# **V. Plant Panel Analysis**

Using plant panel data, this section explores two additional elements of the adjustment process to greater connectivity. _First_ , we ask how much of the observed response is due to adjustments on the extensive (exit) versus the intensive margin (changes within firms). _Second_ , we explore how the capabilities of incumbent plants, this time measured at the plant level, may affect their process of adjustment. Our baseline specification takes the form of difference-in-difference estimation where the _plant-level_ outcome variable in district _d_ at time _t,_ is estimated as: = ∑ ∗G pp,dd,tt D D D D ∗G D s D D + Γ + Ω + ε (3) YY pp,dd,tt is the outcome measure for plant dd∈DD dd _p_ ii,dd in district _d_ in year _t_ , Γ captures the vector of time-invariant plant dd,tt pp tt pp,dd,tt YY ββ bbD dd DD DDccssc DDdd specific traits (e.g. location, industry, ownership and other attributes of the plant) and pp,dd,tt pp Ω captures yearYY specific factors as well as secular trends. The estimate of 1 is the focal parameter, which captures the plant-tt level difference-in-differences effect of the nearest GQ highway segment opening among those plants ββ located in non-nodal districts within 10km of the GQ network, relative to plants more than 50km away (the reference group).<sup>16</sup> Preliminary analysis presented in Table 4 reveals that the sample focusing more on smaller plants (sample scheme) yielded the most provocative results, while those for the larger sample (census scheme) are presented in Annex Table A.6. We summarize the latter, when germane. Because the Annual Survey of Industries is structured as a rotating panel for the majority of the plant size distribution, we limit the sample to the set of plants observed in the data in the five years prior to their nearest GQ segment being completed, thereby halving the number of observations. For changes at the intensive margin, our sample contains two observations per plant, corresponding to records observed just before and just after the opening of the nearest GQ segment. While the resulting subsamples do not permit a direct mapping to the district-level findings, the results are remarkably consistent.

## _i. Extensive margin_

Exit, defined as the failure to observe the same plant five years after the completion of the nearest GQ segment, falls significantly with proximity, consistent with the rising district plant count in figure 1. This is true across firm age, but is particularly so among younger firms where exit falls 14% (table 1, column 1) compared to roughly 10% for mature and older firms. There is no significant effect in the larger firm sample (Annex Table A.6) for any age cohort, and the point estimates are a third to a half smaller, suggesting that

> 16 All variables with nominal values are deflated to 2005 prices, and districts are concorded to be consistently defined across the timespan analyzed (2000 to 2009). We further inspect the plant panel data for outliers and make a small number of values missing that imply implausibly large within-plant year-to-year changes. We calculate TFP-R under the assumption of constant-returns-toscale Cobb-Douglas production with structural parameters set as the median input cost share by three-digit industry.

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