metadata: authors: 0: author: Li,Yue - ETICI
1: author: Sinha Roy,Sutirtha
count: India
docna: 0: docna: The Employment Effect of Place-Based
            Policies : Evidence from India
docty: Policy Research Working Paper
subtopic: Tax Policy,Macroeconomics and Economic Growth,Economic Policy, Institutions and Governance,Construction Industry,Pulp & Paper Industry,Plastics & Rubber Industry,Textiles, Apparel & Leather Industry,Business Cycles and Stabilization Policies,General Manufacturing,Food & Beverage Industry,Common Carriers Industry,Tax Law,Economic Adjustment and Lending,Taxation & Subsidies,Tax Administration,Public Sector Economics,Public Finance Decentralization and
            Poverty Reduction,Macro-Fiscal Policy,Legislation,Judicial System Reform,Foreign Trade Promotion and Regulation,Trade Law,Legal Reform,Social Policy,Regulatory Regimes,Legal Products
teratopic: Macroeconomics and Economic Growth,Social Development,Law and Development,Governance,Industry,Public Sector Development,International Economics and Trade
abstracts: cdata!: Many governments in developing countries
            have pursued policies targeting specific geographic areas
            over the past several decades. However, only a few have
            rigorously evaluated the causal impact of these
            interventions. This paper examines the effectiveness of a
            prominent place-based policy in India: the centrally
            sponsored New Industrial Policy for the state of
            Uttarakhand. Using georeferenced economic census data, the
            analysis applies a boundary discontinuity research design
            and zones in on the unique border between Uttarakhand and
            Uttar Pradesh, two states that were officially one before
            the implementation of the New Industrial Policy. The
            findings show that there was a significant and abrupt
            increase in employment at the town and village level when
            crossing the state border from Uttar Pradesh to Uttarakhand
            after the full implementation of the New Industrial Policy.
            The conclusion even holds for firms within the same sector.
            The increase is mainly due to larger firm sizes and
            expansions into new industries. A main component of the New
            Industrial Policy was excise tax incentives for certain
            industries. The paper finds that the increase in
            cross-border employment is higher for sectors receiving
            excise tax incentives than others. Additionally, exploring
            spillovers between industries, the paper shows that,
            controlling for the direct effects, the sectors with labor
            requirements similar to those receiving excise tax
            incentives also experience an increase in employment.
            Finally, the growth in the number of firms in Uttar Pradesh
            close to the border remained stable before and after the New
            Industrial Policy, which suggests the results are not fully
            driven by firms relocating from Uttar Pradesh to Uttarakhand.
geo_reg_key: 517198,517191,517212
repnme: repnme: The Employment Effect of Place-Based
            Policies : Evidence from India
display_title: The Employment Effect of Place-Based
            Policies : Evidence from India
listing_relative_url: /research/2020/11/32596317/employment-effect-place-based-policies-evidence-india
topicv3: Manufacturing Sectors m1326521 1281,Tax Expenditures m1326898 1122,Special Economic Zone m1327217 1016,Industry Policy m1327403 1075,Tax Incentives m1327701 883
publishtoextweb_dt: 2020-11-19T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Social Development,Law and Development,Governance,Industry,Public Sector Development,International Economics and Trade
origu: EFI-SAR-POV-Poverty and Equity (ESAPV)
no_of_pages: 39
author_keys: 0: author_key: 1312512
model_extractions: Policy Research Working Paper 9477 The Employment Effect of Place-Based Policies Evidence from India _Yue Li Sutirtha Sinha Roy_ Poverty and Equity Global Practice & South Asia Region Office of the Chief Economist November 2020
Policy Research Working Paper 9477

# **Abstract**

Many governments in developing countries have pursued policies targeting specific geographic areas over the past several decades. However, only a few have rigorously evaluated the causal impact of these interventions. This paper examines the effectiveness of a prominent place-based policy in India: the centrally sponsored New Industrial Policy for the state of Uttarakhand. Using georeferenced economic census data, the analysis applies a boundary discontinuity research design and zones in on the unique border between Uttarakhand and Uttar Pradesh, two states that were officially one before the implementation of the New Industrial Policy. The findings show that there was a significant and abrupt increase in employment at the town and village level when crossing the state border from Uttar Pradesh to Uttarakhand after the full implementation of the New Industrial Policy. The conclusion even holds for firms within the same sector. The increase is mainly due to larger firm sizes and expansions into new industries. A main component of the New Industrial Policy was excise tax incentives for certain industries. The paper finds that the increase in cross-border employment is higher for sectors receiving excise tax incentives than others. Additionally, exploring spillovers between industries, the paper shows that, controlling for the direct effects, the sectors with labor requirements similar to those receiving excise tax incentives also experience an increase in employment. Finally, the growth in the number of firms in Uttar Pradesh close to the border remained stable before and after the New Industrial Policy, which suggests the results are not fully driven by firms relocating from Uttar Pradesh to Uttarakhand.

This paper is a product of the Poverty and Equity Global Practice and the Office of the Chief Economist, South Asia Region. 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:// www.worldbank.org/prwp. The authors may be contacted at yli7@worldbank.org and ssinharoy@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# The Employment Effect of Place-Based Policies: Evidence from India

## Yue Li and Sutirtha Sinha Roy

Yue Li and Sutirtha Sinha Roy are with the World Bank. Following the CRediT (Contributor Roles Taxonomy), Sutirtha Sinha Roy’s main contributions are on conceptualization, methodology, data curation, software, formal analysis, validation, visualization, and writing. Yue Li’s main contributions are on conceptualization, methodology, formal analysis, validation and writing. Their contact information is: Sutirtha Sinha Roy Yue Li M: +91-9810645843 M: +1 (202) 473-7471 E: ssinharoy@worldbank.org E: yli7@worldbank.org The World Bank The World Bank 70 Lodi Estate 1818 H Street NW New Delhi Washington, DC 110003 20433 India USA This research was funded by the World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

Skillful research analysis was provided by Milagros Alejandra Chocce Falla and Nikhil Tekwani. The authors thank Hania Kronfol, Rinku Murgai, Martin Rama, Siddharth Sharma, Hans Timmer, and Lixin (Conlin) Xu for very helpful comments and suggestions.

1
The Employment Effect of Place-Based Policies: Evidence from India Yue Li and Sutirtha Sinha Roy

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**Key words:** place-based policies; tax incentives; agglomeration economies; coagglomeration; boundary discontinuity; employment; firm location; India.

**JEL Classification:** R3, R5, H2.

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# 1. Introduction

Many governments in the world have pursued place-based policies to address spatial disparities in economic growth. These interventions are intended to enhance the economic performance in certain areas of a country, through tax incentives, subsidies, land grants, infrastructure investment and other localized benefits to incentivize firms to relocate or invest in the targeted area. These policies have been widespread both in advanced economies and in developing countries in the past several decades. Traditionally, economists have been skeptical about place-based policies. While the efficiency and equity justifications of these policies are strong, their actual benefits and the distortions have long been debated (Austin, Glaeser and Summers 2018; Bartik 1991; Glaeser and Gottlieb 2008; Kline and Moretti 2014a, b).

Whether these policies can generate economic gains at the local or national level is largely an empirical question. However, the available empirical evidence is at best mixed. Although there is a strand of empirical literature on these interventions in advanced economies, only a few studies have attempted to rigorously evaluate place-based policies in developing countries (Duranton and Venables 2018; Neumark and Simpson 2015). Several of these studies show economic gains from place-based interventions, such as special economic zones in China (Lu, Wang and Zhu 2019; Wang 2013).

In this paper, we examine the effectiveness of a prominent place-based policy in India: the centrally sponsored New Industrial Policy for the state of Uttarakhand. Three reasons motivate this choice. First is the quasi-natural experiment nature of the policy. Uttarakhand is a unique state and was split from Uttar Pradesh for political reasons in 2000. And the New Industrial Policy was implemented only in Uttarakhand two years after the new state was created. Therefore, comparing performances between the two states provides an opportunity to evaluate the causal impact of the policy. Second is scale. Because of its comprehensiveness (including infrastructure investment and fiscal incentives) and large size (USD 34 billion), the New Industrial Policy is a good example of a “big push” development strategy in developing countries that is large enough to overcome the threshold effects of local economic development and generate observable impacts. Third is the heterogeneity across sectors. Some sectors received substantially more fiscal incentives than others. This sectoral difference in treatment allows us to assess the direct impact of the fiscal incentives as well as their indirect effects through spillovers between industries.

Our analysis consists of reduced-form evaluations of the Uttarakhand New Industrial Policy’s local impact on employment and firms in the non-agriculture sectors. We take advantage of recently available data— the 2013 round of the Economic Census of India, which captures almost all modern economic activities after the New Industrial Policy had been implemented for over a decade. We geo-reference the Economic Census to the digitized boundaries of towns and villages in India which have recently become available. This spatial granularity allows us to apply a rigorous boundary discontinuity design: taking towns and villages located in Uttarakhand within a short driving distance to the border between Uttarakhand and Uttar Pradesh as targeted places and taking similar bordering towns and villages located in Uttar Pradesh as controls. We explore a range of distance buffers from 40 to 100 kilometers on the either side of the border.

To assess validity of the quasi-natural experiment, we also geo-reference two previous rounds of the Economic Census, 1990 and 1998, to the same digitized boundaries. Although the georeferencing is not perfect due to the passage of time, we find no evidence that, in the period prior to the policy, the bordering places in Uttarakhand performed better or grew faster than the bordering places in Uttar 3
Pradesh. A more restrictive selection of places to those within 4 to 10 kilometers of the border and a placebo test with a fictitious border 50 kilometers into the interior of Uttar Pradesh indicate that spatial discontinuity in geographic features is not a concern for our analysis either.

We find that on average, there is a significant, abrupt increase in employment in towns and villages when one crosses the state border from Uttar Pradesh to Uttarakhand in 2013. Following Holmes (1998), we interpret this as an evidence for the causal impact of the New Industrial Policy at the local level. Taking places within 40 kilometers from the border as an example, there is an 82 percent increase in total employment and a 39 percent increase in total number of firms. Therefore, the industrial promotion policy led to both a net increase in the number of businesses and an increase in firm sizes in the bordering towns and villages of Uttarakhand relative to those in Uttar Pradesh.

A policy relevant question is whether fiscal incentives provided as part of the New Industrial Policy stimulated investment and created new jobs. To shed light on this issue, we first apply the boundary discontinuity research design to the sample at the town-sector level, instead of town (and village) level. We find a significant, abrupt increase in employment within the same sector when crossing the border from Uttar Pradesh to Uttarakhand. However, we find no evidence of an increase in the number of firms. This suggests most of the growth in the number of firms identified by the town-level analysis is because of business investments in newer sectors. Meanwhile, the growth in employment is driven by both investments in new sectors that have larger firms and a growth in firm size. If larger firms tend to be more productive on average, there might have been productivity gains associated with the New Industrial Policy (Bartelsman, Haltiwanger and Scarpetta 2013; Hsieh and Klenow 2009).

