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 RegionOffice of the Chief EconomistNovember 2020

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Produced by the Research Support Team

Abstract

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.

Policy Research Working Paper 9477

Many governments in developing countries have pursued policies targeting specific geographic areas over the past sev-eral decades. However, only a few have rigorously evaluated the causal impact of these interventions. This paper exam-ines 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 Uttara-khand 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 Uttara-khand 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 incen-tives 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.

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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

70 Lodi Estate

New Delhi

110003

India

The World Bank

1818 H Street NW

Washington, DC

20433

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.

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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

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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 within-sector 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

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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 difference-in-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

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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, place-based 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.1

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,

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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.2 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.3

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 mini-truck. 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.4

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 https://www.indiabudget.gov.in/budget_archive/ub2003-04/fb/bill813.pdf. 4 The article can be accessed at: https://www.business-standard.com/article/beyond-business/the-next-boom-town-106121601012_1.html.

https://www.indiabudget.gov.in/budget_archive/ub2003-04/fb/bill813.pdfhttps://www.business-standard.com/article/beyond-business/the-next-boom-town-106121601012_1.htmlhttps://www.business-standard.com/article/beyond-business/the-next-boom-town-106121601012_1.html

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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:

𝑦𝑦𝑚𝑚 = 𝛼𝛼 + 𝛽𝛽𝐵𝐵 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝜔𝜔1 × 𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚 + 𝜔𝜔2 × 𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝜀𝜀𝑚𝑚 (1),

where m indexes a town or village close to the state border. 𝑦𝑦𝑚𝑚 represents an outcome variable for all manufacturing and services sectors in town or village m, including total employment and the number of 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, 𝛽𝛽𝐵𝐵 , 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 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.5

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:

𝑦𝑦𝑚𝑚𝑚𝑚 = 𝛼𝛼 + 𝛽𝛽𝑆𝑆 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝜔𝜔1 × 𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚 + 𝜔𝜔2 × 𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝜆𝜆𝑚𝑚 + 𝜀𝜀𝑚𝑚𝑚𝑚 (2),

where m continues indexing a town (or village) close to the state border and j denotes a 3-digit sector of either manufacturing or services. 𝑦𝑦𝑚𝑚𝑚𝑚 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 interventions had been fully implemented. 𝜆𝜆𝑚𝑚 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 coefficient on Target, 𝛽𝛽𝑆𝑆 , 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 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:

𝑦𝑦𝑚𝑚𝑚𝑚 = 𝛼𝛼 + 𝛽𝛽𝑇𝑇 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝛾𝛾𝑇𝑇 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 +𝜔𝜔1 × 𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚 + 𝜔𝜔2 × 𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚 ×𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝜔𝜔3 × 𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝜔𝜔4 × 𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝜆𝜆𝑚𝑚 + 𝜀𝜀𝑚𝑚𝑚𝑚

𝑦𝑦𝑚𝑚𝑚𝑚 = 𝛼𝛼 + 𝛽𝛽𝑇𝑇 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝛾𝛾𝑇𝑇 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝐿𝐿[𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚;𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚,𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚] + 𝜆𝜆𝑚𝑚 + 𝜀𝜀𝑚𝑚𝑚𝑚 (3).

𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 continues representing whether town m is located in Uttarakhand or not. 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 indicates whether sector j received positive effective central excise tax incentives or not if the investment is in Uttarakhand. Because we control for sector fixed effects, we do not include 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 in the specification and only have the interaction term 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚. 𝐿𝐿�𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚;𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚,𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚� represents the linear function of Distance. Everything else has the same meaning as in Equ.(2).

