industrial policy reforms reduced its ability to take advantage of 1.I.-rti:f1i economies, and hence reduced regate productivity growth.

Our study's contribution to the literature is three-fold. First, our index of **:1I.-rti:fvij** - the **EG** index **-** provides a detailed picture of spatial concentration at a micro level. Since it is computed at a relatively disaggregated 3-digit level of industries across Indian states it avoids problems associated with plants changing product mix (and hence, changing<sup>their industrial</sup> classification which would occur if we considered 4-digit industries), and problems associated with very small geographic units.<sup>4</sup> Second, our study is one of the first that explicitly controls for and measures the impact of three different policies **-** industrial de-licensing, trade reforms, FDI liberalization **-** in addition to controlling for public sector presence in Indian manufacturing. Third, unlike other studies which focus on cross-sections or very short panels, our data spans a **19** year period for approximately **170** industries. Hence, our measures of industrial and trade policy as well as the proxies for the traditional determinants of concentration cover a long and interesting time span. They allow us to present a complex and nuanced picture of the forces that affect spatial concentration. Moreover, the use of a long panel of industries allows us to control for industry and time fixed effects in our specifications. Also we are able to lag explanatory variables to account for the slow moving nature of the determinants of 1.I.-r i..ij. We account for serial correlation **by** estimating clustered standard errors. Lastly, to the best of our knowledge our study is the first to consider **EG** indices of - **h 1 1**<sup>**.tii**fordifferentplantsizecategories.</sup> The paper is organized as follows. In Section 2 we discuss the measurement of :- **i-** eration and the evidence on the traditional determinants of :-1iii i..i. In Section **3** we discuss the policy determinants of :w1iim .ti - de-licensing, trade reforms, and FDI

> 4

> According to Mayer and Mayer (2004) the use of very small geographic units "can lead to an underestimation of agglomeration levels since it **:,** i **It-1.** separates clusters that sprawl across the border between units".

4
liberalization. In Section 4 we describe and summarize our data. Section **5** presents the estimation strategy while Section **6** discusses the results. Section **7** discusses the results using plant-size-based measures of : h1 **.i:'fji.** Section **8** presents the results from alternate specifications. Section **9** concludes.

# **2 Agglomeration: Measurement and Traditional Determinants**

## **2.1 Measurement of Agglomeration**

Several indices can be used to measure the geographic concentration of an industry within a country: e.g., locational Gini coefficients, Hoover indices and Theil indices. Suppose that **s5j** is the share of geographic area **s, s** **_=_** 1, _..M,_ in industry j's total employment in the country that is, **sj =** . Further, x, is the share of geographic area **s** in total manufacturing employment in the country, that is, _x,_ **_= L._** The first measure we consider is a raw geographic concentration index for each industry **_j_** in a given time period which is given **by** **_Gj =_** EM **_(sj, -_** _x,) 2 ._ The index measures the degree to which the geographic pattern of employment in the industry departs from the geographic pattern of manufacturing employment in the country as a whole. The index takes a value of zero if industry employment is uniformly spread across geographic areas. Greater values of the index indicate relatively stronger geographic w 1 i of the industry. **A** potential problem with this raw geographic concentration index is that even if plant location was chosen completely at random it would **by** definition be larger in industries including only a small number of very large plants. Thus **Gj** could be large solely due to industrial structure.

Hence, the second and main measure we consider is the **EG** index proposed **by** Ellison and Glaeser **(1997)** which controls for the number and size distribution of plants, i.e., the impact of the industrial structure. The **EG** index is the most commonly used index in the 1.-issi i: literature. Ellison and Glaeser **(1997)** develop a plant location choice model to analyze geographic concentration at the industry-level that captures both the random ~i1i :1tij- that would be generated if plants chose their location **by** throwing darts on

**5**
a board as well as additional **i l.in-:itiij** caused **by** localized industry-specific spillovers and natural advantages. Drawing upon their model they propose an index of geographic concentration of employment in an industry that controls for industry characteristics, namely for the size distribution of plants in the industry.' Indeed, one of the key advantages of the **EG** index is that it allows for cross-industry comparisons.

Considering the aforementioned raw geographic concentration index **Gj** for industry _j_ and its plant size distribution as measured by the Herfindahl index, **_Hj = I_** 1<sup>_z_</sup><sup>**,**where</sup><sup>**zij**</sup> is plant i's share in employment of industry **_j,_** the **EG** index for industry **_j_** in a given time period is defined **by** Equation **1:6** The subtraction of the term **(1** - **_EMIx2)Hj_** in Equation 1 accounts for the aforementioned problem that **Gj** would be larger in industries with just a few large plants even if plants chose their locations randomly. That term corresponds to the level of concentration that would be expected (in the model of Ellison and Glaeser **(1997))** if plants in the industry chose their locations independently and randomly and both industry-specific spillovers and natural advantages played no role. Therefore, the **EG** index measures the concentration of employment in an industry over and above the level of concentration that would have prevailed if plants were distributed completely at random. Greater values of the **EG** index are associated with greater geographic concentration of the industry.<sup>7</sup> Dumais et al. (2002) show that the index can be generalized to a dynamic setting. Note that we compute the Herfindahl index **_Hj_** based on our plant level data for Indian industries, rather than take it from external sources as in other studies. This is a particularly important advantage of

> 5The main prediction of the model is that the relationship between average levels of concentration and industry characteristics is the same whether concentration is caused **by** from spillovers, from natural advantage, or from a combination of both. This prediction implies that one can construct an index that controls for industry differences, regardless of the cause of concentration. 'The Herfindahl index is defined as usual covering all plants in the industry without a geographic dimen- **S10n. 'A** value of zero for the **EG** index indicates that industry employment is as concentrated as would be expected from a random location process. Ellison and ( I **-** i **(1997)** suggest that industries with **EG** index values higher than **0.05** are **highly** concentrated while those with **EG** index values below 0.02 are not very concentrated.

**6**
our index since it allows us to compute the Herfindal index at the same level of industrial disaggregation as the raw geographic concentration index **Gj.**

# **2.2 Traditional Determinants of Agglomeration**

Mayer and Mayer (2004) and Combes and Overman (2004) review in detail **a** large number of studies that analyze the determinants of :-se1 ii **ii** measured **by** the rate of growth of employment of an industry in a particular location or **by** some ;-1-1 ij index for an industry. These studies focus on knowledge externalities (Glaeser et al. **(1992)),** labor pooling (Overman and Puga **(2009),** Amiti and Cameron **(2007)),** input<sup>networks(Holmes</sup> **(1999),** Rosenthal and Strange (2001)), demand networks (Hanson **(1996),** Hanson **(1997)),** natural advantage, and increasing returns to scale (Kim **(1995),** Amiti **(1999),** Haaland et al. **(1999))** as factors that affect plant location. The intuition behind these various mechanisms for **1ii** is the following.

First, plants will tend to locate in areas that provide localization economies (for example, areas near raw material suppliers, areas with existing input networks, areas where the supply of the types of workers that the plant needs is plentiful, areas with good infrastructure) and urbanization economies (for example, areas where sectors and plants that are a source of demand for the plant's product are located). This means that an industry which is more reliant **on,** say, domestically purchased inputs, or on transportation will tend to locate in areas that have existing input networks. Thus such industry will tend to be more -1.n.tthan an industry that is less dependent on inputs and roads.