We then explore the heterogeneity of fiscal incentives received by different sectors. According to the central government’s guideline, central excise tax exemptions were not offered to a selected group of industries. Additionally, for some sectors, their prevailing central excise taxes across India were zero. We identify the sectors receiving positive effective central excise tax incentives as treated and those receiving zero effective incentives as nontreated. The difference in outcomes between these two groups of firms allows to parse out the impact of central excise tax incentives from other interventions, such as infrastructure investments. Our analysis suggests that both central excise tax and other incentives had significant impact on employment for businesses within the same sector. For places within 40 kilometers to the border, non-central excise tax interventions led to a 20 percent increase in employment in Uttarakhand in comparison with Uttar Pradesh, which accounts for the lion’s share of the average withinsector effect. However, for sectors that received central excise tax incentives, the additional increase in employment is prominent (44 percent), doubling the impact of the non-central excise tax interventions. These results remain robust when the differences in access to infrastructure at the town and village level are explicitly controlled for.

Another key question for policy purposes is whether agglomeration economies were at play so that the New Industrial Policy may have persistent local effects. In theory, agglomeration economies can continue attracting business investment to the targeted area, even after interventions are phased out. It is one of the strongest justifications for place-based policies in terms of efficiency. But, when economic activities are concentrated initially, such as in a core-periphery pattern, agglomeration economies can lead to spatial concentration of firms in other areas that cannot be easily dissolved. This is why a “big push” strategy has a greater potential to overcome the agglomeration economies of existing centers of 4
production and turn around less developed areas. The New Industrial Policy of Uttarakhand could be a similar case in point.

However, it is challenging to separately identify agglomeration economies from the general impact of place-based policies when long-term data with sufficient post-intervention observations are not available. Both channels can lead to concentration of firms in the same sector in the targeted area. We circumvent this challenge by assessing spillovers between industries. Following Ellison, Glaeser and Kerr (2010), we construct the coagglomeration index measuring the overall tendency that two industries locate in proximity, and indices measuring two Marshallian mechanisms for spillovers, namely, input-output linkages and labor pooling, that can give rise to agglomeration across industries. We show that the industrial tendency for colocation leads to employment gains in sectors that were not directly incentivized by the New Industrial Policy. We find that controlling for the direct effects, sectors with labor requirements similar to those receiving excise tax incentives experience an additional increase in employment as one crosses the border from Uttar Pradesh to Uttarakhand, while sectors sharing a labor pool with those not receiving incentives see a decline in employment.

A legitimate concern regarding the significant job growth estimated in Uttarakhand is the potential displacement effect on the Uttar Pradesh side. The Economic Census data are not well suited to directly study firm closure and relocation, the data are cross sectional in nature and do not report the age of firms. We partly address this concern by assessing the growth in the number of firms using all three rounds of Economic Censuses. We show that the growth in firm number stays relatively stable in the bordering places in Uttar Pradesh in the periods before (1990-1998) and (1998-2013) after to the New Industrial Policy. In contrast, we find that the growth in the number of firms increased significantly for the comparable places on the Uttarakhand side of the border. While we cannot fully rule out the displacement effect completely, we take these results as evidence that the significant employment growth due to the policy is not fully driven by the relocation of firms from Uttar Pradesh to Uttarakhand at the border.

Overall, we verify the local channels by which a large place-based policy in India impacts net job creation and firm location. We show the impact takes place through both direct and indirect channels, highlighting the role of agglomeration economies at play. However, owing to data limitation, our analysis sheds light only on the local development impacts of the policy and cannot address the nation-wide net costs and benefits of the New Industrial Policy generated for India as a whole.

This paper extends a strand of relatively new literature evaluating the impacts of place-based policies in developing countries. Most of these existing studies focus on China. In the case of India, Chaurey (2017) uses the Annual Survey of Industries to study the central tax incentives targeting Himachal Pradesh and Uttarakhand, and Shenoy (2018) takes nighttime light data as a proxy of economic activities to investigate a broader set of interventions targeting Uttarakhand. Our analysis complements these studies by exploring firm-level data that have both a more comprehensive coverage (manufacturing and services sectors, formal and informal) and a finer spatial granularity (town and village level instead of district level).

Our research design requires weaker assumptions than much of the prior literature based on differencein-differences estimation—which is the case of Chaurey (2017) — and explores sectoral heterogeneity to assess the impact of fiscal incentives. In doing so, this paper also builds on a large literature studying the impact of taxes on business location decisions (Duranton, Gobillon and Overman 2011; Holmes 1998; Rohlin, Rosenthal and Ross 2014). Another innovative aspect of the design is that we verify the impact of agglomeration economies by looking at spillovers between industries. We provide another piece of 5
evidence in support of the potentially persistent local effects of “big push” policies (Kline and Moretti 2014a). In doing so, we also extend the literature on coagglomeration (Ellison, Glaeser and Kerr 2010; Faggio, Silva and Strange 2017; O’Sullivan and Strange 2018; Diodato et al. 2018).

The remainder of the paper is organized as follows: Section 2 begins with a discussion of the related literature, Section 3 presents a brief history of Uttarakhand and the New Industrial Policy, Section 4 sets up the empirical strategy, Section 5 describes the data, Section 6 presents the results and Section 7 concludes.

# 2. Related literature

## _2.1 Place-based policies_

The paper mostly fits into the large literature on place-based policies. Bartik (1991) provides the first comprehensive taxonomy and discussion of the different types of policies. These policies are widespread both in advanced economies and in developing countries in the past several decades. In advanced economies, place-based polices are best known for targeting underperforming areas. Other place-based policies focus on areas that are already doing well or have the potential to spearhead growth, such as clusters or zones. Place-based policies could be justified on the grounds of efficiency. By design, placebased policies aim to affect the location of firms, employment, industry mix and prices in the targeted area. Agglomeration economies are considered an important rationale for such interventions because these policies can make a region temporarily attractive for investment and potentially create a new center of agglomeration with long-term productivity gains. For policies targeting underperforming areas, more often equity motivations are in play (Kline and Moretti 2014a, b; Duranton and Venables 2018).

However, traditionally, economists have been skeptical about place-based policies. At the local level, the effectiveness of these policies may be marginal and mobile firms and workers may arbitrage away the benefits by relocating. At the national level, the local gains may be offset by losses elsewhere. The actual benefits and the distortions created by these policies have long been debated by economists (Austin, Glaeser and Summers 2018; Busso, Gregory and Kline 2013; Glaeser and Gottlieb 2008; Moretti 2010).

Whether these policies can generate economic gains at the local or national level is largely an empirical question. Most existing studies focus on advanced economies in the United States and Europe. For example, Kline and Moretti (2014a) study one of the most ambitious regional development programs in U.S. history, the Tennessee Valley Authority, that aimed to modernize one of the poorest areas through a series of large-scale infrastructure investments and amounted to subsidizing 10 percent of household income during its peak period. And they find that it generated long-term local employment gains in manufacturing industries and led to national productivity gains through infrastructure improvements.

However, the evaluation of place-based policies in the context of these advanced economies is complicated by at least two issues and the evidence is at best mixed (See Duranton and Venables 2018 and Neumark and Simpson 2015 for reviews). First, most place-based policies target areas that are both poorer and on a slower growth trajectory. Second, many interventions previously evaluated are perhaps too modest to overcome the agglomeration economies of existing centers of production and turn around areas that are economically weak.

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Rigorous studies on place-based policies in developing countries are much scarcer. Interestingly, their findings tend to support the economic gains of these interventions. This is partly because, by research design, these studies try to overcome the two issues affecting the evaluation outcomes. They either investigate targeted areas that are believed to have the potential to spearhead growth or examine policies that are generous in nature. As a case in point, Lu, Wang and Zhu (2019) look at special economic zones (SEZs) in China, which on average, are areas with geographic and socioeconomic advantages. The interventions lasted decades and were also overarching, including infrastructure investment and institutional reforms. Many zones received subsidies from the central government as well. They find zones have a positive effect locally—on the location of firms, investment, employment, productivity and wages, and create a net benefit nationally.<sup>1</sup> In the case of India, Chaurey (2017) studies the 2003 central tax incentives targeting two states, Himachal Pradesh and Uttarakhand, and Shenoy (2018) investigates a broader set of centrally sponsored interventions targeting Uttarakhand, including both fiscal incentives and infrastructure investment, and over the same period. Himachal Pradesh and Uttarakhand were among the smaller, poorer and less developed states in India. However, these interventions lasted for a decade and were among one of the world’s most generous place-based policies in scale. Chaurey (2017) shows that these tax incentives led to large increases in the number of firms, employment, investment and output in the manufacturing industries in the targeted states relative to other states. Shenoy (2018) finds that nighttime light intensity rose sharply in the targeted state relative to the neighboring state, implying about 28 percent increase in output.

While both studies confirm the effectiveness of the interventions at the local level, they are limited by their respective data and corresponding research designs. Relying on the Annual Survey of Industries, the main results of Chaurey (2017) are based on formal companies of the manufacturing industries, which may bias the results by omitting services sectors and informal companies. The Annual Survey of Industries are not spatially granular either, which poses a challenge to Chaurey’s (2017) most stringent specification that relies on boundary discontinuity but compares district-level outcomes (Baum-Snow and Ferrira 2015). The findings of Shenoy (2018) depend on nighttime light intensity data, which are spatially much more granular, and more conducive to boundary discontinuity design. However, nighttime light intensity remains a proxy for economic activities. Additionally, nighttime light data measure aggregated economic output and are not conducive to investigate the specific ways that the interventions affect economic agents’ behaviors.

In this paper, we revisit the question on the effectiveness of the centrally sponsored interventions in Uttarakhand and address several limitations of previous studies. First, we use the 2013 round of the Economic Census to cover all modern economic activities (non-farm activities) in both the formal and informal sectors. Second, we follow a rigorous boundary discontinuity design and compare outcomes at the town and village level while controlling for distance to the border. Most importantly, we investigate outcomes at the town-sector level and explore the heterogeneity across industries in the incentives. Our results shed light on the specific channels through which these interventions affect employment and firm locations.

> 1 Alder et al. (2013) and Wang (2013) also find evidence supporting the effectiveness of the SEZs in China.

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# _2.2 Taxes, agglomeration economies and firm location_

When investigating specific channels, the paper also builds on the literature measuring the impact of tax policy on businesses location, and the literature quantifying the effect of agglomeration economies on the geography of firms.

There has been extensive work on the impact of taxes on business location decisions (See Wasylenko 1997 and Giroud and Rauch 2019 for reviews). Following Fox (1986) and Holmes (1998), several recent studies adopt the boundary discontinuity design to address identification challenges. For example, for the United States, Chirinko and Wilson (2008) focus on state investment tax incentives; Giroud and Rauch (2019) study both corporate income tax and personal income tax; Rohlin, Rosenthal and Ross (2014) investigate three types of taxes together: corporate income tax, personal income tax and sale tax. For France, Rathelot and Sillard (2008) look at local corporate tax. And for the United Kingdom, Duranton, Gobillon and Overman (2011) study the local tax on non-residential property. Overall, these studies show that subnational tax policies significantly affect the location decisions of existing investors and new business activity.

Moreover, they also find that the impact is heterogeneous, varying by the composition of economic activities, by industry and by other characteristics of the businesses. Rohlin, Rosenthal and Ross (2014) predict that in the context of multiple sectors (manufacturing, services, residential) bidding for land, the capitalization of cross-border differences in tax conditions is partial and tax differences will affect where different types of businesses locate. Our analysis follows this literature closely by exploring a boundary discontinuity design and the sectoral heterogeneity in firms’ responses to tax incentives, which is a key part of the place-based policies in Uttarakhand.