In addition to the coefficient on Target, we are also interested in the coefficient on the interaction term 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 . 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

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latter shows the effect of central excise tax incentives on the employment outcomes of the treated sectors.6

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 place-based 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 post-intervention 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:

𝑦𝑦𝑚𝑚𝑚𝑚 = 𝛼𝛼 + 𝛽𝛽𝐶𝐶 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝛾𝛾𝐶𝐶 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝜌𝜌1 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐸𝐸𝐸𝐸𝑚𝑚𝑇𝑇 + 𝜌𝜌2 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 ×𝐸𝐸𝐸𝐸𝑚𝑚𝑁𝑁𝑇𝑇 + 𝐿𝐿[𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚;𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚,𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚] + 𝜆𝜆𝑚𝑚 + 𝜀𝜀𝑚𝑚𝑚𝑚 (4).

𝐸𝐸𝐸𝐸𝑚𝑚𝑇𝑇 represents the sum of the coagglomeration indices between sector j and all other sectors that received positive central excise tax incentives (or treated). 𝐸𝐸𝐸𝐸𝑚𝑚𝑁𝑁𝑇𝑇 represents the sum of the

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 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐸𝐸𝐸𝐸𝑚𝑚𝑇𝑇 captures the indirect impact of the New Industrial Policy through industries’ tendency to collocate with treated sectors. And the coefficient on 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐸𝐸𝐸𝐸𝑚𝑚𝑁𝑁𝑇𝑇 captures the indirect impact through industries’ tendency to collocate with nontreated sectors.

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, input-output 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:

𝑦𝑦𝑚𝑚𝑚𝑚 = 𝛼𝛼 + 𝛽𝛽𝑀𝑀 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝛾𝛾𝑀𝑀 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 + 𝜃𝜃1 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐼𝐼𝐼𝐼𝑚𝑚𝑇𝑇 + 𝜃𝜃2 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 ×𝐼𝐼𝐼𝐼𝑚𝑚𝑁𝑁𝑇𝑇 + 𝜂𝜂1 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐿𝐿𝐿𝐿𝑚𝑚𝑇𝑇+𝜂𝜂2 × 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐿𝐿𝐿𝐿𝑚𝑚𝑁𝑁𝑇𝑇 + 𝐿𝐿[𝐷𝐷𝐷𝐷𝐷𝐷𝑇𝑇𝑇𝑇𝐷𝐷𝐷𝐷𝑇𝑇𝑚𝑚;𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚,𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚] + 𝜆𝜆𝑚𝑚 + 𝜀𝜀𝑚𝑚𝑚𝑚

(5).

𝐼𝐼𝐼𝐼𝑚𝑚𝑇𝑇represents the sum of the input-output linkage indices between sector j and all other sectors that were treated. 𝐼𝐼𝐼𝐼𝑚𝑚𝑁𝑁𝑇𝑇represents the sum of the input-output linkage indices between sector j and all other sectors that were not treated. 𝐿𝐿𝐿𝐿𝑚𝑚𝑇𝑇represents the sum of the labor pooling indices between sector j and all other sectors that were treated, and 𝐿𝐿𝐿𝐿𝑚𝑚𝑁𝑁𝑇𝑇represents that between sector j and all other sectors that were not treated. Everything else has the same meaning as in Equ.(4).

As in (4), direct effects are controlled for in this specification. The coefficient on 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐼𝐼𝐼𝐼𝑚𝑚𝑇𝑇 measures the indirect impact of the New Industrial Policy through sectors’ buyer-seller relationship with treated sectors. The coefficient on 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐼𝐼𝐼𝐼𝑚𝑚𝑁𝑁𝑇𝑇measures the indirect impact through sectors’ buyer-seller relationship with nontreated sectors. The indirect impact due to sectors having similar labor

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requirements with treated and nontreated sectors are captures by the coefficients on 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐿𝐿𝐿𝐿𝑚𝑚𝑇𝑇 and 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚 × 𝐿𝐿𝐿𝐿𝑚𝑚𝑁𝑁𝑇𝑇, respectively.

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.8

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-osrm.org/.

http://project-osrm.org/

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absence of non-agricultural establishments. Nearly 6,400 of them had no non-agricultural establishment with more than four workers at the time of the census. We exclude both types of places in the baseline analysis.

