# 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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