One strand of the tax impact literature also considers how agglomeration economies influence the effectiveness of tax incentives. In theory, the existence of agglomeration economies can have ambiguous impact because of multiple equilibria. On the one hand, agglomeration economies can strengthen tax effects. A temporary tax cut in an area may attract firms initially. Over time, this can create a new center of agglomeration, which, in turn, attracts more firms to the area. However, this result is more likely when competing areas are similar (Baldwin et al. 2003; Burbidge and Cuff 2005). On the other hand, agglomeration economies can weaken tax effects. When economic activities are concentrated initially, such as in a core-periphery pattern, agglomeration economies can lead to spatial concentration of firms that cannot be dissolved by tax differences (Baldwin and Krugman 2004). Empirically, Brulhart et al. (2012) and Jofre-Monseny and Sole-Olle (2012) apply the same empirical strategy but reach the opposite conclusions for businesses in Switzerland and in Catalan in Spain.

Another related literature is on quantifying the impact of agglomeration economies on the geography of firms. Marshall (1890, 1920) argues for three mechanisms of agglomeration that reduce costs and influence firm location decisions: input-output linkages, labor pooling and knowledge spillovers. However, it is challenging to Identify the existence and relative importance of three mechanisms empirically because all three mechanisms lead to the same spatial pattern for an individual industry (Duranton and Puga 2004). Ellison, Glaeser and Kerr (2010) make a breakthrough and study the observed coagglomeration of industry pairs—the tendency of two industries to locate together. They create proxies to measure the mechanisms promoting industries to collocate and shed light on the impact and the microfoundations of agglomeration economies in the United States. A handful of papers follow this approach for a few other countries (Faggio, 8
Silva and Strange 2017). Building on this literature, we measure coagglomeration and the mechanisms of spillovers between industries to uncover the indirect effects of the place-based policies in Uttarakhand.

# 3. Background

India has attempted to correct the imbalances in economic growth across locations through freight equalization and less developed district grants policies, but with limited impact. More recently, it has attempted to use other place-based industrial policies, such as the Special Economic Zones (SEZs) Act, to directly influence the locational choices of firms through a range of area-specific incentives. The industrial promotion scheme for the “special category” states of Uttarakhand in 2003 emerged in this context.

## _3.1 The split of Uttarakhand from Uttar Pradesh_

Uttarakhand, formerly known as Uttaranchal, is largely a hilly state nestled in the Himalayas in the northern part of India. After the Independence in 1947, the area was merged into the state of Uttar Pradesh. In 1998 the legislative assembly and council of Uttar Pradesh passed a Reorganisation Bill calling for the formation of a new state from the area. Two years later, the Parliament of India passed Uttar Pradesh Reorganisation Act to authorize the creation of the new state. On November 8, 2000, Uttarakhand formally split from Uttar Pradesh.

The split was driven by history and political reasons. It was not due to any specific controversy about district borders. In fact, the state border between Uttarakhand and Uttar Pradesh was based on the district borders existing before the split. The Reorganization Act simply clarified the northwestern districts that would form the new state. Consequently, Uttarakhand largely comprises districts from Uttar Pradesh’s Himalayan region. However, two border districts, Haridwar and Udham Singh Nagar, are not mountainous (Tillin, 2013; Shenoy 2018).

## _3.2 The New Industrial Policy_

After the split, the Government of India (central government) initiated a series of interventions to support Uttarakhand’s industrial development. Uttarakhand was placed in the list of “special category” states because it is predominantly covered by hilly areas and forests. With a total population of 8.5 million in 2001, it was also one of the smaller states. Because of its topography and size, industrialization was perceived as a challenge. Furthermore, the state is of strategic importance, bordering China and Nepal. The centrally sponsored interventions started in 2002 and were phased out in a decade, featuring fiscal transfers, better access to and investment in infrastructure, and a range of fiscal incentives. The overall transfers and incentives were estimated to be around USD 34 billion (at 2005 purchasing power parity).

Uttarakhand’s per capita share of central tax revenue is higher than most other states because of its status as a “special category” state. The status was accorded to it in 2001 because of its topography as a hill state, low population density and its strategic location as a border state. In India, states’ revenues consist of transfers from the central government and those generated by state taxes. On a per capita basis, the grants from central government to Uttarakhand was ten times as much as those to Uttar Pradesh in 2002. The gap continued widening until 2012 when central transfers to both states fall (Shenoy 2018; Reserve Bank of India 2013).

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A dedicated public agency, the State Infrastructure and Industrial Development Corporation of Uttarakhand Limited (SIIDCUL), was established in 2002 to channel central transfers to infrastructure and industrial development. The agency was endowed with share capital of 500 million rupees (USD 37.3 million in purchasing power parity) and up-front capital of 200 million rupees (USD 14.9 million). Its main mandate was to build public industrial estates and support private industrial estates, with dedicated infrastructure. The central government has also granted the state full control over its own power resources and rights to draw from central-government power resources since 2002 (Government of Uttarakhand 2016).

The central government further decided to provide additional tax incentives to businesses in Uttarakhand starting in 2003. All new firms and existing units with substantial expansion in the designated industrial estates were entitled to a full exemption from central excise duty for a period of 10 years, a full exemption from central corporate income tax for an initial period of five years and between 25 and 30 percent exemption for additional five years, and 15 percent of capital investment subsidy. For existing firms, substantial expansion requires that they increased the value of fixed capital investment in plant and machinery by at least 25 percent.

However, only a few months after the enactment of the policy, in June 2003, India’s Central Board of Excise and Customs (CBEC), issued a list of industries that were considered ineligible for the central excise tax exemptions, even if they chose to locate in Uttarakhand. The negative list covered a number of sectors, such as manufacturing of plastics, paper and rubber.<sup>2</sup> A short negative list was also issued for the central corporate income tax exemptions with only refined petroleum products, polyester and motor vehicle production sectors appeared on the list.<sup>3</sup> The central excise tax exemption was expected to be removed in March 2010, but it was further renewed until 2017. The central corporate income tax exemption was removed in March 2012. Overall, for most eligible sectors, these tax incentives were substantive and offered for a long period of time (CBEC 2003; Chaurey 2017; Ministry of Commerce and Industry 2003).

Press reports at the time suggest that the incentives provided under the New Industrial Policy were successful in drawing major industries to the state. For instance, an article in a leading business daily showed that the cost of producing a common pharmaceutical item in Uttarakhand was 25 percent lower than in Gujarat. The article also showed that as information technology and manufacturing establishments were making large investments in the region, they were simultaneously encouraging their vendors to open production centers in the state. Tata Motors was a prominent example, which bought 650 acres of land in the city of Rudrapur, a major industrial area promoted by SIDCUL, for the production of a popular minitruck. Its investment was complimented by an additional 350 acres of land procured by 57 vendors supplying automobile parts and components directly to Tata Motors’ production unit.<sup>4</sup>

> 2 The Central Board of Excise and Customs (CBEC) was renamed the Central Board of Indirect Taxes and Customs (CBIC) in 2017.

> 3 The negative list for corporate tax exemptions can be accessed at

> - <u>https://www.indiabudget.gov.in/budget_archive/ub2003 04/fb/bill813.pdf.</u>

> 4 The article can be accessed at: https://www.business-standard.com/article/beyond-business/the-next-boom- <u>town-106121601012_1.html.</u>

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# 4. Empirical Strategy

## _4.1 Place-based policies: Quasi-natural experiment and boundary discontinuity_

In this paper, we empirically evaluate whether the centrally sponsored New Industrial Policy affected firms’ behavior and created more jobs in the targeted area as compared to the control area. Our empirical strategy exploits the quasi-natural experiment nature of the split of Uttarakhand from Uttar Pradesh. The policy regimes were identical in both states before the split. And the series of interventions brought by the New Industrial Policy in Uttarakhand after the split were absent in Uttar Pradesh. Other major policy initiatives were effective in both states, such as the Electricity Act 2003 and the Special Economic Zones Act of 2005. They should not plague the conclusion of our analysis.

However, in addition to public policies, many other local characteristics, such as natural geography, demographics and agglomeration economies, can affect firm decisions and outcomes. Following Holmes (1998), our empirical strategy applies a boundary discontinuity design and examines what happens at the towns and villages in close proximity to the border between Uttarakhand and Uttar Pradesh. At this state border, the geographic determinants of the distribution of firms—climate, elevation, soil fertility, transport connectivity—demographics, socio-economic characteristics and the level of agglomeration benefits should be approximately the same on both sides of the border.

At this state border, average differences in employment and firm characteristics on the Uttarakhand side relative to the Uttar Pradesh side can then be attributed to interventions under the New Industrial Policy. If the interventions have been effective in influencing business location and investment decisions, there should be an abrupt change in business activities at the border after their implementation.

We initially apply this empirical strategy using a cross-sectional linear regression of the following form: × T T T 1 × D D 2 × D D × T T T (1), where yy _m_ mm indexes a town or village close to the state border. = αα+ ββ<sup>BB</sup> mm + ωω DDT TTmm + ωω represents an outcome variable for all DDT TTmm mm + εεmm manufacturing and services sectors in town or village _m_ , including total employment and the number of mm yy firms. We measure the outcome using data from 2013, after most interventions had been fully implemented. _Target_ indicates whether the town or village is located in Uttarakhand, the targeted area (=1), or in Uttar Pradesh, the control area (= _0_ ). We are interested in its coefficient, , which indicates the average effect of all interventions of the New Industrial Policy in Uttarakhand relative to Uttar Pradesh at the border. _Distance_ measures the driving distance between each town or village and the border. Following the ββ<sup>BB</sup> literature, we incorporate it in the form of a linear function, to control for any additional differences near the border areas. How close to the state border should we select our sample places and conduct the analysis is an empirical question. Following the literature, we replicate the analysis for town and villages within several distance buffers, ranging from 40 to 100 kilometers.<sup>5</sup>

> 5 Based on Calonico, Cattaneo and Farrell (2020), we identify the optimal bandwidth for our analysis to be in this range.

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# _4.2 Sectors and tax incentives: Treated vs. non-treated_

The New Industrial Policy comprises of two major components: infrastructure investment and tax incentives. While the above analysis can identify average employment effect of all interventions, they cannot tell us whether either of them is effective or not. To shed lights on the specific channels that average effect may takes place, we exploit the differential treatment of tax incentives across sectors.

First, in order to explore differences at the sector level and prepare for later analysis, we apply the boundary discontinuity design to the sample at the town-sector level, instead of town (and village) level. We introduce fixed effects at the sector level to control for any spatial invariant characteristics of the sectors. The specification is the following: m × T T T 1 × D D 2 × D D × T T T m (2), where yy _m_ = αα+ ββ continues indexing a town (or village) close to the state border and<sup>SS</sup> mm + ωω DDT TTmm + ωω DDT TTmm _j_ denotes a 3-digit sector of mm + λλmm + εε either manufacturing or services. m represents an outcome variable for sector _j_ in town (or village) _m_ . Again, these outcomes include total employment and the number of firms for 2013, after most yy interventions had been fully implemented. represents the set of fixed effects for sectors at the 3-digit National Industrial Classification 2008 (NIC 2008) level. Similar to Equ.(1), we are interested in the mm λλ coefficient on _Target_ , , which shows the average effect of all interventions within the same sector at the town and village level in Uttarakhand relative to Uttar Pradesh at the border. Everything else has the ββ<sup>SS</sup> same meaning as in Equ.(1).