We also remove all observations from Dehradun, the capital city of Uttarakhand. The reasons are twofold. First, as a country with a strong federalism tradition, state capitals typically have more autonomy and are much more resourceful than other cities in India. The conditions for the boundary discontinuity design—similar local characteristics across the border, are less likely to be satisfied for a state capital. Second, the sectoral coverage of central excise tax exemptions in Dehradun is governed by a separate official document that had been amended several times. The treatment status of sectors located in the city had not been stable.9 In a robustness analysis, we include the observations from Dehradun and the main results are not affected.

Finally, we end up with 8,505 geo-referenced towns and villages for the baseline analysis that are both within the 100-kilometer distance buffer from the Uttar Pradesh and Uttarakhand border and have data on establishments with more than four workers from the 2013 Economic Census. Figure 1 depicts their locations. Among these towns and villages, 5,128 places have information from the 1998 Economic Census and 2,642 places are captured by both the 1990 and 1998 rounds of Economic Censuses, which we use to check pre-state split patterns and pre-trends.

Figure 1. Locations of towns and villages in the final sample

9 The document is the Doon Valley notification (S.O.102(E)).

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The final sample for our analysis includes both public and private firms, regardless of formality, and includes all workers, both hired and non-hired, in non-agricultural economic sectors for the 8,505 towns and villages. Figure 2 shows the employment distributions (panel a) and firm count distributions (panel b) at the town and village level. The share of mid-to-large sized locations in terms of employment and firms is larger in Uttarakhand compared to Uttar Pradesh. This suggests that at the state border, modern economic activities tend to cluster more in Uttarakhand than they do in Uttar Pradesh.

Figure 2. Distributions of employment and number of firms

a. Log (employment) b. Log (number of firms)

5.2 Tax incentives

The CBEC’s June 2003 notification contained a negative list of 21 products, such as rubber, paper and plastic, for which central excise tax exemptions were ineligible. Additionally, the prevailing central excise taxes of some sectors were zero across India. For all these industries, there were no effective central excise tax incentives. As such, there is considerable heterogeneity in central excise tax incentives but nearly uniformity in other incentives across sectors, with the exception of the short negative list for the corporate income tax incentives. The heterogeneous treatment status in terms of central excise taxes is summarized in Table 1.

Table 1. The heterogeneity in central excise tax incentives

Central Excise Rate:

In the CBEC negative list:

Treatment status in Uttarakhand

Interpretation

Zero Yes Untreated If excise rate was zero everywhere in India, there was no differential tax treatment across the border.

Zero No Untreated

Non-zero Yes Untreated If a sector was in the negative list, excise exemptions did not apply and therefore there was no differential tax treatment across the border.

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Non-zero No Treated If a sector was not in the negative list, and the excise rate across India was non-zero, then a differential tax rate existed across 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.10 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).11

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:

𝐸𝐸𝐸𝐸𝑖𝑖𝑚𝑚 =∑ (𝐷𝐷𝑚𝑚𝑖𝑖 − 𝑥𝑥𝑚𝑚)�𝐷𝐷𝑚𝑚𝑚𝑚 − 𝑥𝑥𝑚𝑚�𝑀𝑀𝑚𝑚=1

1 − ∑ 𝑥𝑥𝑚𝑚2𝑀𝑀𝑚𝑚=1

where 𝐷𝐷 and 𝑗𝑗 denote sectors, and 𝑚𝑚 indicates geographic areas. 𝐷𝐷𝑚𝑚𝑖𝑖 represents the share of sector 𝐷𝐷′𝐷𝐷 employment in place 𝑚𝑚 and 𝑥𝑥𝑚𝑚 is the average employment share of the place across all sectors.

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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𝐸𝐸𝐸𝐸𝑖𝑖𝑚𝑚can be interpreted as the likelihood of industry 𝐷𝐷 and 𝑗𝑗 locate to generate spillovers for one another in place 𝑚𝑚. We construct this index at the district level, using the 2013 round of Economic Census and excluding establishments from Uttar Pradesh and Uttarakhand from the sample.