Second, industries with increasing returns to scale can lower their costs of production and raise productivity if they locate production in a few locations only. Hence, industries characterized **by** increasing returns to scale will tend to be more geographically concentrated than other industries. However Haaland et al. **(1999)** argue that while theory provides an unambiguous prediction that industries characterized **by** a higher degree of returns to scale will tend have higher levels of absolute concentration (i.e., when an industry is unequally distributed across geographic units), no such prediction holds for the case of relative concentration. That is, a higher degree of returns to scale may not affect how different the geographic spread of an industry is relative to the average spread of industries between geo-

**7**
graphic units. As Mayer and Mayer (2004) report, there is little evidence that the degree of returns to scale affects -p1ii . **irii.** In fact, Haaland et al. **(1999)** find that higher degrees of economies of scale are associated with lower relative and absolute concentration levels.

Third, plants will also choose location based on dynamic externalities **-** the Marshall **(1890),** Arrow **(1962),** Romer (1984) or MAR externalities. As plants tend to locate near one another, the extent of knowledge spillovers increases, leading to greater productivity of the industry. This results in a higher rate of growth of employment of that industry and that location. Other dynamic externalities include those modeled **by** Porter **(1991),** that relate the degree of competition in a location to higher innovation and hence to greater knowledge spillovers and greater concentration, as well as others modeled **by** Jacobs **(1969)** which relate variety and diversity in a location to greater knowledge spillovers, and hence to greater concentration in that location. Our specification will include controls for all these traditional determinants of :w1 i.ificpi. as described in Section **5.**

# **3 Policy Determinants of Agglomeration**

Before describing in detail the policy reforms in India, we discuss existing evidence regarding the explicit role of government policy for the geographic concentration of manufacturing. Lall and Chakravorty **(2005, 2007)** study the determinants of the location of new private and public sector industrial investments in India as structural reforms proceeded between **1992** and **1998.** Their results show that new private sector industrial investments were biased towards existing industrial clusters and coastal districts and depended on the size of the investments in the same location prior to the reforms and on the size of the investments in neighboring locations as reform proceeded. The authors infer that structural reform led to increased spatial inequality in terms of industrialization. These studies provide some interesting and **important** stylized facts about the location of manufacturing industry in India. However, due to the nature of the data used they are unable to analyze long-term patterns and determinants of '-g1iii n.-1i as we do in this paper. Bai et al. (2004) investigate the evolution of the Hoover index of :- :1 i constructed using **Cliij.** industrylevel data from **1985** to **1997.** They estimate the impact of local protectionism (i.e., the

**8**
fact that local governments shelter certain industries in the region from competition) on 1.i.t **i..** Their results show that industries with high past profit margins and high shares of state ownership are less geographically concentrated.' Lu and Tao **(2009)** address that same question using(Iiii. **%..** industry-level data from 1998 to **2005** and show that there is less geographic concentration in industries with higher shares of employment in state-owned enterprises. Fujita and Hu (2001) show an increasing income disparity between China's coastal and interior regions and attribute it to the clustering of manufacturing and the selfselection of FDI into coastal Special Economic Zones. Ge **(2009)** demonstrates that access to foreign trade and FDI is a driving force of unbalanced spatial distribution in China. In particular, industries dependent on foreign trade and FDI are more likely to locate in regions with better access to foreign markets.

Martincus and Sanguinetti **(2009)** estimate the impact of the interaction between tariffs and distance to the capital city on the regional employment shares of manufacturing industries in Argentina (for years 1974, **1985,** and 1994). They find that lower tariffs were associated with the de-concentration of manufacturing activities away from Buenos Aires and its surrounding region, which they interpret as indicative that with the opening to trade, demand and cost linkages weakened and **1.in-.tii** diseconomies such as high commuting costs or high land rents prevailed.<sup>9</sup> Using measures of openness based on trade volumes rather than trade policies, Martincus (2010) estimates a similar econometric specification to explain the distribution of Brazilian industries across states and shows that more open **industries** tended to locate in states near the country's largest trading partner Argentina, and this tendency increased with the trade liberalization of the 1990s. These studies demonstrate that industrial policies can have large and long term impacts on spatial concentration and, potentially, on interregional wage and income inequality. In the sections below, we describe the policies that were in place in India prior to the reforms and whose liberalization we expect

> 'Thei mechanism behind their findings is that in the fiscally decentralized Chinese regime local governments have an incentive to protect industries with high profit margins and state-owned enterprises because they are able to expropriate some of their profits through ad-hoc taxes and fees. 'This finding is obtained in specifications that control for comparative advantage and input-output (IO) linkages factors through interactions of industry characteristics (e.g., intermediate input in, ) and region characteristics (e.g., natural resource endowments).

**9**
to have an effect on the spatial distribution of manufacturing.'o

> We consider below three types of industrial policies **-** licensing of private industry, barriers to international trade, and regulation of FDI **-** and their potential impact on the spatial distribution of manufacturing industries. Note that these policy reforms were implemented differentially across industries and time, but not differentially across geographic regions.

# **3.1 The License "Raj"**

Since the 1950s, industrial licensing was one of the major methods to control private enterprise in India and to direct private capital into desirable industries. Under the Industries (Development and Regulation) Act of **1951,** all factories that were already operating or wished to operate in a specified list of industries were required **by** the government to obtain a license to continue or begin production." Licenses were issued only **by** the central government and affected several aspects of a plant's operation. Almost **by** definition, the licensing regime controlled entry into the industry and hence the amount of competition faced **by** a plant. **A** license also specified the amount of output that a plant could produce. Importantly, licenses were conditional on the proposed location of the project and permission was required to change locations.1<sup>2</sup> During the 1970s and 1980s the Indian licensing regime was used as an important instrument for determining location decisions. " The administration of the licensing regime allowed the central government to give positive weight to proposals intending to locate production in backward areas and to give negative weight to proposals located in/ near identified metropoli-

> 0 For more details, see Krishna and Mitra **(1996),** Sharma **(2006b),** Sivadasan **(2009)** and Topalova and Khandelwal (2011) and **(1.!in** _I1-c0-_ **.II,** and Sharma (2011). "Factories are defined as (i) enterprises that do not use power but employ more than **100** workers or (ii) enterprises that use power and employ more than **50** workers.

**1 2 In** addition, the exact nature of the item to be produced was also specified and the plant needed permission or another license to change its product mix. Even the type of technology and inputs that the plant could use in production (though not specified on the license) was determined because the most crucial raw materials (steel, cement, coal, fuel, furnace oil, railway wagon movements, licenses to import inputs) were controlled **by** the government and the plant needed to get annual allotments of these for its production. While deciding on a license, the considerations of the licensing committee were mainly macro-economic in nature and had little to do with the project's merits. For example, policy-makers disdained variety and thought that competition was wasteful.

"sAppendix **A** provides more details on how the industrial policy regime (in particular, licensing) was used to impact the spatial distribution of manufacturing industries.

**10**
tan and urban areas.'<sup>4</sup> Marathe **(1989)** finds that these policies were quite effective: e.g., in **1975** and **1976** 4,'( of approved licenses corresponded to projects located in backward areas but at the same time all the other licenses went to only four industrialized states. Thus the industrial distribution in India was tending towards bi-modal. In the 1980s, the Indian government started to relax the licensing regime **by** de-licensing certain industries. Table 1 shows the percentage of manufacturing employment that was de-licensed in selected years in the 1980s and 1990s as well as the cumulative percentage of total employment, output and capital that was de-licensed. This piecemeal approach to reforming industrial policy continued throughout the 1980s. In **1991,** the Indian economy faced a balance of payment (BOP) crisis and was forced to take loans from the International Monetary Fund (IMF). Under pressure from the IMF, the most significant de-licensing episode occurred **-** industrial licensing was removed for s-1. of manufacturing output.