Based on CBEC’s notification in June 2003, central excise tax exemptions were not offered for a selected group of industries. Additionally, for some industries, their prevailing central excise taxes across India were zero. For all these industries, there were no effective central excise tax incentives. We expect firms receiving positive effective central excise tax incentives (treated) behave differently from those receiving zero incentives (non-treated). We identify both groups of sectors and use this intuition to parse out the impact of central excise tax incentives on employment outcomes from the effect of the overall New Industrial Policy. The specification is of the following form: m × T T T × T T T × T T 1 × D D 2 × D D × T T T 3 × D D × T T 4 × D D × T T T × T T m mm mm mm mm mm yy = αα+ ββ<sup>TT</sup> + γγ<sup>TT</sup> TT + ωω DDT TT + ωω DDT TT m mm + ωω× T T T DDT TTmm× T T TTTmm + ωω× T T DDT TTmm D ;T mmT T , T TTmmT+ λλmm + εε m (3). mm mm mm mm mm mm mm yy = αα+ ββ<sup>TT</sup> + γγ<sup>TT</sup> TT + LL[D DDT TT TT ] + λλ + εε T T T continues representing whether town _m_ is located in Uttarakhand or not. T T indicates whether sector _j_ received positive effective central excise tax incentives or not if the investment is in mm mm Uttarakhand. Because we control for sector fixed effects, we do not include T T in the specification TT and only have the interaction term T T T × T T . D ;T T T , T mmT � represents the TT linear function of _Distance_ . Everything else has the same meaning as in Equ.(2). mm mm mm mm mm TT LL�D DDT TT TT In addition to the coefficient on _Target,_ we are also interested in the coefficient on the interaction term T T T × T T . The former now measures the effect of non- central excise tax incentives on the employment outcomes of all sectors in Uttarakhand relative to Uttar Pradesh at the border, while the mm mm TT 12
latter shows the effect of central excise tax incentives on the employment outcomes of the treated sectors.<sup>6</sup> It is worth noting that a short negative list was also issued for the central corporate income tax exemptions. However, the list is much more restrictive with only bitumen, petroleum, polyester and a few motor vehicle production sectors being included. And, it has limited overlap with the negative list for the central excise tax incentives. We expect much smaller impact of this negative list for corporate income tax on firm’s investment behavior and do not control for these sectors explicitly in the baseline analysis. However, as robustness checks, we explore the impact of this negative list in two ways: first, we exclude the sectors from the analysis, and second, we add these sectors to the treated group.

# _4.3 Spillovers: Coagglomeration and Marshallian mechanisms_

In theory, the existence of agglomeration economies can have ambiguous impact on the effects of a placebased policy because of multiple equilibria. On one hand, agglomeration forces can strengthen the policy. They can continue attracting business investments to the targeted area, even after the policy is phased out. On the other hand, agglomeration economies can weaken the policy. When economic activities are initially concentrated in other areas, they can result in spatial concentration of firms that is hard to be reshaped. For this reason, a “big push” type of policy has a greater potential to overcome the threshold effect of development and turn a backward place around. The New Industrial Policy could be such a case because of its scale. In this paper, we empirically examine this hypothesis.

To quantify the impact of agglomeration economies, previous studies have used long-term data with postintervention observations (Kline and Moretti 2014a). In the absence of such data, it is challenging to separately identify agglomeration economies from the general impact of place-based policies because both could lead to the concentration of firms in the same sector in the targeted area.

We circumvent this issue by investigating spillovers between sectors. Following Ellison, Glaeser and Kerr (2010), we first construct the coagglomeration index measuring the overall tendency that two industries locate close to each other. This measure is related to the covariance of sectors across locations and accounts for both natural advantages and agglomeration economies, after controlling for sectoral differences in firm size. To mitigate the issue of endogeneity, we exclude data from Uttarakhand and Uttar Pradesh when computing the coagglomeration index for all sector pairs.

We use this coagglomeration index to examine whether employment outcomes vary due to sectors’ tendency to collocate, after controlling for the direct effect. The specification is the following: m × T T T × T T T × T T 1 × T T T × E 2 × T T T × E D ;T T T , T T m (4). TT mm mm mm mm mm mm yy = αα+ ββ<sup>CC</sup> + γγ<sup>CC</sup> TT + ρρ + ρρ NNTT E represents the sum of the coagglomeration indices between sector mm + LL[D DDT TTmm mm TTmm] + λλmm + εε _j_ and all other sectors that received positive central excise tax incentives (or treated). TT E represents the sum of the mm NNTT mm 6 Abeberese and Chaurey (2018) also study the impact of tax incentives in the two states on the number of firms using economic census data. However, they continue relying on state-wide comparison without geo-referencing the data and applying the more stringent identification strategy. They do not explore the specific channels, and only capture the impact of the interventions in the early years by using the 2005 Economic Census.

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coagglomeration index between sector _j_ and all other sectors that receive zero central excise tax incentives (or not treated). Everything else has the same meaning as in Equ.(3).

The coefficient on _Target_ now measures the direct effect of non-central excise tax incentives on the employment outcomes of all sectors, and the coefficient on the interaction term _Target X Treat_ measures the direct effect of central excise tax incentives on the treated sectors. Meanwhile, the coefficient on T T T × E captures the indirect impact of the New Industrial Policy through industries’ tendency to collocate with treated sectors. And the coefficient on TT T T T × E captures the indirect impact mm mm through industries’ tendency to collocate with nontreated sectors. NNTT mm mm One disadvantage of the coagglomeration index is that it does not distinguish between colocation tendency due to agglomeration forces from that caused by natural advantages. To better assess the impact of agglomeration forces, we construct indices to measure two important Marshallian mechanisms that can increase industries’ tendency to locate together and give rise to agglomeration, namely, inputoutput linkages and labor pooling.

The index on input-output linkages measures the extent that two sectors buy and sell from each other. It is computed from Input-Output accounts for India. Industries that serve intermediate inputs in the production processes of other sectors can minimize transportation costs by locating close to one another. In such a case, the growth of a central excise tax-incentivized sectors in Uttarakhand can induce other sectors linked through the value-chain to locate within the state.

The index on labor pooling captures the extent to which two sectors share the same type of workers. The information is based on sectors’ occupational characteristics reported by labor force surveys of India. Industries that require similar types of workers can also choose to locate in the same labor market to reduce matching costs and tap into suitable labor force in the market. In this case, the growth of a central excise tax-incentivized sectors in Uttarakhand can induce other sectors requiring similar types of labor to locate within the state.

Using these two indices, we investigate whether employment outcomes vary due to input-output linkages and/or labor pooling between industries in Uttarakhand relative to Uttar Pradesh at the border, following: m × T T T × T T T × T T 1 × T T T × I 2 × T T T × I 1 × T T T × Lmm 2 × T T Tmm × L mm D ;Tmm T TmmTT , T T mm m yy = αα+ ββ<sup>MM</sup> + γγ<sup>MM</sup> TT + θθ + θθ NNTT TT (5). NNTT mm mm mm mm mm mm mm mm mm + ηη +ηη + LL[D DDT TT TT ] + λλ + εε I represents the sum of the input-output linkage indices between sector _j_ and all other sectors that were treated. TT I represents the sum of the input-output linkage indices between sector _j_ and all other mm sectors that were not treated. NNTT L represents the sum of the labor pooling indices between sector _j_ and mm all other sectors that were treated, and TT L represents that between sector _j_ and all other sectors that mm were not treated. Everything else has the same meaning as in Equ.(4). NNTT mm As in (4), direct effects are controlled for in this specification. The coefficient on T T T × I measures the indirect impact of the New Industrial Policy through sectors’ buyer-seller relationship with TT mm mm treated sectors. The coefficient on T T T × I measures the indirect impact through sectors’ buyerseller relationship with nontreated sectors. The indirect impact due to sectors having similar labor NNTT mm mm 14
requirements with treated and nontreated sectors are captures by the coefficients on T T T × L and T T T × L , respectively. TT mm mm NNTT mm mm

# 5. Data

## _5.1 Locations, distance and employment_

The Economic Census of India is used to compute the total employment and number of firms for towns and villages and for sectors in each town or village (MOSPI 2013). We use the 2013 round of the Economic Census for our baseline results and the 1990 and 1998 rounds for robustness checks. We geo-reference all three rounds of the Economic Census to digitized boundaries of administrative units in India, available down to the town or village level. These boundaries are based on India’s Administrative Atlas _2011_ (ORGI 2011b) and were generated as part of a broader research project, the Spatial Database for South Asia (Li et al. 2015).

For each round of the Economic Census, we achieve the geo-refence through two steps. First, we create a concordance between the town/village codes used by the Economic Census and those of the Population Census of India 2011 (ORGI 2011a). 7 Second, we match the town/village codes used by the Population Census with those of the digitized administrative units, using an official concordance and addressing some additional mismatches not covered by the concordance (ORGI 2011b).

We obtain the spatial coordinates of all towns and village from the digitized boundaries of administrative units. Thereafter, we use the Open Source Routing Machine and take advantage of the road network data from OpenStreetMap to calculate the shortest driving distance from all towns and village to the border between Uttarakhand and Uttar Pradesh.<sup>8</sup> The Economic Census of India enumerates almost all non-farm establishments, including single-worker to the largest establishments, formal and informal, from both manufacturing and services sectors. For our analysis, we include all non-agricultural economic sectors, excluding public administration and defense. The 2013 Economic Census uses the three-digit National Industrial Classification 2008 (NIC 2008) to classify sectors. We follow this nomenclature in the following analysis.

Several studies have confirmed the Economic Census as a good source to study the spatial characteristics of establishments in India over longer periods. However, it has a coverage issue related to micro enterprises at the left-most end of the firm-size distribution (Amirapu et. al 2019; Manna 2010; and Raveendran 2006). Following the literature, we exclude establishments with fewer than five workers from the baseline analysis. And we use these observations in robustness checks.

In terms of places, we restrict to the towns and villages within 100-kilometer driving distance from the border in both Uttar Pradesh and Uttarakhand. Overall, there are 19,088 towns and villages in this area. Among them, over 4,100 were not canvassed in the 2013 round of the Economic Census, because of the

> 7 We use the Socioeconomic High-resolution Rural-Urban Geographic Dataset for India (SHRUG) to match the 1990 and 1998 Economic Census to the 2011 Population Census (Asher et al. 2019).

