metadata: authors: 0: author: Grover,Arti Goswami
1: author: Maloney,William F.
2: author: O'Connell,Stephen A.
count: India,World
docna: 0: docna: Which Firms Drive the Gains from
            Connectivity and Competition ? The Impact of India’s Golden
            Quadrilateral across the Firm Life Cycle
docty: Policy Research Working Paper
theme: Migration, Remittances and Diaspora Engagement,Investment and Business Climate,Economic Policy,Economic Growth and Planning,Private Sector Development,Business Enabling Environment,Spatial Growth
abstracts: cdata!: This paper uses the construction of
            India's Golden Quadrilateral (GQ) highway to explore
            the impact of an exogenous increase in market access and
            competition across the firm life cycle and generates four
            findings. First, while exit rates fall for all plants,
            aggregate gains are driven by expansion of young plants.
            Older plants stagnate or contract, consistent with the
            challenges of increased competition for incumbents. Second,
            the benefits of connectivity to young plants depend on
            access to complementary factors, such as finance, and
            business conditions, although older plants respond better in
            more distorted districts, perhaps reflecting access to
            inputs while protecting output markets as in de Loecker et
            al. (2016).  Third, expanding young plants correspond to
            capital intensive value chain embedded activities that do
            not require close coordination with final producers. Fourth,
            plant-level panel data confirms plant capabilities as
            central to both the magnitude of the response, and to the
            composition of plants driving it. Aggregate expansion among
            young plants is driven by high skill plants while
            contraction of old plants is driven by low skill plants,
            consistent with frontier firms being able to escape
            competition (Aghion et al. 2014).
display_title: Which Firms Drive the Gains from
            Connectivity and Competition ? The Impact of India’s Golden
            Quadrilateral across the Firm Life Cycle
listing_relative_url: /research/2024/02/34266383/firms-drive-gains-connectivity-competition-impact-india’s-golden-quadrilateral-across-firm-life-cycle
topicv3: Infrastructure and Growth m1326244 1083,Resource Misallocations and Policy
            Failures m1327150 1069,Roads and Highways m1327398 168,Trade and Firms Entry and Exit m1327644 1193,Firm Dynamics and Productivity Growth
            m1504195 2864
publishtoextweb_dt: 2024-02-26T00:00:00Z
dois: 10.1596/41123
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 41
author_keys: 0: author_key: 1066720
1: author_key: 93000
2: author_key: 141960
model_extractions: Policy Research Working Paper 10710 Which Firms Drive the Gains from Connectivity and Competition?

The Impact of India’s Golden Quadrilateral across the Firm Life Cycle _Arti Grover William Maloney Stephen D. O’Connell_ International Finance Corporation& Latin America and the Caribbean Region February 2024
Policy Research Working Paper 10710

# **Abstract**

This paper uses the construction of India’s Golden Quadrilateral (GQ) highway to explore the impact of an exogenous increase in market access and competition across the firm life cycle and generates four findings. First, while exit rates fall for all plants, aggregate gains are driven by expansion of young plants. Older plants stagnate or contract, consistent with the challenges of increased competition for incumbents. Second, the benefits of connectivity to young plants depend on access to complementary factors, such as finance, and business conditions, although older plants respond better in more distorted districts, perhaps reflecting access to inputs while protecting output markets as in de Loecker et al. (2016). Third, expanding young plants correspond to capital intensive value chain embedded activities that do not require close coordination with final producers. Fourth, plant-level panel data confirms plant capabilities as central to both the magnitude of the response, and to the composition of plants driving it. Aggregate expansion among young plants is driven by high skill plants while contraction of old plants is driven by low skill plants, consistent with frontier firms being able to escape competition (Aghion et al. 2014).

This paper is a product of the International Finance Corporation and the Office of the Chief Economist, Latin America and the Caribbean Region. It is part of a larger effort by the World Bank Group 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 agrover1@worldbank.org and wmaloney@worldbank.org.