Some aspects of the first phase of reforms in the 1980s are noteworthy. First, the impact of de-licensing in a particular industry was different for differently sized plants. The licensing regime included a size-based exemption which meant that plants below a certain size threshold were exempt from the licensing provisions (Sharma **(-1i it,. -.h)).** However these small plants still faced locational constraints **-** for example, they could not locate near any large Indian city **-** but these constraints were milder than those imposed on large plants. Thus, within a given industry, one can expect different patterns of spatial concentration for plants of different sizes. Second, de-licensing of industries in the 1980s was accompanied **by** continued restrictions on plant location. In particular, large plants could avail of the de-licensing only if they located<sup>in</sup> a set of "backward **;i.** that is, those with little or no manufacturing industry. This again points to the possibility of a size-based response of spatial concentration to de-licensing of an industry.

The potential impact of de-licensing on concentration in India is ambiguous. On the one hand, the industrial policy regime was willfully used to settle industry in backward, **non-** industrialized areas. Hence, after an industry was de-licensed, plants would tend to choose locations based on economic criteria: e.g., areas with good infrastructure, availability of

> 1<sup>4</sup> The administration of the licensing regime was characterized **by** trials **by** a licensing committee, a high degree of centralization, and a tradition of placing microeconomic constraints on firms regarding output, technology and input use.

11
input networks, and an appropriately skilled labor force. This would suggest that industrial de-licensing should be associated with an increase in-aw1 ti.-,fv. On the other hand, industrial policy was creating artificial clusters of industry in backward areas. As licensing requirements were removed, these clusters would likely break up, leading to a decline in 1i. **i:vij** levels." Another issue to note is that de-licensing in the 1980s was conditional on plant size and location. That is, a large plant was considered de-licensed only if it located in certain "backward" areas. Thus, de-licensing might have led to even more geographic dispersion in plant location in India.

# **3.2 The Tariff-Quota "Raj"**

Prior to reform India's trade regime was one of the most restrictive in Asia. The regime consisted of high nominal tariffs, a complex import licensing system, an actual user policy that restricted imports **by** intermediaries, restrictions of certain exports and imports to the public sector, phased manufacturing programs that mandated progressive import substitution, and government purchase preferences for domestic producers (Topalova and Khandelwal (2011)). After the BOP crisis of **1991,** there were major changes in both the tariff and non-tariff barrier levels applied to Indian industries, as well as in the methods **by** which the trade regime was implemented. Non-tariff barriers were rationalized and scaled down: e.g., **26** import licensing lists were removed and a single list that contained prohibited imports (the hg.rivC list) was established. Average tariffs fell **by** 43 percentage points between **1990** and **1996,** and the standard deviation of tariffs fell **by 50%.** The Rupee was devalued **21** against the dollar in **1991** and in **1993** India adopted a flexible exchange rate regime. The rationalization of tariff and non-tariff barriers continued into the early 2000s.

The implications of trade liberalization for the regional distribution of industries within a country have been examined **by** several **N v-** Economic Geography **(NEG)** models, where the location choices of firms and consumers are determined **by** opposing centripetal and centrifugal forces.1<sup>6</sup> The :-1ii 1 i (centripetal) forces result from the interaction between

> 5Ideally, we would like to consider, in addition to the licensing regime, the direct impact of subsidy and concessional finance schemes that were offered at the state level but data for these are not available. "For excellent reviews of the literature on the spatial effects of international trade see Mayer and Mayer (2004) and Briilhart (2010). The essential features of **NEG** models are: **(1)** increasing returns to scale

12
> IRS, market size (location of demand), the costs of trading across regions and countries, and/ or backward and forward linkages among producers. The dispersion (centrifugal) forces differ across **NEG** models, in some cases they arise from the costs that firms face to reach an

> exogenously dispersed demand of immobile consumers (working in the agricultural sector) while in other cases they arise from congestion costs (rent and commuting costs) associated with large industrial **in. .tii**

Several **NEG** models generate the prediction that trade liberalization favors the geographic concentration of manufacturing activities within the liberalizing country. Paluzie (2001) and Monfort and Nicolini (2000) derive this prediction **by** extending the models **by** Krugman **(1991)** and Krugman and Venables **(1995)** to the set-up of, respectively, a country with two symmetric regions liberalizing its trade with the rest of the world and a country with two symmetric regions liberalizing trade against a foreign country (also with two symmetric regions). As international trade is liberalized, (i) access to foreign demand (exports) lowers the incentives for domestic firms to locate near domestic consumers that now represent a smaller share of sales and (ii) the presence of foreign supply (imports) lowers the incentives for domestic firms to locate near other domestic firms for input-output **(10)** linkages since foreign firms now represent a larger share of supply to domestic consumers. The incentive for a firm to locate away from domestic competitors (in the periphery) is provided **by** the possibility of being sheltered when serving the local market. With trade liberalization, competition in the periphery comes also from foreign firms and makes the dispersion of manufacturing activities less attractive. Briilhart et al. (2004) and Crozet and Koenig (2004) consider regions that differ in their distance to the rest of the world: a border region and an interior region. The main prediction from their **NEG** models is that trade liberalization fosters spatial concentration of manufacturing in the border region with better access to international markets. The novel forces in these models are that domestic firms may be attracted to the border region to reap the full benefits from improved access to foreign demand and domestic

> (IRS) internal to the firm (e.g., due to indivisible fixed costs) and monopolistic competition generally of the Dixit and Stiglitz **(1977)** type in the manufacturing sector; (2) costs of trading outputs produced and inputs used **by** firms across distances; **(3)** endogenous firm location decisions **;** (4) endogenous location of demand. Despite a vast **NEG** literature following Krugman **(1991),** due to technical difficulties in characterizing the equilibrium distribution of economic activity, only recent models that assume many regions within a country and distinguish across regions and countries allow researchers to characterize the response of agglomeration to international trade liberalization.

**13**
consumers face an incentive to **i** 1 1<sup>.-:fintheborderregiontoaccessimportedgoods.</sup> However, another set of **NEG** models generate the prediction that trade liberalization favors the geographic dispersion of manufacturing activities within the liberalizing country. Krugman and Elizondo **(1996)** consider a model with symmetric regions and congestion costs and show that upon opening to trade, the importance of foreign demand and foreign supply increases and the weight of backward and forward linkages decreases. Hence, the location of domestic firms and consumers in a larger domestic market becomes less important.'<sup>7</sup> Since congestion costs are present and independent of trade openness, their strength under a large set of parameter values leads to the dispersion of manufacturing activities. Behrens et al. **(2007)** obtain the same prediction in a model with endogenous competition effects." In this model the 1.hI-.ti.ijo of competing firms reduces their market power, which imposes downward pressure on their local markups and this pro-competitive effect acts as an additional dispersion force. Summing up, the sign of the relationship between trade liberalization and -x1i.ti:fjij of manufacturing firms within a country is theoretically ambiguous. Hence, empirical evidence is necessary to determine that sign in the case of India.

# **3.3 FDI Liberalization**

The industrial policy regime in India from the 1970s onwards controlled foreign direct investment. Prior to **1991** foreign ownership rates were restricted to be below **-In** in most industries (Sivadasan **(2009)).** In addition, restrictions were placed on the use of foreign brand names, on remittances of dividends abroad, and on the proportion of local content in output. Liberalization of FDI occurred only after the BOP crisis of **1991.** Foreign ownership of up to **51%** was allowed for a group of industries and other restrictions on brands, remittances and local content were relaxed.