> 8 - Open Source Routing Machine can be accessed at http://project <u>osrm.org/.</u>

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<!-- Start of picture text --> Kernel density estimate<br>*<br>+<br>=<br>a<br>TM<br>24 6 8 10 12<br>Log (employment)<br>——— Uttar Pradesh<br>——— Uttarakhand<br>termel = epanechnikey, bandwidth = 0.1861, sample: locations within 100 kms on either side of<br><!-- End of picture text --> <!-- Start of picture text --> Kernel density estimate<br>=<br>o<br>=<br>at<br>mM<br>0 2 4 6 8<br>Log (total firms)<br>——— Uttar Pradesh<br>——— Uttarakhand<br>= epanechnikev, bandwidth = 0.1533, sample: locations within 100 kms on either side of borde<br><!-- End of picture text --> termel = epanechnikey, bandwidth = 0.1861, sample: locations within 100 kms on either side of bordesernel = epanechnikev, bandwidth = 0.1533, sample: locations within 100 kms on either side of borde
|Non-zero|No|Treated|If a sector was not in the negative list, and<br>the excise rate across India was non-zero,<br>then a differential tax rate existed across<br>the border.|
|---|---|---|---|

We take three steps to classify a sector as treated or untreated in terms of central excise taxes. First, we identify all 3-digit NIC 2008 codes that are contained in the CBEC’s negative list of excise exemptions in Uttarakhand. Next, we use CBEC’s publication on central excise tax rate published in 2005 to identify if a product had zero or non-zero excise rate across India.<sup>10</sup> CBEC’s publication use 8-digit Indian Tariff Code (ITC) classification to notify the tax rates. And the first five digits of the ITC code correspond to the international Harmonized System codes 2002 (HS 2002).<sup>11</sup> Finally, we create a concordance between the 5-digit HS 2002 product codes and the 3-digit NIC 2008 sector codes and identify sectors that are not in the negative list but have zero central excise taxes. We use a sequence of concordance tables, containing cross-walks from HS 2002 to CPC1.1, CPC2 and ISIC Rev.4. To classify whether a 3-digit NIC 2008 industry code had a zero or non-zero central excise tax in India, we calculate the share of all HS 2002 products with a non-zero tax rate mapped to a NIC 2008 industry code. A 3-digit NIC 2008 sector is considered to have a non-zero excise tax rate in India if this share is above 50%, and a zero rate otherwise.

The final list of sectors receiving positive effective central excise tax incentives covered 49 sectors at the 3-digit NIC 2008 level. Other sectors such as agricultural food processing industries received zero excise taxes. And all services sectors received zero excise taxes.

A short negative list was also issued for the central corporate income tax incentives. The list covers only 4 sectors at the 3-digit NIC level. Three of them overlap with the negative list for central excise tax, including manufacture of man-made fibers, bodies (coachwork) for motor vehicles and motor vehicles. And one sector does not, namely, manufacturing of refined petroleum products. We expect much smaller impact of this negative list on firm’s investment behavior and do not control for these sectors explicitly in the baseline analysis. But we explore the impact of this negative list in several robustness analyses.

# _5.3 Coagglomeration measures_

Following Ellison, Glaeser and Kerr (2010), we compute three measures on coagglomeration between sector pairs. The first measure captures the overall tendency for industries to locate within a geographical unit and is calculated for each pair of sectors in our sample, as follows: 1 − ∑ where and denote sectors, and indicates geographic areas. represents the share of sector employment in place and is the average employment share of the place across all sectors. mmii DD jj mm DD mm DD′DD mm xx

> 10 2005 is the year closest to the start of the New Industrial Policy for which such publication exists.

> 11 At the 5-digit HS 2002 level, CBEC’s publication comprises of 5,203 unique products. See Velayudhan (2019) for detailed explanation.

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E in place . We construct this index at the district level, using the 2013 round of Economic Census and iimm<sup>can be interpreted as the likelihood of industryDDandjjlocate to generate spillovers for one another</sup> excluding establishments from Uttar Pradesh and Uttarakhand from the sample. mm The second measure captures the strength of input-output linkage between industries. We calculate it between sectors and as: DD Ijj<sup>UUDDTTiim</sup> , <u>m</u> , <u>m</u> , <u>m</u> ~~)~~ mii ii mii UUDDTT UUDDTT UUDDTT iimm<sup>= arg max</sup><sup>~~(~~</sup> kk kkmm kk mmkk kk iikk kk kkii U i where denotes the value of sector ∑UUDDTT used as inputs in the production of sector ∑UUDDTT ∑UUDDTT ∑UUDDTT , and ∑U UU denotes the ratio between inputs from sector UUDDTTiimm DD and the sum of all inputs in the production of jj . The overall kk UUkkii index is the maximum value of the four pair-wise shares. The underlying data are India’s input-output DD jj matrix developed in Singh and Saluja (2016) for 2013-14 that is consistent with National Accounts Statistics 2015 and produced using the official supply use table released in 2012-13. The input-output matrix comprises of 130 unique products and services, categorized by the 3-digit IO commodity classification. We concorded pair-wise shares denoted in this classification to the 3-digit NIC 2008 codes. The third measure is on the similarity between sectors in terms of the types of workers employed. We make use of the employment module of the 2011-2012 National Sample Survey of India (NSSO 2012), which reports detailed occupation and sector information for each worker. We construct i as the fraction of sector ’s employment in occupation . As the number of observations in the National Sample SShT TT Survey data is limited, we calculate this variable at 2-digit NIC 2008 level. The similarity of worker types between industries DD and are proxied as the correlation between oo i and across all occupations, denoted as L . The values of the three measures are summarized in Table 2. mmii DD jj SShT TT SShT TT iimm **Table 2. Summary statistics of the coagglomeration measures**

||Coagglomeration index|Input-Output linkages|Laborpoolinglinkages|
|---|---|---|---|
|Mean|0.0003|0.0618|0.0861|
|Median|-0.0004|0.0219|0.0161|
|Standard deviation|0.0124|0.1068|0.3678|
|Largest value|0.6122|0.9881|1|
|Smallest value|-0.0504|0|-1|
|Number of sectorpairs|23871|24310|6662|

In the analysis, we use the three measures to calculate the indirect exposure of a sector to central excise tax incentives as: I = ∑ I T T where I denotes the coagglomeration index ( E ), input-output linkages ( TT NN I ) or labor pooling linkages ( L )between mm kk=1 mmkk mmkk industry and , and DDEEIIT T is an indicator variable that is equal to 1 if sector DDEEII ∗II{kk: T tt} DDEEII is treated or receiving central excise tax incentives. Similarly, the indirect exposure of a sector to non-excise incentives as: I jj = ∑kk II{kk: TI tt} T T . To make sure the results are comparable across kk these indices, we also normalize all NNTT NN I and I to have a zero mean and a unit standard mm kk=1 mmkk deviation. DDEEII DDEEII ∗II{kk: DDooDDT TT tt} NNTT mm mm DDEEII DDEEII 19
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columns. All standard errors are robust to heteroskedasticity.<sup>12</sup> _Target_ is estimated to have a positive and statistically significant impact on both total employment and total number of firms across all distance buffers. For both outcome variables, the coefficient on the interactive term _Distance X Target_ is estimated to be negative and significant, suggesting the impact of the New Industrial Policy measured at the border declines with distance from the border.

# **Table 3. Average effects of the New Industrial Policy** **<u>(2013, town and village level)</u>**

||||Log (employm<br>|ent)<br>||||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|0.8200<sup>***</sup>|0.8755<sup>***</sup>|0.9759<sup>***</sup>|1.0315<sup>***</sup>|1.0612<sup>***</sup>|1.0709<sup>***</sup>|1.0771<sup>***</sup>|
||(7.44)|(8.95)|(10.91)|(12.66)|(14.18)|(15.16)|(16.00)|
|_Distance_<br>_Distance X Target_|-0.0021<br>(-0.95)<br>-0.0018|-0.0021<br>(-1.29)<br>-0.0063|-0.0011<br>(-0.86)<br>-0.0125<sup>***</sup>|-0.0004<br>(-0.43)<br>-0.0155<sup>***</sup>|-0.0000<br>(-0.06)<br>-0.0170<sup>***</sup>|0.0004<br>(0.66)<br>-0.0172<sup>***</sup>|0.0012<sup>*</sup><br>(2.14)<br>-0.0169<sup>***</sup>|
|_Constant_|(-0.35)<br>2.9018<sup>***</sup><br>(47.74)|(-1.65)<br>2.9021<sup>***</sup><br>(54.79)|(-4.16)<br>2.8781<sup>***</sup><br>(61.09)|(-6.81)<br>2.8597<sup>***</sup><br>(68.24)|(-10.06)<br>2.8491<sup>***</sup><br>(75.35)|(-12.75)<br>2.8334<sup>***</sup><br>(80.77)|(-15.20)<br>2.8065<sup>***</sup><br>(86.18)|
|_No. of observations_|3652|4521|5228|5988|6859|7644|8505|
||||Log (total fir|ms)||||
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|0.3831<sup>***</sup><br>(4.56)|0.4317<sup>***</sup><br>(5.78)|0.5127<sup>***</sup><br>(7.49)|0.5474<sup>***</sup><br>(8.80)|0.5800<sup>***</sup><br>(10.19)|0.5805<sup>***</sup><br>(10.80)|0.5867<sup>***</sup><br>(11.45)|
|_Distance_|-0.0007|-0.0011|-0.0003|0.0001|0.0004|0.0005|0.0011<sup>*</sup>|
||(-0.38)|(-0.80)|(-0.29)|(0.10)|(0.68)|(0.92)|(2.38)|
|_Distance X Target_|-0.0009<br>(-0.22)|-0.0049<br>(-1.65)|-0.0100<sup>***</sup><br>(-4.20)|-0.0119<sup>***</sup><br>(-6.62)|-0.0135<sup>***</sup><br>(-10.37)|-0.0135<sup>***</sup><br>(-13.13)|-0.0134<sup>***</sup><br>(-15.91)|
|_Constant_|<br>0.8824<sup>***</sup>|<br>0.8893<sup>***</sup>|<br>0.8715<sup>***</sup>|<br>0.8606<sup>***</sup>|<br>0.8507<sup>***</sup>|<br>0.8486<sup>***</sup>|<br>0.8282<sup>***</sup>|
||(18.06)|(20.78)|(22.78)|(25.26)|(27.65)|(29.69)|(31.28)|
|_No. of observations_|3652|4521|5228|5988|6859|7644|8505|

_Note:_ All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

Overall, the results in Table 3 show that the New Industrial Policy had a significant local effect on employment and businesses. According to the point estimation, for places within 40 kilometers from the border, there was an 82 percent increase in total employment when one crosses the border from Uttar Pradesh to Uttarakhand. In terms of total firms, the point estimation suggests a 39 percent increase, which

> 12 For all specifications, clustering standard errors at the subdistrict level does not affect our main results and conclusions. The additional results with clustered standard errors are available upon request.

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is much smaller in magnitude. Thus, the difference in total employment is driven by both a net increase in the number of businesses and an increase in firm sizes.

It should also be emphasized that there is a wide confidence band around the point estimates for the impact on employment and total firms. Nevertheless, for places within 40 kilometers from the border, the lower bound of the confidence-intervals is above 61 percentage points for the logarithm of employment and above 22 percentage points for the logarithm of total firms. This finding suggests that the average effects of the New Industrial Policy are both statistically significant and large enough in magnitude to warrant attention by policy makers.

A legitimate concern of boundary discontinuity design is about the spatial discontinuity of geographic features that is independent of policies. Our analysis so far has mitigated this concern by controlling for a linear function of distance. We further check the robustness of our results against this concern in two ways.

As a more stringent analysis, we restrict our sample to towns and villages between 4 and 10 kilometers away from the border. In a sense, while these town and villages can lie on different sides of the border, many of them are contiguous or close to contiguous to each other. The issue of potential spatial discontinuity of geographic features that are not due to policies is thus further addressed in this more stringent analysis. The choice of distance buffer is consistent with those selected by Duranton, Gobillon and Overman (2011) (1-3 kilometers), and Rohlin, Rosenthal and Ross (2014) (1, 5 and 10 miles), and that by Shenoy (2018) for the study’s most stringent specification (4 kilometer).