The second measure captures the strength of input-output linkage between industries. We calculate it between sectors 𝐷𝐷 and 𝑗𝑗 as:

𝐼𝐼𝐼𝐼𝑖𝑖𝑚𝑚 = arg max (𝑈𝑈𝐷𝐷𝑇𝑇𝑖𝑖𝑚𝑚

∑ 𝑈𝑈𝐷𝐷𝑇𝑇𝑘𝑘𝑚𝑚𝑘𝑘,𝑈𝑈𝐷𝐷𝑇𝑇𝑚𝑚𝑖𝑖

∑ 𝑈𝑈𝐷𝐷𝑇𝑇𝑚𝑚𝑘𝑘𝑘𝑘,𝑈𝑈𝐷𝐷𝑇𝑇𝑖𝑖𝑚𝑚

∑ 𝑈𝑈𝐷𝐷𝑇𝑇𝑖𝑖𝑘𝑘𝑘𝑘,𝑈𝑈𝐷𝐷𝑇𝑇𝑚𝑚𝑖𝑖

∑ 𝑈𝑈𝐷𝐷𝑇𝑇𝑘𝑘𝑖𝑖𝑘𝑘)

where 𝑈𝑈𝐷𝐷𝑇𝑇𝑖𝑖𝑚𝑚 denotes the value of sector 𝐷𝐷 used as inputs in the production of sector 𝑗𝑗 , and 𝑈𝑈𝑈𝑈𝑈𝑈𝑖𝑖𝑖𝑖

∑ 𝑈𝑈𝑈𝑈𝑈𝑈𝑘𝑘𝑖𝑖𝑘𝑘

denotes the ratio between inputs from sector 𝐷𝐷 and the sum of all inputs in the production of 𝑗𝑗. The overall index is the maximum value of the four pair-wise shares. The underlying data are India’s input-output 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 𝑆𝑆ℎ𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 as the fraction of sector 𝐷𝐷’s employment in occupation 𝑜𝑜. As the number of observations in the National Sample Survey data is limited, we calculate this variable at 2-digit NIC 2008 level. The similarity of worker types between industries 𝐷𝐷 and 𝑗𝑗 are proxied as the correlation between 𝑆𝑆ℎ𝑇𝑇𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 and 𝑆𝑆ℎ𝑇𝑇𝑇𝑇𝑇𝑇𝑚𝑚𝑖𝑖 across all occupations, denoted as 𝐿𝐿𝐿𝐿𝑖𝑖𝑚𝑚. The values of the three measures are summarized in Table 2.

Table 2. Summary statistics of the coagglomeration measures

Coagglomeration index Input-Output linkages Labor pooling linkages 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 sector pairs 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: 𝐼𝐼𝐼𝐼𝐷𝐷𝐸𝐸𝐼𝐼𝑚𝑚𝑇𝑇 = ∑ 𝐼𝐼𝐼𝐼𝐷𝐷𝐸𝐸𝐼𝐼𝑚𝑚𝑘𝑘 ∗ 𝐼𝐼{𝑘𝑘: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑡𝑡}𝑁𝑁𝑘𝑘=1 where 𝐼𝐼𝐼𝐼𝐷𝐷𝐸𝐸𝐼𝐼𝑚𝑚𝑘𝑘 denotes the coagglomeration index ( 𝐸𝐸𝐸𝐸 ), input-output linkages ( 𝐼𝐼𝐼𝐼 ) or labor pooling linkages ( 𝐿𝐿𝐿𝐿 )between industry 𝑗𝑗 and 𝑘𝑘, and 𝐼𝐼{𝑘𝑘: 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑡𝑡} is an indicator variable that is equal to 1 if sector 𝑘𝑘 is treated or receiving central excise tax incentives. Similarly, the indirect exposure of a sector to non-excise incentives as: 𝐼𝐼𝐼𝐼𝐷𝐷𝐸𝐸𝐼𝐼𝑚𝑚𝑁𝑁𝑇𝑇 = ∑ 𝐼𝐼𝐼𝐼𝐷𝐷𝐸𝐸𝐼𝐼𝑚𝑚𝑘𝑘 ∗ 𝐼𝐼{𝑘𝑘:𝐷𝐷𝑜𝑜𝐷𝐷𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑡𝑡}𝑁𝑁𝑘𝑘=1 . To make sure the results are comparable across these indices, we also normalize all 𝐼𝐼𝐼𝐼𝐷𝐷𝐸𝐸𝐼𝐼𝑚𝑚𝑇𝑇 and 𝐼𝐼𝐼𝐼𝐷𝐷𝐸𝐸𝐼𝐼𝑚𝑚𝑁𝑁𝑇𝑇 to have a zero mean and a unit standard deviation.