To our knowledge, the theoretical effects of FDI liberalization on the -. w1-.F -. iti-j of

> 17The authors use their model to rationalize the existence of giant cities in developing countries as a by-product of import substitution policies. The prediction is obtained assuming that inter-regional transport costs within the country are lower than international transport costs and the autarkic equilibrium is characterized **by** a spatial concentration of economic activities due to backward and forward linkages. "The paper extends to a spatial setting the monopolistic competition model of Ottaviano et al. (2002) where firms face a variable demand **-I** - i.f-.. In this model, the exogenously dispersed demand of unskilled workers is the dispersion force and trade liberalization has a pro-competitive effect i.e., firm mark-ups **fall** with the number of local producers rather than being fixed as in most other **NEG** models.

14
manufacturing industries have not been explicitly studied, but some insights from the **NEG** literature can be borrowed to conjecture about those effects. FDI liberalization in an industry is expected to bring the entry of new foreign firms, with a direct effect on the concentration or dispersion of manufacturing across regions, but can also affect the location decisions of (new or incumbent) domestic firms, with an indirect effect on the concentration of manufacturing across regions.

Focusing first on the entry of new foreign firms, **NEG** models suggest that if this new FDI enters the country to supply the domestic market, it would tend to locate close to large markets to economize on inter-regional transport costs. **If** those transport costs are low, however, then foreign firms would be indifferent about their location since they could serve domestic customers from anywhere, possibly contributing to the geographic dispersion of the industry. The theoretical model and the evidence in Amiti and Javorcik (2008) show that market access was crucial in determining the location of new FDI inflows across **(Ili1. %..** provinces. For new FDI aimed at exploiting production advantages such as cheap unskilled labor and targeting export markets, the optimal location choice is not clear. On the one hand, a new foreign firm may have an incentive to locate away from main industrial centers where wages are higher **-** leading to industrial dispersion **-** but on the other hand it could prefer to locate near major centers to use a pool of skilled labor or to be near ports or major borders **-** increasing industrial.-1iin i:f1i. Amiti and Javorcik (2008) show that lower production costs also played an important role in attracting new FDI inflows to certain Cilio %..provinces. For new FDI of both types, the degree of usage of domestic versus imported inputs could also affect location choices. **A** new foreign firm relying heavily on domestic **10** linkages would have an interest in locating close to suppliers which may or may not be already located in highly concentrated industrial areas. Amiti and Javorcik (2008) show that supplier access was critical in determining the location choices of new FDI inflows in China.'<sup>9</sup> Focusing next on the location decisions of domestic firms in response to the entry of foreign firms, on the one hand domestic firms would have an incentive to locate away from foreign firms if those are competitors in serving the domestic market as in **NEG** models. But on the other hand, the possibility of **10** linkages with (and expected knowledge spillovers **"See** Ottaviano and Thisse **(2008)** for a survey of the rationales for location of new FDL

**15**
from) foreign firms could lead domestic firms to locate near foreign firms. Since the location of foreign firms themselves near or far from existing **i1,ll! i.,ji** is not clear-cut, the effects for domestic firms' choices of location are also not clear-cut. In policy-making circles **it** is often believed that FDI can stimulate the geographical concentration of activities for example into **i lln i.ifipji** of small firms acting as suppliers to multinationals (Propris and Driffield **(2006)).** In some cases, new foreign firms are explicitly attracted to be part of such clusters as is the case for Special Economic Zones in China. But at the same time many policy-makers believe that FDI can support the regeneration of less-favored regions, which would result in a dispersion of industrial activity. The large subsidies provided to FDI firms to locate in la'. 'rd regions in developed and developing countries alike illustrate this perspective (Haskel et al. **(2007)).** As in the case of trade liberalization, the overall effect of FDI liberalization on industrial 1i **vt vi.I** is ambiguous 1iii&. among others, on the motive for FDI, the degree of local sourcing of inputs **by** new foreign firms, and the pre-existing geographical distribution of industries.

# **3.4 Public Sector Reservation**

The mixed economy framework prevailing in India prior to reforms mandated **a** large and growing role for the public sector. Certain important industries were reserved exclusively to the public sector. Additionally, the location of public sector enterprises was used to further the goal of industrializing the backward regions of the country, disregarding economic conditions. It is therefore possible that the spatial distribution of Indian industries with a large share of public sector enterprises differs systematically from that of comparable industries with less public sector presence. Moreover, the economic reforms of **1991** brought a paradigm shift in political and public views regarding the role of the public and the private sector in India. As Table B.1 shows, the average share of output produced **by** state-owned enterprises declined from an average of : **in** the 1980s to l in 1994, and then to 1:1 in **1999.**

**16**
**4 Data**

We use plant-level data from the Annual Survey of Industries **(ASI)** conducted **by** the Central Statistical Organization **(CSO),** a department of the Ministry of Programme Planning and Implementation of the Government of India to compute our -ighiin.-.iti.j indices for all consecutive years in the period **1980-81** to **1999-00,** with the exception of **1995-96** when the survey was not conducted. The length of our data allows us to cover all the Indian reforms of the 1980s as well as the major reform episode of **1991.** The survey covers all factories registered under the Factories Act of 1948. The **ASI** frame can be classified into 2 sectors: the 'census sector' and the 'sample sector'. Units employing more than **100** workers constitute the census sector. Units in the census sector are covered with a sampling probability of one while units in the sample sector are covered with sampling probabilities lower than one (onehalf until **1987-88** and one-third after **1987-88)."** As Bollard et al. (2010) explain in detail in their appendix, there are substantial caveats to the use of these data. Sampling schedule changes in **1997** led to drastic changes in sample size: the number of total plants covered and the number of plants in the census sector dropped sharply.<sup>2</sup> ' There is a sharp increase in the standard deviation of average employment in both the census and the sample sectors, causing us to worry about heteroskedasticity. In order to correct for the noise and heteroskedasticity, we will weight all descriptive statistics and regressions **by** 3-digit industry-year total employment. That is, we will give more weight to larger, presumably less noisy industries.<sup>2</sup> As an alternative to employment weights, we will also use **FGLS** techniques to weight each observation **by** the inverse of its variance relative to industry and year means. That is, we will downweight observations which are very far from industry and year means. We discuss the technique in detail below. In order to measure changes in industrial policy, we use the detailed dataset of industrial policy in India constructed **by** Sharma (2006a,b) and used in Chamarbagwalla and Sharma (2011). This dataset identifies which 4-digit industries underwent reform in terms of freedom from licensing requirements in each year from **1970** to **1990.** Table 1 shows the quantum of de-

> 20Note that the **ASI** data is a series of repeated cross-sections of plants, not a panel of plants, thus it does **not** allow **one** to identify entry and exit.

> 21See Appendix Figures B.1 and B.2.

> 22Appendix Table B.1 shows the weighted averages of the key variables used in our analysis.