Table 4 reports the estimated coefficients on _Target_ . Because of much smaller buffers, we do not control for distance. The results are highly consistent with those from Table 3. For towns and villages within 5 - 10 kilometers of the border, employment was 70 to 82 percent higher in Uttarakhand than in Uttar Pradesh, and the number of firms was 27 to 42 percent greater. For places within 4 kilometers of the border, the number of firms did not differ significantly when one crosses the border, but the size of firms was significantly higher in Uttarakhand than in Uttar Pradesh.

We then experiment with a placebo test to further address the concern of spatial discontinuity of geographic features. Following Rohlin, Rosenthal and Ross (2014), we create a fictitious border 50 kilometers into the interior of Uttar Pradesh. We then examine the employment patterns in 2013 across this pseudo border, following Equ.(1) and allowing the distance buffers to vary between 20 and 50 miles.

The results of the placebo test from the pseudo border further confirm the robustness of our findings. Table 5 report the results. In comparison with Table 3, for both outcome variables, _Target_ does not matter when considering the pseudo border in Uttar Pradesh. This is consistent with the view that the _true_ border between Uttar Pradesh and Uttarakhand had a noteworthy influence on business investment and location decisions, and the influence was related to the New Industrial Policy.

22
**Table 4. Average effects, 4 to 10-kilometer buffers**

**<u>(2013, town and village level)</u>**

||||Log (emplo<br>|yment)<br>||||
|---|---|---|---|---|---|---|---|
||(1)<br>4 kms|(2)<br>5 kms|(3)<br>6 kms|(4)<br>7 kms|(5)<br>8 kms|(6)<br>9 kms|(7)<br>10 kms|
|_Target_|0.6599**<br>(3.26)|0.7761***<br>(4.66)|0.7356***<br>(4.71)|0.6992***<br>(5.27)|0.7829***<br>(6.33)|0.7772***<br>(6.80)|0.8206***<br>(7.58)|
|_No. of_|195|271|350|449|530|597|678|
|_observations_|||Log (total<br>|firms)<br>||||
||(1)<br>4 kms|(2)<br>5 kms|(3)<br>6 kms|(4)<br>7 kms|(5)<br>8 kms|(6)<br>9 kms|(7)<br>10 kms|
|_Target_|0.1486|0.2687*|0.3232**|0.3578***|0.4027***|0.4126***|0.4218***|
||(0.99)|(2.14)|(2.80)|(3.63)|(4.34)|(4.79)|(5.15)|
|_No. of_|195|271|350|449|530|597|678|
|_observations_||||||||

_Note:_ All regressions include a _constant_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

**Table 5. Placebo test using a pseudo border in Uttar Pradesh**

**<u>(2013, town and village level)</u>**

|||Log (emp<br>|loyment)<br>||
|---|---|---|---|---|
||(1)<br>20 kms|(2)<br>30 kms|(3)<br>40 kms|(4)<br>50 kms|
|_Target_|-0.3611|-0.1158|-0.2009|-0.2720*|
||(-0.89)|(-0.51)|(-1.24)|(-2.22)|
|_No. of observations_|3011|4515|5691|6739|
|||Log (tot|al firms)||
||(1)|(2)|(3)|(4)|
||20 kms|30 kms|40 kms|50 kms|
|_Target_|-0.2435|-0.1021|-0.0937|-0.1603|
||(-0.72)|(-0.54)|(-0.69)|(-1.59)|
|_No. of observations_|3011|4515|5691|6739|

_Note:_ All regressions include a _constant_ , _Distance_ and _Distance X Target_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

23
Relying on the quasi-natural experiment of a state split raises the understandable concern about the validity of our research design. The difference in employment patterns might have existed across the border before the state split and thus before the introduction of the New Industrial Policy. Alternatively, there might have been a difference in employment growth trends across the border before the state split. Both would bias our results upward.

We address these concerns sequentially. First, we investigate employment patterns in modern sectors right before the state split. We geo-reference data from the 1998 Economic Census to match the towns and villages presented in the 2013 Economic Census and replicate the analysis of Equ.(1) for employment outcomes in 1998. Table 6 presents the results. _Target_ does not appear to affect total employment or total number of firms. The results hold across all distance buffers. Therefore, in 1998, there were no systematic differences in employment size and firm numbers for town and villages close to but on different sides of the state border that is defined in 2000.

Second, we address the issue of pre-split trends by examining the growth of employment and the change in total firms at the town and village level prior to the state split. To measure changes, we further georeference the data from the 1990 Economic Census. We then compute the changes in employment outcomes between 1990 and 1998 as the outcome variables and apply Equ.(1). As shown in Table 7, prior to the split, for towns and villages close to the border, neither employment nor the number of firms was growing faster in Uttarakhand than in Uttar Pradesh. If anything, among these places, employment in modern sectors was growing slower in Uttarakhand than in Uttar Pradesh.

Admittedly, the matching between towns and villages over the three rounds of Economic Censuses is not perfect. Depending on the distance buffers, we capture 54 to 60 percent of the places when looking at the patterns in 1998, and 29 to 31 percent of places when analyzing the pre-split trends between 1990 and 1998. In addition, Abeberese and Chaurey (2019) and Chaurey (2017) find similar results on the patterns and trends of employment in manufacturing industries before 2000, using data different from ours and for districts close to the border. Overall, the quasi-natural experiment nature of the state split is supported by our analysis and previous studies on this topic.

24
**Table 6. Employment patterns prior to the state split**

# **<u>(1998, town and village level)</u>**

|||Log (<br>|employme<br>|nt)<br>||||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|0.2911|0.1864|0.1887|0.1219|0.0645|0.1032|0.1135|
||(1.94)|(1.44)|(1.58)|(1.09)|(0.64)|(1.11)|(1.31)|
|_No. of observations_|1962|2526|3033|3542|4111|4628|5128|
|||Log|(total firm|s)||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||40 kms|50 kms|60 kms|70 kms|80 kms|90 kms|100 kms|
|_Target_|0.1748|0.1284|0.1459|0.1030|0.0848|0.1138|0.1102|
||(1.55)|(1.31)|(1.60)|(1.19)|(1.10)|(1.62)|(1.68)|
|_No. of observations_|1962|2526|3033|3542|4111|4628|5128|

_Note:_ The analysis is based on towns and villages that are matched to the 2013 Economic Census. All regressions include a _constant_ , _Distance_ and _Distance X Target_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

**Table 7. Employment trends prior to the state split** **<u>(1990-1998, town and village level)</u>**

|||ΔL<br>|og(employm<br>|ent)<br>||||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|-0.3040|-0.3187|-0.3089*|-0.2411|-0.2860*|-0.2473*|-0.2449*|
||(-1.56)|(-1.87)|(-1.99)|(-1.75)|(-2.31)|(-2.14)|(-2.23)|
|_No. of observations_|1057|1333|1591|1870|2157|2398|2642|
|||Δ<br>|Log (total fir<br>|ms)<br>||||
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|-0.2430|-0.1656|-0.1184|-0.0933|-0.1232|-0.0888|-0.0933|
||(-1.76)|(-1.37)|(-1.06)|(-0.93)|(-1.40)|(-1.09)|(-1.20)|
|_No. of observations_|1057|1333|1591|1870|2157|2398|2642|

_Note:_ The analysis is based on towns and villages that are matched to the 2013 Economic Census. All regressions include a _constant_ , _Distance_ and _Distance X Target_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

25
# _6.2 Within-sector effects and sectoral heterogeneity_

In this section, we investigate the impact of the New Industrial Policy within a sector. Two questions are addressed sequentially: first, was there an abrupt increase in economic activities even within the same sector in Uttarakhand relative to Uttar Pradesh at the border, and second, was the increase in activities within the same sector more prominent for sectors receiving positive central excise tax incentives than for other sectors?

Table 8 presents the results on the average within-sector effects, controlling for spatial invariant sector characteristics as in Equ.(2). In comparison with Table 3, several differences are noteworthy. When looking at employment within a sector, _Target_ is still estimated to have positive and statistically significant impact. But its magnitude is much smaller. For places within the 40-kilometer distance buffer, the magnitude is about one third of that in Table 3. Regarding total number of firms within a sector, _Target_ does not seem to matter anymore.

The combination of these results has several interesting implications. First, at the border, the New Industrial Policy led to a net increase in the number of firms in bordering places of Uttarakhand in comparison to similar places in Uttar Pradesh. However, most of the net increase is due to business investments in newer sectors. Second, the interventions have also resulted in an increase in firm sizes in Uttarakhand in comparison with Uttar Pradesh at the border. The increase is driven both by new investments in sectors that tend to have larger firms and the growth in firm size within the same sector.

The increase of firm size within a sector is of the magnitude of 27 percent for places within 40 kilometers from the border. The literature suggests that productivity distribution is disperse among firms even within a sector, and tends to correlate with size distribution (Bartelsman, Haltiwanger and Scarpetta 2013; Hsieh and Klenow 2009). Thus, our finding of a larger firm size suggests that there could be productivity gains associated with the New Industrial Policy.

Next, we apply Equ.(3) to investigate the impact of central excise tax incentives. The results are reported in Table 9. Consistent with Table 8, we find significant within-sector impact on employment but not on the number of firms. Focusing on employment as the outcome variable, _Target_ remains a positive and significant explanatory variable across all distance buffers. Clearly, non-central excise tax interventions, such as infrastructure investments, central corporate income taxes and capital investment subsidies, had significant impact on employment for businesses within the same sector. Meanwhile, the interaction term between targeted places and treated sectors ( _Target X Treat_ ) appears to be positively associated with employment across all distance buffers. Central excise tax incentives mattered as well.

Let’s take places within 40 kilometers from the border as an example to understand the economic magnitudes of the impact. According to the point estimation, for all sectors, non-central excise tax interventions led to 20 percent increase in employment in Uttarakhand in comparison with Uttar Pradesh at the border. Comparing with the results in Table 8, we find this impact accounts for the lion’s share of the average within-sector effect. However, for sectors receiving positive effective central excise tax incentives, the increase was indeed much more prominent. Based on the point estimation, on average, central excise tax incentives resulted in an additional 44 percent increase in employment in these sectors, doubling the impact of the non-central excise tax interventions.