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6. Empirical findings

6.1 Average effects

We first address the question on the average local impact of the New Industrial Policy. Essentially, was there an abrupt increase in modern economic activities in Uttarakhand relative to Uttar Pradesh at the border after a decade of policy implementation? Before presenting results from the regression analysis, it is useful to look at a picture, Figure 3.

Panel a. of Figure 3 illustrates the average employment of towns and villages by their distance to the border between Uttarakhand and Uttar Pradesh. For each state, we select towns and villages within 50-kilometer driving distance from the border. We further classify these places into groups by their distances to the border, and each group includes 100 places. We then plot the logarithm of the average employment for each group of towns and villages against their median distance. It is strikingly clear that when one crosses the border from Uttar Pradesh to Uttarakhand, there was a sharp increase in the employment level in 2013. Panel b. presents the average number of firms of towns and villages by their distance to the border. A similar pattern emerges.

Figure 3. Average town and village level employment in 2013, by distance to the border between Uttarakhand and Uttar Pradesh

a. Log (average employment) b. Log (average number of firms)

Note: Panel a. plots the logarithm of the average employment for each group of 100 towns and villages that are classified by their distances to the border. The black dots are for places in Uttar Pradesh and the blue dots are for places in Uttarakhand. The black line presents the estimated linear relationship for places in Uttar Pradesh and the blue line for places in Uttarakhand. Panel b. plots the logarithm of the average number of firms for each group of 100 towns and villages that are classified by their distances to the border. The black dots are for places in Uttar Pradesh and the blue dots are for places in Uttarakhand. The black line presents the estimated linear relationship for places in Uttar Pradesh and the blue line for places in Uttarakhand.

Table 3 reports the results from Equ.(1) on the average effects of the New Industrial Policy at the town and village level. The top panel presents results on total employment and the bottom panel on total number of firms. The results of the seven different distance buffers are presented in the corresponding

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columns. All standard errors are robust to heteroskedasticity.12 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 (2013, town and village level)

Log (employment)

(1) (2) (3) (4) (5) (6) (7) 40 kms 50 kms 60 kms 70 kms 80 kms 90 kms 100 kms Target 0.8200*** 0.8755*** 0.9759*** 1.0315*** 1.0612*** 1.0709*** 1.0771*** (7.44) (8.95) (10.91) (12.66) (14.18) (15.16) (16.00) Distance -0.0021 -0.0021 -0.0011 -0.0004 -0.0000 0.0004 0.0012* (-0.95) (-1.29) (-0.86) (-0.43) (-0.06) (0.66) (2.14) Distance X Target -0.0018 -0.0063 -0.0125*** -0.0155*** -0.0170*** -0.0172*** -0.0169*** (-0.35) (-1.65) (-4.16) (-6.81) (-10.06) (-12.75) (-15.20) Constant 2.9018*** 2.9021*** 2.8781*** 2.8597*** 2.8491*** 2.8334*** 2.8065*** (47.74) (54.79) (61.09) (68.24) (75.35) (80.77) (86.18) No. of observations 3652 4521 5228 5988 6859 7644 8505