**17**
licensing that took place during the sample years and brings forward two important points. The first is that the reforms of the 1980s were quite significant in terms of the percentage of manufacturing output, employment and capital affected. Cumulatively, 2. . of output and employment had been de-licensed as of **1990.** Hence, studies that ignore the pre-1991 changes in the licensing regime provide misleading estimates of the impact of the **1991** reforms. The second is that de-licensing in **1991** was not across the board as is the common assumption in most studies. After **1991,** 1'. of manufacturing output and **11%** of employment remained under compulsory licensing though some of these industries were gradually de-licensed in **1993** and 1994. The de-licensing measure used **by** Sharma (2006a) is a dummy variable at the 4-digit industry level denoted **by** _Dekt_ that is equal to one in all years greater than equal to year t if industry **_k_** was de-licensed in year t. Since our **EG** index is computed at the **3-** digit industry level, we need to calculate the proportion of employment in the 3-digit industry that was de-licensed as of year t. Hence, our measure of industrial de-licensing is given **by** **_Nj_** _DEL t_ **_= Ek Dcktykt_** where **_k_** indexes the 4-digit industries within the 3-digit industry _j_ and **Ek-1** **_Ykt_** _Y_ refers to employment.

Data on trade policy are obtained from Das **(2003).** The author computes the effective rate of protection _(ERP)_ for 3-digit Indian manufacturing industries in four sub-periods: **1980-81** to **1985-86, 1986-87** to **1990-91, 1991-92** to **1994-95,** and **1995-96** to **1999-00.23** We also use tariff data from Topalova and Khandelwal (2011) which are available for every year but only from **1987** to **1999** therefore reducing our sample size. However, these data have rich cross-sectional and time-series variation **-** which the ERP data lack. We estimate all our main specifications with both measures of trade protection. We use the FDI deregulation variable from Sivadasan **(2009)** which is a dummy variable indicating which 4-digit industries were FDI-deregulated in **1992.24** We combine this dummy variable with the **ASI** data to calculate the proportion of employment in a 3-digit industry that was exposed to FDI. Hence, our **_Nj_** measure of FDI deregulation is given by _FDIjt FDIktyt_ where **_k_** indexes the 4-digit **Ek=1** Ykt industries within the 3-digit industry **_j_** and _Y_ refers to employment.

> 23Das **(2003)** calculates these ERP measures of trade for **72** 3-digit industries. In order to use all industries in our analysis, we use the average of his ERP measure for the corresponding 2-digit industry and in some cases, the economy-wide average. Note that for any given industry, the ERP has a constant value in all years within each **of** the four sub-periods.

> 24 The announcement of this reform was made in August **1991,** but its implementation began only in **1992.**

18
**5 Estimation Strategy**

In order to calculate the rawh.1 1<sup>s.tiiindex</sup><sup>**G**for each3-digitindustryandyearwe</sup> regate the plant-level employment data to the 3-digit industry and state level in each year weighting **by** sampling weights. To obtain the **EG** index for each 3-digit industry and year using Equation **1,** we make use also of a Herfindahl index for each industry and year calculated using plant-level data on market shares." Table 2 shows that the mean of the **EG** index rose from **1980** to **1990** and then declined in **1999.** Thus Indian manufacturing ;1.-. i.-'t1 during the 1980s and dispersed during the 1990s. Appendix Tables B.2 and B.3 present the ten most and least - 1.-. n.-.t1 industries in **1980** and the evolution of their **EG** indices over the next **19** years.

Our main specification modifies that used **by** Rosenthal and Strange (2001) based on two important considerations. First, the spatial distribution of manufacturing industry is a slow moving process. This is bound to be the case although both the raw **-** 1.-.fn.-:.-jij index **G** as well as the **EG** index are expected to respond fairly quickly to changes in their determinants. That is because these indices change not only due to entry/ exit of plants, but also due to changes in employment **by** existing plants. However, it does take time for existing plants to change location or for new plants to emerge in response to changes in policies or industry characteristics. Hence, we allow all our independent variables to affect 1-ii.-.1j with a lag. Second, spatial concentration is likely to be persistent: industries that were more concentrated in the past may tend to be systematically more concentrated in the present and in the future. We account for a very general form of serial correlation in concentration **by** clustering the standard errors at the 3-digit industry level. The main specification we

> 25The **ASI** data do not allow us to identify geographic units smaller than states: the data includes a **numeric** district variable but no label to go with it. Hence it is not possible to identify which district corresponds to a particular value of the district variable. However, we were able to identify districts using matching techniques to create a pseudo-panel of plants and presuming that a given plant does not change location over time. Once districts can be identified and followed over time we are able to calculate the **EG** index using the district as the geographic unit. For most industries we find that the values of the **EG** index calculated at the district level tend to be smaller compared to those of the **EG** index calculated at the state level. This is consistent with the findings in other studies **1** i and Mayer (2004)). These results can be obtained from the authors upon request.

**19**
estimate is the following where **j** denotes a 3-digit industry and t denotes a year: The main coefficients of interest in Equation 2 are those on the policy variables: industrial de-licensing or deregulation **_(DEL),_** trade protection measured **by** the log effective rate of protection _(ERP)_ or **by** log tariffs _(TAR)_ in some specifications, and FDI liberalization _(FDI)._ Our main specification also controls for traditional determinants of the geographic concentration of an industry related to the importance of localization economies. Following Rosenthal and Strange (2001) we proxy externalities from labor pooling _(LPOOL)_ **by** the wage bill share of skilled (non-production) workers in the industry. Industries benefit from positive externalities via shared input-output networks, proxied **by** the log of materials per shipment **_(MATS)._** We use the log of inventories of finished goods per shipment to proxy for an industry's dependence on transport networks _(INVEN)_ and average real capital per plant in an industry to proxy for the returns to scale in the industry _(IRS)._ Capital is measured as the book value of average fixed capital owned **by** the plant. Summary statistics and definitions of these determinants of :1iii n.f1i are provided in Appendix Table B.1. As Rosenthal and Strange (2001) point out, the coefficients on these industry characteristics reflect the equilibrium relationship between geographic concentration and localization and urbanization economies. Thus, _LPOOL,_ **_MATS_** _and INVEN_ affect geographic concentration but are also affected **by** it. Hence, the coefficients on these variables cannot be interpreted as causal, though the problem may be ameliorated **by** the use **of** 1.'p.l values of these variables. In either direction of causality, the relationship hinges on cost reduction: geographic concentration reduces the costs of labor and inputs and because of this, industries sensitive to labor and input costs tend to concentrate. Thus, there is still valuable information to be gleaned from the coefficients on the variables capturing localization economies.

As most other studies on the determinants **_ofivwhIiF_** i:tim. we do not have appropriate measures to capture two other potentially important determinants: natural advantage and 20
knowledge spillovers. To address the potential omitted variable bias problem that may result, we control for industry fixed effects at the 2-digit industry level in our specifications **_(aj,).2_**<sup>**_6_**</sup> We also include year fixed effects **_(6t)_** in all our specifications to account for secular changes in the geographic concentration of manufacturing industries in India. In particular, these year fixed effects may capture generalized improvements in infrastructure that led to more possibilities for geographic concentration. To reduce the<sup>influenceofoutliers andtocontrol</sup> for heteroskedasticity, we weight each industry-year observation **by** its size (measured as total employment) and inversely **by** its estimated variance relative to the industry-year mean. In the case of the **EG** index the weights are given **by:** where _EG_ is the average **EG** in year _t_ and **_EGj_** is the average **EG** in industry **j. 7** To apply the **GLS** technique, we first estimate Equation 2 in an ancillary regression where each observation is weighted **by** its size and obtain the corresponding residuals. Then we project the square of those residuals on industry-year fixed effects. The predicted values from this second regression are the variance estimates used to obtain the **FGLS** estimator. Finally, we estimate Equation 2 again but now weighing each observation **by** w3 that is its size divided **by** the variance estimate. The use of w3 allows us to downweight both noisy industries and noisy years. In robustness specifications we will consider alternative weights that downweight just noisy industries or just noisy years.