26
**Table 8. Average within-sector effects of the New Industrial Policy** **<u>(2013, town-sector level)</u>**

|||L|og (employm|ent)||||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|0.2683<sup>***</sup>|0.2354<sup>***</sup>|0.3031<sup>***</sup>|0.2829<sup>***</sup>|0.2895<sup>***</sup>|0.3206<sup>***</sup>|0.3470<sup>***</sup>|
||(6.34)|(6.30)|(8.59)|(8.88)|(9.78)|(11.52)|(13.10)|
|_Distance_|0.0018|-0.0017<sup>*</sup>|0.0011|-0.0004|-0.0008<sup>*</sup>|-0.0005|0.0003|
||(1.60)|(-2.22)|(1.60)|(-0.71)|(-2.09)|(-1.54)|(1.06)|
|_Distance X Target_|-0.0004|0.0011|-0.0020|-0.0015|-0.0021<sup>*</sup>|-0.0035<sup>***</sup>|-0.0047<sup>***</sup>|
|_Constant_|(-0.22)<br>2.4447<sup>***</sup>|(0.75)<br>2.5052<sup>***</sup>|(-1.57)<br>2.4375<sup>***</sup>|(-1.49)<br>2.4704<sup>***</sup>|(-2.56)<br>2.4815<sup>***</sup>|(-5.34)<br>2.4702<sup>***</sup>|(-8.45)<br>2.4461<sup>***</sup>|
||(84.30)|(100.17)|(103.42)|(120.52)|(135.18)|(144.62)|(151.25)|
|_No. of observations_|11984|14248|16223|18215|20296|22356|24546|
||||Log (total fir|ms)||||
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|-0.0175|-0.0474|0.0112|-0.0160|-0.0173|0.0018|0.0219|
||(-0.58)|(-1.79)|(0.44)|(-0.70)|(-0.83)|(0.09)|(1.17)|
|_Distance_|0.0024<sup>**</sup><br>(2.84)|-0.0010<br>(-1.70)|0.0020<sup>***</sup><br>(3.57)|0.0003<br>(0.73)|-0.0003<br>(-1.00)|-0.0003<br>(-1.06)|0.0003<br>(1.46)|
|_Distance X Target_|0.0001<br>(0.09)|0.0012<br>(1.18)|-0.0013<br>(-1.46)|-0.0006<br>(-0.83)|-0.0008<br>(-1.42)|-0.0018<sup>***</sup><br>(-3.95)|-0.0028<sup>***</sup><br>(-7.33)|
|_Constant_|0.4320<sup>***</sup><br>(19.33)|0.4958<sup>***</sup><br>(25.52)|0.4263<sup>***</sup><br>(22.45)|0.4692<sup>***</sup><br>(28.82)|0.4865<sup>***</sup><br>(33.46)|0.4837<sup>***</sup><br>(35.74)|0.4650<sup>***</sup><br>(36.17)|
|_No. of observations_|11984|14248|16223|18215|20296|22356|24546|

_Note:_ All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

27
**Table 9. Within-sector effects with heterogeneous central excise tax incentives** **<u>(2013, town-sector level)</u>**

||||Log (employ|ment)||||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|0.2015<sup>***</sup><br>(4.78)|0.1493<sup>***</sup><br>(4.01)|0.2160<sup>***</sup><br>(6.11)|0.1971<sup>***</sup><br>(6.16)|0.2003<sup>***</sup><br>(6.75)|0.2337<sup>***</sup><br>(8.37)|0.2636<sup>***</sup><br>(9.91)|
|_Target X Treat_|0.4415<sup>**</sup>|0.5734<sup>***</sup>|0.5862<sup>***</sup>|0.6092<sup>***</sup>|0.6477***|0.6446<sup>***</sup>|0.6280<sup>***</sup>|
||(2.78)|(4.12)|(4.53)|(5.14)|(5.74)|(6.04)|(6.10)|
|_Distance_|0.0025<sup>*</sup>|-0.0016<sup>*</sup>|0.0013|-0.0004|-0.0011<sup>**</sup>|-0.0008<sup>**</sup>|-0.0001|
||(2.28)|(-2.04)|(1.88)|(-0.83)|(-2.90)|(-2.63)|(-0.42)|
|_Distance X Target_|0.0006|0.0032<sup>*</sup>|0.0000|0.0003|-0.0004|-0.0020<sup>**</sup>|-0.0033<sup>***</sup>|
||(0.32)|(2.24)|(0.02)|(0.33)|(-0.45)|(-3.00)|(-6.01)|
|_Distance X Treat_|-0.0045|-0.0003|-0.0011|0.0007|0.0024|0.0026<sup>*</sup>|0.0029<sup>**</sup>|
||(-1.10)|(-0.11)|(-0.47)|(0.42)|(1.75)|(2.31)|(3.03)|
|_Distance X Target_|-0.0066|-0.0145<sup>**</sup>|-0.0140<sup>**</sup>|-0.0144<sup>***</sup>|-0.0146<sup>***</sup>|-0.0142<sup>***</sup>|-0.0133<sup>***</sup>|
|_X Treat_|(-0.92)|(-2.72)|(-2.99)|(-3.63)|(-3.94)|(-4.33)|(-4.35)|
|_Constant_|2.4411<sup>***</sup>|2.5018<sup>***</sup>|2.4350<sup>***</sup>|2.4682<sup>***</sup>|2.4798<sup>***</sup>|2.4687<sup>***</sup>|2.4446<sup>***</sup>|
||(82.26)|(98.15)|(102.22)|(119.59)|(134.34)|(144.05)|(150.95)|
|_No. of observations_|11984|14248|16223|18215|20296|22356|24547|
||||Log (total fi|rms)||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||40 kms|50 kms|60 kms|70 kms|80 kms|90 kms|100 kms|
|_Target_|-0.0214|-0.0570<sup>*</sup>|0.0001|-0.0220|-0.0275|-0.0070|0.0154|
||(-0.68)|(-2.07)|(0.00)|(-0.93)|(-1.27)|(-0.34)|(0.79)|
|_Target X Treat_|0.0270|0.0670|0.0822|0.0574|0.0938|0.0865|0.0734|
||(0.27)|(0.75)|(0.97)|(0.75)|(1.31)|(1.28)|(1.14)|
|_No. of observations_|11984|14248|16223|18215|20296|22356|24547|

_Note:_ All regressions include a _constant_ , and a linear function of _Distance_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

We note in the previous sections that central corporate income tax had a negative list as well. It can be argued that corporate income taxes are generally larger and because of these few sectors, our specification might capture the impact of both the central excise and the central corporate income tax incentives. To check to what extent this other negative list influences our results, we drop all four sectors that are among the corporate tax exemptions negative list and re-conduct the analysis of Equ.(3). As an alternative check, we include these sectors as treated sectors. The results of both analyses are highly consistent with those reported in Table 9 and available upon request.

Another legitimate concern is on the impact of infrastructure investments and whether our estimates on tax incentives are biased upward because we do not control for these investments explicitly. The main argument favoring our approach is that we do not expect the impact of infrastructure investments to vary 28
systematically between the treated and non-treated sectors with respect to tax incentives. Nonetheless, we measure access to electricity, road and railway station at the town and village-level using data from the India Census 2011 and introduce these variables as additional controls in Equ.(3) to check the robustness of our estimates. The results regarding total employment are reported in the top panel of table 10. Both _Target_ and the interaction term _Target X Treat_ remain positively associated with employment. And the magnitudes of the estimates are comparable to those reported in table 9.

**Table 10. Within-sector effects with heterogeneous central excise tax incentives, robustness checks**

**<u>(2013, town-sector level)</u>**

||Log (e<br>|mployment),<br>|controllingf<br>|or access to i<br>|nfrastructure<br>|<br>||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|0.2191***<br>(5.31)|0.1556***<br>(4.23)|0.2215***<br>(6.33)|0.1630***<br>(5.16)|0.1519***<br>(5.16)|0.1844***<br>(6.63)|0.2061***<br>(7.76)|
|_Target X Treat_|0.5212**|0.6356***|0.6451***|0.6674***|0.7130***|0.7090***|0.6876***|
||(3.28)|(4.55)|(4.94)|(5.55)|(6.22)|(6.54)|(6.57)|
|Infrastructure|Y|Y|Y|Y|Y|Y|Y|
|_No. of_|11984|14248|16223|18215|20296|22356|24547|
|_observations_||Log (<br>|employment)<br>|,micro firms<br>|<br>|||
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|-0.0383|0.0030|0.0423*|0.0220|0.0382*|0.0663***|0.0683***|
||(-1.51)|(0.14)|(2.05)|(1.18)|(2.24)|(4.14)|(4.50)|
|_Target X Treat_|-0.0615|-0.0771|-0.1029|-0.0826|-0.0804|-0.0906|-0.1112*|
||(-0.83)|(-1.18)|(-1.68)|(-1.46)|(-1.53)|(-1.84)|(-2.36)|
|_No. of_<br>_observations_|34516|41975|48325|54640|61601|67649|74058|

_Note:_ All regressions include a _constant_ , and a linear function of _Distance_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

As another robustness check, we restrict to a sample of micro firms—those with fewer than five workers, and replicated the analysis of Equ.(3). We expect the estimated impact of the New Industrial Policy to be small among these firms, in contrast to the impact on the larger firms. It is because most of the micro firms are subsistence entrepreneurship and they tend to stay small regardless the benefits offered by the New Industrial Policy. In addition, we expect the impact of exercise tax incentives to be particularly limited as these micro firms are much less likely to comply with tax codes than the larger firms. The results regarding total employment are reported in the bottom panel of table 10. Indeed, we find no impact of exercise tax incentives and limited impact of other interventions on employment outcomes among micro firms in the border area. While the coverage of micro firms by the Economic Census is of lower quality, 29
the results provide further evidence for the impact of the New Industrial Policy by confirming the differences between micro- and larger firms.

# _6.3 Indirect effects through spillovers_

In this section, we explore whether the New Industrial Policy generated indirect local impact because of agglomeration economies. Table 11 presents the results from Equ.(4), in which we use a measure on the overall coagglomeration tendency between sectors to capture spillovers. The conclusion that both central excise tax incentives and non-central excise tax interventions have significant direct impact on employment even for businesses within the same sector still holds in this extended specification.

Controlling for these direct effects, we find that sectors with a stronger tendency to collocate with sectors receiving positive excise tax incentives saw a net increase in firm size as one crosses the border from Uttar Pradesh to Uttarakhand. In contrast, controlling for direct effects, sectors with a stronger tendency to collocate with non-treated sectors see both a net decline in firm size and a reduction in the number of firms.

These results suggest the New Industrial Policy had significant indirect local employment effects through ties between industries. These indirect effects could be due to the typical benefits of agglomeration economies: greater product diversification, better labor matching, and stronger knowledge spillovers. Being located in close proximity made firm in different industries more productive, which in turn attracted more investments.

Applying Equ.(5), we probe the indirect effects and explore which benefit(s) might have been more prominent. We focus on two Marshallian mechanisms, input-output linkages and labor pooling, that can lead to spillovers between industries. Table 12 reports the results, which are mostly comparable to those in Table 10, except that _Target_ appears to be marginally matter for the total number of firms. Overall, the results on direct effects continues to hold in this specification. Additionally, the results on labor-pooling related variables are strong and consistent with the results in Table 11 while the results on input-output linkages related measures are weak and do not show consistent patterns.

Controlling for direct effects, sectors sharing similar labor requirements with sectors receiving positive excise tax incentives saw a net increase in firm size as one crosses the border from Uttar Pradesh to Uttarakhand. Instead, sectors that have stronger labor-related ties to non-treated sectors saw a decline in firm size and a slightly reduction in the number of firms. Overall, excise tax incentives not only affect treated sectors directly but also impact other sectors depending on whether they share a labor pool with the treated sectors or not.

For places within 40 kilometers from the border, a one-standard deviation increase in labor-related ties to sectors receiving positive excise tax incentives would result in an additional 0.6 percent employment increase in a sector in Uttarakhand compared to in Uttar Pradesh. Conversely, a one-standard deviation increase in labor-related ties to sectors not receiving the incentives would lead to an additional decline of 0.4 percent in a sector’s employment in Uttarakhand compared to in Uttar Pradesh.