Log (total firms)

(1) (2) (3) (4) (5) (6) (7) 40 kms 50 kms 60 kms 70 kms 80 kms 90 kms 100 kms Target 0.3831*** 0.4317*** 0.5127*** 0.5474*** 0.5800*** 0.5805*** 0.5867*** (4.56) (5.78) (7.49) (8.80) (10.19) (10.80) (11.45) Distance -0.0007 -0.0011 -0.0003 0.0001 0.0004 0.0005 0.0011* (-0.38) (-0.80) (-0.29) (0.10) (0.68) (0.92) (2.38) Distance X Target -0.0009 -0.0049 -0.0100*** -0.0119*** -0.0135*** -0.0135*** -0.0134*** (-0.22) (-1.65) (-4.20) (-6.62) (-10.37) (-13.13) (-15.91) Constant 0.8824*** 0.8893*** 0.8715*** 0.8606*** 0.8507*** 0.8486*** 0.8282*** (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. * p < 0.05, ** p < 0.01, *** 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.

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Table 4. Average effects, 4 to 10-kilometer buffers (2013, town and village level)

Log (employment)

(1) (2) (3) (4) (5) (6) (7) 4 kms 5 kms 6 kms 7 kms 8 kms 9 kms 10 kms Target 0.6599** 0.7761*** 0.7356*** 0.6992*** 0.7829*** 0.7772*** 0.8206*** (3.26) (4.66) (4.71) (5.27) (6.33) (6.80) (7.58) No. of observations

195 271 350 449 530 597 678

Log (total firms)

(1) (2) (3) (4) (5) (6) (7) 4 kms 5 kms 6 kms 7 kms 8 kms 9 kms 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 observations

195 271 350 449 530 597 678

Note: All regressions include a constant. All standard errors are heteroskedastic robust. t statistics in parentheses. * p < 0.05, ** p < 0.01, *** p < 0.001.

Table 5. Placebo test using a pseudo border in Uttar Pradesh (2013, town and village level)

Log (employment)

(1) (2) (3) (4) 20 kms 30 kms 40 kms 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 (total 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. * p < 0.05, ** p < 0.01, *** p < 0.001.

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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 geo-reference 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.

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Table 6. Employment patterns prior to the state split (1998, town and village level)

Log (employment)

(1) (2) (3) (4) (5) (6) (7) 40 kms 50 kms 60 kms 70 kms 80 kms 90 kms 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 firms)

(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. * p < 0.05, ** p < 0.01, *** p < 0.001.

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

ΔLog(employment)

(1) (2) (3) (4) (5) (6) (7) 40 kms 50 kms 60 kms 70 kms 80 kms 90 kms 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

ΔLog (total firms)

(1) (2) (3) (4) (5) (6) (7) 40 kms 50 kms 60 kms 70 kms 80 kms 90 kms 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. * p < 0.05, ** p < 0.01, *** p < 0.001.

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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.

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Table 8. Average within-sector effects of the New Industrial Policy (2013, town-sector level)

Log (employment)

(1) (2) (3) (4) (5) (6) (7) 40 kms 50 kms 60 kms 70 kms 80 kms 90 kms 100 kms Target 0.2683*** 0.2354*** 0.3031*** 0.2829*** 0.2895*** 0.3206*** 0.3470*** (6.34) (6.30) (8.59) (8.88) (9.78) (11.52) (13.10) Distance 0.0018 -0.0017* 0.0011 -0.0004 -0.0008* -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* -0.0035*** -0.0047*** (-0.22) (0.75) (-1.57) (-1.49) (-2.56) (-5.34) (-8.45) Constant 2.4447*** 2.5052*** 2.4375*** 2.4704*** 2.4815*** 2.4702*** 2.4461*** (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 firms)

(1) (2) (3) (4) (5) (6) (7) 40 kms 50 kms 60 kms 70 kms 80 kms 90 kms