# **6 Results**

## **6.1 Main Results**

The first column of Table **3** shows the results of estimating Equation 2 using the raw geographic concentration index as the dependent variable, including the one year lag of all

> **-' \W** chose not to include fixed effects at the 3-digit level since these would soak up too much of the potentially meaningful variation in the data. Though we have a long panel of 3-digit industries, the determinants of agglomeration are industry characteristics that change slowly over time. Thus, cross-sectional variation is very important in id1. **I., ,** the coefficients on those determinants. **27In1** the case of the **G** index the weights are defined similarly replacing **EG by G.**

21
independent variables. Both de-licensing and FDI liberalization lead to a significant decline in spatial concentration of Indian industries while trade liberalization has no significant effect. The estimates using the **EG** index as dependent variable are presented in Column 2 and show that geographic concentration declines significantly with de-licensing and FDI liberalization conditional on standard Marshallian externalities. However, **:tii** does not respond significantly to changes in trade policy. Hence plant location in deregulated and FDI-liberalized industries is increasingly dispersed. Note that the determinants considered in our specifications explain a large fraction ( _i)_ of the variation in spatial concentration of manufacturing in India.

In both specifications, the coefficient on the degree of returns to scale is negative and significant. This result may seem puzzling at first. But as Haaland et al. **(1999)** point out, the theoretical relation between returns to scale and relative concentration is ambiguous. That is, the degree of returns to scale characterizing an industry may affect where the industry locates, in the center or the periphery. Returns to scale **by** themselves do not allow us to draw any conclusions about how concentrated an industry is relative to other industries. One concern regarding the effects of the degree of returns to scale is that the proxy for IRS is highly correlated with the proxy for labor pooling, as shown **by** Table B.4. To address this potential multicollinearity problem we estimate Equation 2 excluding the proxy for returns to scale and present the results in Column **3** of Table **3.** Note that we focus in what follows on specifications that use the **EG** index as dependent variable given its advantages relative to the **G** index. While in Column **3** the magnitudes of the other coefficients are similar to those in Column 2, the standard errors on some of the variables change zAiwrting the importance of multicollinearity. Another explanation for the negative coefficient on returns to scale relates to the actual proxy for returns to scale used. Haaland et al. **(1999)** aver that capital per plant is a better measure of the extent of unexploited economies of scale than of realized scale economies in an economy. Consider two industries with identical cost functions. In the industry with higher output per plant, scale economies are mostly realized and hence the marginal cost improvements from a further increase in scale are likely to be small. In the industry with lower output per plant, there is scope to monetize unexploited scale economies. Thus, one interpretation of the result is that the greater the unexploited 22
scale economies, the less geographically concentrated will be an industry. Given our concern about multicollinearity, the results that we will present hereafter correspond to specifications that exclude the measure of returns to scale. But we should note that all results would be qualitatively similar if the measure of returns to scale was included." To subject the large effects of de-licensing and FDI liberalization in Columns **1-3** to a more stringent specification, we present in Column 4 of Table **3** the results from a specification where we replace individual 2-digit industry and year fixed effects with 2-digit industry-year interaction fixed effects. These interaction fixed effects control for time-varying industryspecific omitted variables such as technological changes or political economy factors. The estimates show that our results are robust to the control for these interaction fixed effects. **_29_** In order to provide a clear economic magnitude for the coefficient estimates, we compute the elasticity of the **EG** index with respect to each of the independent variables using the coefficient estimates from Column 4 of Table **3 -** our preferred specification **-** and the values of the average **EG** index in the first sample year **1980** (0.084). **A** one standard deviation rise in the proportion de-licensed output (0.49) reduces the **EG** index **by** 9. ?.while a one

**-** 29 **"T** . **---** results are **:,iI -I .- upon request.** 0ne could **argue** that the estimated policy effects indicate that industries that are more -, - i i. **I** concentrated are more organized, have greater **1.1.1 .- 1. ;** power, and thus were able to lobby the Indian government to not de-license them or to not **1l..: iI .--** FDI. That is, there might be reverse < **n1-** Ji **I-.,** between geographic concentration and de-licensing or FDI liberalization. However, two points need to be made with respect to such potential reverse < **n1-** i1 **1.:.** First, there is no evidence that Indian industrialists were **11 .1.. .I, -** for particular kinds of industrial policy during the initial reforms of the 1980s. Since the reforms were not announced nor discussed within the government and legislature, it is not clear whether industrialists knew that such reforms were in the works. Further, the industrial de-licensing that took place in **1991** was a result of a BOP crisis for which political economy factors played no role. Second, even if political economy factors were important, it is not clear what type of policy stance lobbyists from geographically concentrated industries would be asking for. One the one hand, the licensing regime was a source of rents for large incumbents that would want to preserve the status quo. On the other hand, these large incumbents had to suffer the onerous conditions imposed **by** the licensing regime, as they could not change production levels, product mix, technology of production, nor location of production without permission. For example, Sharma **(2006b)** documents massive shortages of commodities like cement, scooters, and cars in India during the 1970s and 1980s, but incumbents could not respond to these profit-making opportunities due to the licensing restrictions they faced. This means that industrialists might prefer to be de-licensed. Overall, it is not clear whether_ **-J iI. -11** concentrated induistries wouild have a unified **1. .1 ... -** effort in order to influence industrial policy. Assuming that political economy factors existed, they might be captured **by** industry fixed effects. But if they varied over time they could still bias our results: e.g.. **-,** - i. 11 . concentrated industries might realize the benefits of deregulation from observing other deregulated industries within their cluster and change their **1. .1 .1.-. -** efforts from maintaining the status quo to asking for deregulation. The specification in Column 4 address these concerns **by** including the interaction fixed effects.

**23**
standard deviation rise in FDI-liberalized output (0.2) leads to a 10.: fall in the **EG** index around its mean. Interestingly, evidence from other countries (particularly China) shows that FDI deregulation leads to increased average geographic concentration. Fujita and Hu (2001) examine increasing regional (coast versus interior) disparity in growth in China particularly in the context of the location of establishments in Special Economic Zones and attribute it to the clustering of manufacturing, and the self-selection of FDI into coastal SEZs. Ge **(2006)** also finds evidence that foreign investments are more likely to locate in regions with better access to foreign markets. Our results show that FDI liberalization leads to increased industrial dispersion. This might highlight differences between FDI that is geared towards exports of finished products (the case in China) versus FDI that is meant to produce for the dispersed domestic (host) market which may be the case for India.

# **6.2 Robustness Tests**

Given the ambiguity of theory regarding the effect of trade liberalization on intra-national concentration, the insignificant coefficient on _ERP_ is not surprising. To check the robustness of this non-result, Column 1 of Table 4 shows the results from estimating our main specifications including nominal tariffs instead of ERP. The results show an insignificant effect of tariffs on h 11 i:f1ij. Further, in unreported results we combine these nominal tariffs with coefficients based on input-output tables to create a proxy for input tariffs faced **by** each industry and find no significant effects of input tariffs on c 1 i1 :nf1i.<sup>3</sup> s One potential reason for these findings could be multicollinearity between trade and FDI policy. Both reforms occurred simultaneously in August **1991** and were broad in scope. Thus in Column 2 we present a specification that includes de-licensing and FDI liberalization as the policy variables but excludes trade policy while in Column **3** we present a specification that includes de-licensing and trade liberalization as the policy variables but excludes FDI liberalization. We find that the impact of de-licensing and FDI on geographic concentration rises marginally when trade policy is excluded, but overall the results are qualitatively similar to those in Table **3.** Similarly, the effects of de-licensing and the insignificant effect of trade

> soThe specification considered includes both final goods tariffs as well as input tariffs. These results are

> ; **I.- upon request.**

24
liberalization are maintained when FDI liberalization is dropped.

