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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