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