Although we control for public sector presence in all specifications so far and find the corresponding effects to be generally insignificant, it is possible that industries reserved for the public sector exhibit patterns of spatial concentration that are systematically different from those of other industries. Hence, in Column 4 of Table 4 we estimate Equation 2 including ERP again and focusing on the set of industries which were not reserved for the public sector and find that the basic qualitative results from<sup>Table</sup><sup>**3**continuetohold.</sup> Appendix Table B.5 presents the results from additional robustness checks. Column 1 shows the estimates weighing each industry-year observation according to its employment size which are qualitatively similar (though weaker) than those in Table **3.** However, BreuschPagan and White tests for heteroskedasticity imply a rejection of the hypothesis of constant variance. Columns 2 and **3** show the estimates downweighting, respectively, **just noisy in-** dustries and just noisy years. These corrections for heteroskedasticity reduce the standard errors for almost all coefficients when compared to those in Column 1 obtained when observations are weighted only **by** industry employment size. Moreover, it is important to note that all results **-** estimated using either weighted or unweighted regressions **-** are robust to restricting the sample to the years with less noisy **ASI** data **-** 1980-94.3' Appendix Table B.5 also presents the results from estimating Equation 2 including two or three year lags of the independent variables. Allowing for this longer response of geographic concentration to its determinants does not change the coefficients much relative to those in Table **_3.32_** It is interesting to note that Tables **3** and 4 show that the **EG** index responds reasonably quickly (within one year) to changes in policy variables. This result would be less plausible if we were examining .-, v1 ii using the more direct and interesting approach of modeling a plant's location decision as in Mayer et al. (2010). Unfortunately, our data do not allow us to consider a plant's location decisions since the data are repeated cross-sections of plants rather than a panel. The **EG** index which we use essentially measures the change in a location's employment share relative to the employment share in the average location. Thus, the **EG**

> 32"These An alternative results are;:- model **,** for **I** the uplon evolution request.process of spatial concentration would include the various lags of the independent variables in a single specification. However since tests reveal a very high degree of correlation between the lags, it is not clear that the results from such a specification could be relied upon.

**25**
index and its components can increase fast in response to changes in location determinants since an employment share may increase due to a capacity expansion of incumbent plants rather than the construction of new factories. Our use of a one year lag of the independent variables does not imply any unreasonable assumptions about the birth of new plants from one year to the next.

# **7 Agglomeration Patterns for Plants of Different Sizes**

In this section, we consider the degree of spatial concentration for different plant sizes within each industry. While industrial policy in India was geared towards macroeconomic goals, it was implemented differentially across plants, based on their size. In particular, Indian plants with a book value of fixed capital below a certain threshold (call it _Ktrge)_ were exempt from licensing provisions, they did not need to take permission to enter or produce and were subject to less strict location provisions. Another category of plants with fixed capital below an even smaller threshold _(Ktm'1),_ entitled "small **%...,** were exempt from licensing in addition to having certain products reserved for their production and being subject to very lenient location provisions.

Another motivation to consider size-based **EG** indices is that during the 1980s, even delicensing was administered differentially for the large plants: plants whose fixed capital was greater than _K;are_ were de-licensed only if they located in certain backward areas. Overall it might be insightful to assess whether all other determinants considered in Section **6** affect ii n.-t1i. differentially across various plant size categories. To compute size-based **EG** indices, we define three dummy variables at the plant level **-** Slit equal to one if _Kit_ **_<=_** _Ksmal;_ S2it equal to one<sup>if</sup><sup>_K'az_</sup><sup>**_<_**</sup><sup>_Kit_</sup><sup>**_<_**</sup><sup>_K;arge_and</sup> S3it equal to one if _Kit_ **_>=_** _K_ **_arge._** The first category **S1** is small plants, the second category **S2** is medium-sized plants (exempt from licensing provisions but not small enough to be small scale), and the third category **S3** is not exempt large plants. We compute the **EG** index for each size category in each industry, using data from the corresponding set of plants. Figure 1 presents the evolution of the average **EG** indices over the sample period for the three plant size categories. The figure shows significant variation in the patterns and levels of spatial

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concentration: small plants are more concentrated than medium-sized plants, which in turn are more concentrated than large plants. The **EG** index for medium-sized and for large plants also exhibits more variability relative to the **EG** index for small plants. **All** three indices show an increasing trend up to 1994, and a declining trend thereafter. But for the years **1996-99,** this could be the result of noisier data rather than actual changes in spatial concentration. Table **5** presents the results of estimating Equation **3** below for each size-based **EG** index separately: where each of the Marshallian externalities' variables are calculated separately for the three plant size categories within each industry instead of being calculated for the average plant in each industry as in Equation **3.** For example, the variable **_MATS,_** which proxies for **10** linkages is obtained as the average ratio of materials to sales for plants of size **s** in industry _j._ The same reasoning applies to the variables _INVNst_ and _LPOOL>1 ._<sup>_3 3_</sup> Columns **1-3** of Table **5** show the results for small, medium-sized and large plants, respectively. The spatial concentration of the three types of plants responds differentially to policies. As a result of FDI liberalization in an industry, small plants disperse while mediumsized and large plants exhibit no significant response. Specifically, a one standard deviation rise in the proportion of FDI-deregulated output is associated with a **9.'" '** decline in the **EG** index for small plants. Further, a one standard deviation rise in the proportion of delicensed output is associated with a **11.T,** decline in .1if. i,-:1i of small plants and a increase in w1-.1ij **of** medium-sized plants. Small plants tend to disperse geographically with trade liberalization: a one standard deviation fall in log ERP results in a 2." **'** decline in the **EG** index for small plants.34 As further evidence of differential size-based responses, we find

> "This flexible functional form to assess size-based effects of the determinants of agglomeration is informed

> **by** recent literature that documents tremendous **h. I.** i -. across plants even within narrowly defined industries (e.g., Foster et al. (2001)). 1<sup>4</sup> We use the mean **EG** index of **-0.015** for large plants and of 0.104 for small plants, as well as the 0.49 standard deviation of **_DEL_** and **0.73** as the standard deviation of _ERP_ to calculate these economic

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that large plants are significantly more spatially concentrated as a result of trade reforms: a decline in log ERP **by** one standard deviation reduces the **EG** index for large plants **by** around its mean. However, large plants respond neither to de-licensing nor to FDI liberalization.

We also find some evidence of differential size-based effects for the traditional determinants of :-41, ,-si 1. Greater labor pooling has an insignificant effect on the concentration of medium-sized and large plants but leads to more spatial dispersion of small plants. Finally, stronger public sector presence in an industry has a significant positive impact on the concentration of large plants, which presumably include some of the large, public sector enterprises. In order to facilitate hypothesis testing about the differential determinants of -1i.vt1ii across plant sizes, we pool all the size-based **EG** indices together and estimate a fully interacted model **-** defining **D2jt** to be the dummy for the medium-sized plants and **D3jt** the dummy for the large plants **-** given **by:** where the Marshallian externalities' variables are defined as above. Note that since the policy variables vary only **by** industry and year, we continue to interact them with<sup>thesize</sup> indicators.