30
# **Table 11. Indirect effects, with coagglomeration index**

# **<u>(2013, town-sector level)</u>**

||||Log (employ<br>|ment)<br>||||
|---|---|---|---|---|---|---|---|
||(1)<br>40 kms|(2)<br>50 kms|(3)<br>60 kms|(4)<br>70 kms|(5)<br>80 kms|(6)<br>90 kms|(7)<br>100 kms|
|_Target_|0.2223<sup>***</sup>|0.1651<sup>***</sup>|0.2231<sup>***</sup>|0.2170<sup>***</sup>|0.2286<sup>***</sup>|0.2610<sup>***</sup>|0.2707<sup>***</sup>|
|_Target X Treat_<br>_Target X EG_<sup>_T_</sup>|(4.52)<br>0.3034<br>(1.89)<br>0.0136<sup>***</sup><br>(4.42)|(3.77)<br>0.4449<sup>**</sup><br>(3.15)<br>0.0116<sup>***</sup><br>(4.19)|(5.41)<br>0.4675<sup>***</sup><br>(3.55)<br>0.0105<sup>***</sup><br>(3.89)|(5.76)<br>0.4801<sup>***</sup><br>(3.98)<br>0.0113<sup>***</sup><br>(4.40)|(6.53)<br>0.5144<sup>***</sup><br>(4.47)<br>0.0116<sup>***</sup><br>(4.68)|(7.90)<br>0.5090<sup>***</sup><br>(4.69)<br>0.0117<sup>***</sup><br>(4.83)|(8.58)<br>0.5206<sup>***</sup><br>(4.97)<br>0.0087<sup>***</sup><br>(3.56)|
|_Target X EG_<sup>_NT_</sup>|-0.0096<sup>***</sup><br>(-3.85)|-0.0085<sup>***</sup><br>(-3.65)|-0.0085<sup>***</sup><br>(-3.85)|-0.0072<sup>***</sup><br>(-3.49)|-0.0062<sup>**</sup><br>(-3.08)|-0.0063<sup>**</sup><br>(-3.21)|-0.0067<sup>***</sup><br>(-3.50)|
|_No. of_|11984|14248|16223|18215|20296|22356|24546|
|_observations_||||||||
||||Log (total fi|rms)||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||40 kms|50 kms|60 kms|70 kms|80 kms|90 kms|100 kms|
|_Target_|-0.0636|-0.0916<sup>**</sup>|-0.0476|-0.0580<sup>*</sup>|-0.0543<sup>*</sup>|-0.0309|-0.0243|
||(-1.77)|(-2.87)|(-1.56)|(-2.11)|(-2.14)|(-1.29)|(-1.06)|
|_Target X Treat_|0.0227|0.0671|0.0937|0.0631|0.0941|0.0834|0.0870|
||(0.22)|(0.74)|(1.08)|(0.81)|(1.28)|(1.21)|(1.32)|
|_Target X EG_<sup>_T_</sup>|-0.0004|-0.0008|-0.0021|-0.0014|-0.0009|-0.0006|-0.0025|
||(-0.20)|(-0.49)|(-1.24)|(-0.88)|(-0.55)|(-0.38)|(-1.62)|
|_Target X EG_<sup>_NT_</sup>|-0.0055<sup>**</sup>|-0.0042<sup>**</sup>|-0.0049<sup>**</sup>|-0.0040<sup>**</sup>|-0.0033<sup>*</sup>|-0.0032<sup>*</sup>|-0.0041<sup>**</sup>|
||(-3.14)|(-2.60)|(-3.26)|(-2.84)|(-2.42)|(-2.45)|(-3.10)|
|_No. of_<br>_observations_|11984|14248|16223|18215|20296|22356|24546|

_Note:_ All regressions include a _constant_ , and a linear function of _Distance_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

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# **Table 12. Indirect effects, with measures on input-output and labor pooling linkages** **<u>(2013, town-sector level)</u>**

||||Log (employ<br>|ment)<br>||||
|---|---|---|---|---|---|---|---|
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||40 kms|50 kms|60 kms|70 kms|80 kms|90 kms|100 kms|
|_Target_|0.3107***<br>(5.47)|0.2325***<br>(4.55)|0.2972***<br>(6.09)|0.2960***<br>(6.53)|0.3089***<br>(7.22)|0.3387***<br>(8.28)|0.3372***<br>(8.47)|
|_Target X Treat_|0.2604<br>(1.56)|0.4262**<br>(2.89)|0.4349**<br>(3.16)|0.4270***<br>(3.38)|0.4519***<br>(3.75)|0.4557***<br>(3.99)|0.4961***<br>(4.49)|
|_Target X IO_<sup>_T_</sup>|0.0022|0.0017|0.0012|0.0017|0.0023|0.0023|0.0014|
||(1.34)|(1.09)|(0.77)|(1.21)|(1.67)|(1.70)|(1.02)|
|_Target X IO_<sup>_NT_</sup>|-0.0007|-0.0004|-0.0005|-0.0007|-0.0009*|-0.0009*|-0.0006|
||(-1.21)|(-0.84)|(-0.97)|(-1.54)|(-2.02)|(-1.97)|(-1.37)|
|_Target X LP_<sup>_T_</sup><br>_Target X LP_<sup>_NT_</sup>|0.0068***<br>(3.35)<br>-0.0041***<br>(-4.65)|0.0055**<br>(2.93)<br>-0.0034***<br>(-4.18)|0.0057**<br>(3.18)<br>-0.0034***<br>(-4.39)|0.0067***<br>(3.89)<br>-0.0036***<br>(-4.90)|0.0069***<br>(4.16)<br>-0.0036***<br>(-4.96)|0.0067***<br>(4.14)<br>-0.0034***<br>(-4.83)|0.0046**<br>(2.87)<br>-0.0027***<br>(-3.94)|
|_No. of_|11980|14244|16218|18209|20290|22350|24539|
|_observations_||||||||
||||Log (total fi|rms)||||
||(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||40 kms|50 kms|60 kms|70 kms|80 kms|90 kms|100 kms|
|_Target_|-0.6762|-1.1039**|-0.8029|-1.2698|-1.4340*|-1.3325*|-1.2290*|
||(-1.70)|(-3.17)|(-0.85)|(-1.56)|(-2.04)|(-2.14)|(-2.17)|
|_Target X Treat_|0.0335|0.0925|0.1124|0.0731|0.1023|0.0934|0.1178|
||(0.32)|(0.99)|(1.26)|(0.91)|(1.34)|(1.29)|(1.70)|
|_Target X IO_<sup>_T_</sup>|-0.0016|-0.0017|-0.0022*|-0.0019|-0.0015|-0.0015|-0.0020*|
||(-1.39)|(-1.66)|(-2.10)|(-1.94)|(-1.66)|(-1.69)|(-2.26)|
|_Target X IO_<sup>_NT_</sup>|0.0007|0.0008*|0.0008**|0.0007*|0.0006*|0.0006*|0.0007**|
||(1.94)|(2.37)|(2.59)|(2.38)|(2.08)|(2.21)|(2.70)|
|_Target X LP_<sup>_T_</sup>|0.0011|0.0003|0.0003|0.0008|0.0008|0.0009|-0.0005|
||(0.87)|(0.25)|(0.23)|(0.72)|(0.81)|(0.94)|(-0.49)|
|_Target X LP_<sup>_NT_</sup>|-0.0013*|-0.0007|-0.0008|-0.0008|-0.0007|-0.0008|-0.0003|
||(-2.14)|(-1.35)|(-1.49)|(-1.58)|(-1.56)|(-1.67)|(-0.74)|
|_No. of_<br>_observations_|11980|14244|16218|18209|20290|22350|24539|

_Note:_ All regressions include a _constant_ , and a linear function of _Distance_ . All standard errors are heteroskedastic robust. _t_ statistics in parentheses.<sup>*</sup> _p_ < 0.05,<sup>**</sup> _p_ < 0.01,<sup>***</sup> _p_ < 0.001.

32
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pollution from the combustion of fossil fuel is a major concern at the national scale due to the wide use of coal as the source of energy (Greenstone et al. 2012). Therefore, if there is significant displacement of firms in these places, we expect a drop in the particle pollution level post 2003 for the bordering towns and villages in Uttar Pradesh. In contrast, we find that PM 2.5 level consistently increased between 2003 and 2013 for the places within the 100-kilometer buffer to the border.<sup>13</sup> This finding does not support that industrial activities on the Uttar Pradesh side declined after the New Industrial Policy.

Our findings are consistent with the literature as well. Using panel data of formal manufacturing industries, Chaurey (2017) shows that there is no evidence that more factories closed down in the neighboring states, including Uttar Pradesh, compared to Uttarakhand. The study also finds no evidence of differential closure of firms or relocation of industrial activity in the neighboring states, including Uttar Pradesh, and in other major states in India before and after the implementation of the New Industrial Policy. Independently, Shenoy (2018) shows that the New Industrial Policy did not dampen output in Uttar Pradesh as being measured by the panel data on nighttime light intensity. Instead, output grew rapidly over the time in places close to the border between Uttar Pradesh and Uttarakhand. While the evidence of Chaurey (2017) is at the state level and Shenoy’s (2018) results are based on aggregated output, the combination of the two studies and our own analysis suggest that the impact of New Industrial Policy is not simply a relocation and displacement story.

Could policies other than the place-based New Industrial Policy be influencing our results? As mentioned in the empirical strategy section, other major policy initiatives were effective in both states, such as the Electricity Act 2003 and the Special Economic Zones Act of 2005. These policies are likely to not affect our results. Another potentially confounding candidate is the overall doing business environment in a state. Taking the state-level World Bank Doing Business indices as proxies, we find that Uttar Pradesh had implemented more reforms to improve business environment than Uttarakhand between 2002 and 2015 (World Bank 2015). Criminal record rate is another proxy for overall quality of governance. Based on data reported by the National Crime Records Bureau, we find that Uttar Pradesh and Uttarakhand’s rates of cognizable crimes experienced a parallel trend between 2001 and 2012, with Uttar Pradesh seeing greater year-to-year variations (NCRB 2012).

Overall, the New Industrial Policy, as a comprehensive package of infrastructure investment and fiscal incentives, stands out as the most important policy difference between the two states over the time period. The analysis on the impact of differential tax treatments across sectors gives us further confidence that our results reveal the impact of the New Industrial Policy more than other policies.

# 7. Conclusion

Previous studies have shown place-based policies to have mixed effects on economies. In this paper, we find that a large-scale industrial promotion scheme targeting the Indian state of Uttarakhand had a large positive impact on local employment outcomes. We apply a boundary discontinuity design and examine the impact of this policy in places close to the border between Uttar Pradesh and Uttarakhand, which were one state right before the promotion scheme. Most interestingly, we show that the place-based excise tax incentives as part of the promotion scheme had a direct positive impact on employment in the

> 13 The data are satellite-based and drawn from von Donkelaar (2016).

34
sectors that received these incentives, agglomeration forces led to employment gains in sectors that were not directly incentivized by the policy, and the indirect effects from externalities were more significant through the labor-pooling mechanism than through the input-output linkages.

We caution, however, that our findings do not fully rule out the displacement effect and cannot speak to the national-level welfare impact of the place-based industrial promotion scheme. This will be a useful topic for future research. Additionally, more research is required to assess if agglomeration economies were strong enough for industries to continue co-locating in these areas, once the incentive policies were rolled back. The availability of new data in the coming years makes this a promising research direction.

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