The last column of Table **5** presents the results from estimating Equation **5** while Table **6** presents the results from testing various hypotheses about the coefficient estimates. Delicensing reduces the spatial concentration of small plants **_(01_ < 0)** but raises concentration for medium-sized and large plants **_(01_** + **'12 > 0** and **01 + 7)13 > 0)** though these effects are insignificant. FDI liberalization reduces significantly the spatial concentration of small and magnitudes.

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medium-sized plants **(03** _32_ **_<_ 0** and **03 < 0).** The spatial concentration of large plants falls significantly in response to trade liberalization (02 ±23 > 0) while that of small plants increases significantly (02 < 0). In industries with greater public sector presence large plants are more spatially concentrated **(4** _Y43_ **_>_ 0)** while medium-sized plants are significantly less concentrated **(04** _42_ **_<_ 0).**

# **8 Alternate Specifications**

Tables **3** and 4 show that, among the traditional determinants of him i..ij. **an** industry's dependence on **10** linkages and on transport increases spatial concentration in India. However, counterintuitively more labor pooling tends to lower spatial concentration. One concern is that a state might be too large a geographic unit to consider when measuring labor pooling. Plants may target smaller units, for example, districts with large proportions of educated or appropriately skilled labor force. However, when we estimate Equation 2 using district-level **EG** indices the result that greater labor pooling potential has a negative or zero effect on :-1-1 i is maintained. The counterintuitive result is also obtained for alternative measures of labor pooling including average employment shares of skilled workers and labor productivity in an industry.<sup>3</sup> ' The data show that the correlation between the **EG** index and labor pooling is large and negative initially, and then declines over time. Hence, there is a time dimension to the relationship between the demand for skill and geographic concentration. It is possible that as industries upgrade technology and skill-biased technical change occurs, labor pooling becomes an increasingly important determinant of :i1mr ation. Further, Chamarbagwalla and Sharma (2011) provide evidence of skill upgrading in Indian manufacturing during the 1980s and 1990s which would suggest that the effect of labor pooling on 1 **ti** may have changed from the 1980s to the 1990s.

Labor pooling is not the only factor whose relationship with;-ii 1 .tij may change over time. As n-t by the discussion in Section **3.1,** the negative effect of de-licensing on 1.in. i.-ti. could be due to the fact that de-licensing led to a break up of artificially created clusters in deemed backward areas. **If** this is the case, then we should see different trends in "The results based on district-level **EG** indices and using alternative labor pooling measures are available upon request.

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spatial concentration in the 1990s (when there were no restrictions on location of deregulated plants) compared to the 1980s (when plants in deregulated industries were deregulated only if they located in backward areas). Similarly as technologies and infrastructure improve over time, input-output linkages and transport dependence may affect 1 i.-1i differentially. Table B.6 shows that relative to the 1980s, de-licensing led to a rise in 1<sup>n</sup> during the 1990s.<sup>36</sup> This finding is consistent with the story that during the 1980s plants may have taken advantage of deregulation **by** setting up new establishments in backward areas where no clusters existed, leading to a decline in average 1iii n.-1ij. During the 1990s plants begin to choose their locations optimally according to economic forces, for example seeking areas with good infrastructure, **10** linkages, externalities and these areas tended to already have industrial clusters. We also find that labor pooling did not affect **1n.-I.ft C** i'-Pi in the 1990s and thus the negative effect of labor pooling on -w1in--i:f1j is confined to the 1980s. This evidence is consistent with the story that as Indian industries upgraded technology, the benefits from labor pooling rose and caused these industries increasingly to The relationship between the traditional and the policy determinants of geographic concentration may vary along an additional dimension: the trade orientation of the industry. For example, as trade reforms take effect and import-competing industries cut costs to **com-** pete with imports, they may become increasingly sensitive to 1iii n.-1i economies as a source of cost savings. Additionally, exporting industries may respond to de-licensing **by** relocating to geographic areas that reduce their costs and make them even more competitive in the world markets. In order to test these hypotheses, we calculate the trade orientation of each 3-digit industry and estimate Equation 2 for non-traded, import-competing and export-oriented industries separately.<sup>3 7</sup> To facilitate hypothesis testing we estimate also a fully interacted pooled model and present the results in Table **B.7** defining **D2jt** to be the dummy for import-competing industries and **D3jt** the dummy for export-oriented industries.

> "FDI liberalization is not included in this specification since that variable does not vary until **1990. 1**<sup>**7**</sup> **We** follow Pavcnik (2002) in defining industry trade orientation i **1.** i **-** on the average ratio of industry imports to output and of industry exports to output over the period **1980-99.** An industry with an importoutput ratio greater than 1ij' is considered import-competing. An industry with an export-output ratio greater than **111'.** is considered export-oriented. The remaining industries are categorized as non-traded. Our results are invariant to the use of thresholds other than **11.**

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Table B.8 shows the results from testing various hypotheses about the coefficient estimates.

We find that de-licensing reduces significantly the geographic concentration of non-traded industries but the total effects on import-competing and export-oriented industries are insignificant. The only significant (and positive)<sup>effectof tradereformsongeographicconcen-</sup> tration occurs, surprisingly, for non-traded industries. With respect to the traditional determinants of -aw1iii **i.-** ti we v. find that most mechanisms are stronger for import-competing industries. In particular, import-competing industries;-, **wi** as a result of greater labor pooling potential while the non-traded industries disperse. This<sup>isconsistent withthestory</sup> that greater import competition forces import-competing industries to cut costs via greater use of localization economies.

# **9 Conclusion**

In this paper we analyze the impact of industrial and trade policies on the w1 tii i: of manufacturing industries in India. Our results show the importance of policies **-** in particular de-licensing and FDI liberalization **-** in affecting the spatial distribution of Indian manufacturing. Our study is one of the few that uses a long time span of Indian plant-level data from **1980** to **1999** that allows us to consider these important changes in industrial policies. Several of our results can inform policymakers, particularly in developing countries. Our results show that both de-licensing and FDI liberalization led to a decline in average 1.eration levels, while trade policy had no significant effect. That is, plants respond to some market-oriented policy reforms but not all. Further, our results point out the importance of an hitherto unexplored policy that can affect :a1iii .ti - FDI liberalization **-** and emphasize the need to examine alternative mechanisms that affect plant location decisions.

Our results are also unique in that most studies for developed and developing countries alike have emphasized how market-based forces tend to increase - 1ii.i-tii. Thus it is important to acknowledge the ambivalence of the theory and the prevalence of a multitude of mechanisms **-** including for example, the existing spatial distribution and congestion costs in existing clusters **-** that affect 1 i i..ij. and hence should inform policy. Our result that policy reforms increase dispersion in Indian manufacturing provides an explanation for why

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Bollard et al. (2010) find that only a quarter of the Indian oiiregate productivity growth can be attributed to the policy reforms. Increased dispersion following the reforms reduced an important source of productivity gains **-** -g1iii .ti economies.

Thirdly, our results emphasize the importance of accounting for heterogeneity across plants. We find considerable and consistent evidence that small, medium-sized and large plants respond differently to the policy determinants<sup>of</sup> :w1iii<sup>i.tim.Our resultsshow the</sup> importance of looking beyond industry-level average effects. In the case of India we use plant size as the main source of plant-level heterogeneity because industrial policy in India prereform was conditional on plant size. However there are several other important dimensions along which plants are heterogenous and should be considered in future empirical studies as well as in policy decisions.

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