_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._ Produced by the Research Support Team
# **Which Firms Drive the Gains from Connectivity and Competition? The Impact of India’s Golden Quadrilateral across the Firm Life Cycle**

Arti Grover,<sup>*</sup> William Maloney,<sup>†</sup> and Stephen D. O’Connell<sup>‡</sup>

**Keywords:** firm dynamics, connectivity, competition, productivity, infrastructure, highways, India, manufacturing.

**JEL classification:** D22, D24, O12, O14, O18, R12

**Acknowledgements:** We thank the World Bank’s Research Support Budget for funding this paper. We are grateful to Leslie A. Martin, Shanthi Nataraj and Ann Harrison for sharing their code on matching ASI cross-sectional data with the ASI establishment panel data. We are indebted to Denis Medvedev and William Kerr for their support and insightful comments on the research proposal. We also thank two anonymous reviewers for their suggestions that helped improve the planned analysis at the concept stage.

> * Corresponding author. International Finance Corporation, World Bank. Email: agrover1@worldbank.org

> † International Bank for Reconstruction and Development, World Bank. Email: wmaloney@worldbank.org

> ‡ Emory University. soconnell.work@gmail.com
# **I. Motivation and Context**

The enhanced market access and increased competition facilitated by greater connectivity are critical drivers of firm dynamics and overall growth, although with theoretically ambiguous effects. On the one hand, increased connectivity through US railway expansion increased productivity and growth through market access and reallocation channels (Hornbeck and Rotenberg, 2019, 2021) and high costs of shipping to distant markets may drive size differences of plants between the United States and India (Hsieh and Klenow, <mark>2012, 2014).</mark> On the other hand, increased competition may cause local firms and industries to contract (Banerjee et al., 2012; Fujita and Krugman, 1999; Chandra and Thompson, 2000).

Ghani et al (2016) exploit a natural experiment offered by the construction of India’s Golden Quadrilateral (GQ) highway connecting the four major cities in India – Delhi, Mumbai, Chennai, and Kolkata – to document that the net effect was to expand manufacturing along the corridor. However, there is little literature on the underlying adjustment dynamics of such interventions – which types of firms generate the observed aggregate responses. Hence, this paper lifts the hood on the previous GQ findings: by creating synthetic cohorts from the rich location-coded plant-level data set, as well as constructing a true plant panel, it details <mark>how the reaction of plants to greater connectivity varies across their life cycle</mark> – <mark>young, mature and old-plant, local initial conditions, and time.</mark><sup>4</sup> <mark>Conceptually, the effects of connectivity could differ radically across these dimensions</mark> – <mark>older firms unaccustomed to competition may contract, while firms born within a more competitive context may be prepared to take advantage of new markets. Alternatively, older firms may have established access to necessary complementary factors – finance, land, or even local product markets that, in line with de Loecker et al. (2016) findings of increased markups with trade liberalization in India, may permit them to benefit from cheaper inputs, while defending their markets, thereby even strengthening their incumbent position. Cutting across all groups is the literature pioneered by A</mark> ghion et al. (2005, 2009, 2014; Cusolito, et al. 2022; Bloom et al., 2022) <mark>that stresses how the proximity to the technological frontier determines the degree to which firms may innovate to escape new competition, versus stagnating or exiting, as well as the emerging literature suggesting that firm capabilities, such as managerial or technological practices</mark>

> 4 The firm dynamics literature suggests that in the <mark>United States competition drives an “up or out” dynamic where most startups either grow or are pushed out of the market, contributing to healthy reallocation of resources to more productive firms, while the incumbents are forced to improve their capabilities (e.g., by upgrading management practices, Bloom et al. (2019), or investing in R&D, Aghion et al. 2005; 2014). A small fraction of fast-growing surviving startups contributes disproportionately to aggregate job growth (Haltiwanger et al., 2013). By comparison, developing countries such as Colombia (Eslava and Haltiwanger, 2019) and India (A</mark> kcigit et al. 2016) <mark>as well as some OECD countries (Adalet McGowan, et al. 2017) exhibit “neither up nor out” dynamics, new firms are stymied in entry and growth, and aggregate productivity and job growth are dampened</mark> (Tybout 2000; Li and Rama <mark>2015).</mark>

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<mark>raise firm performance and resilience to shocks (Bloom et al, 2007). Neither theory nor empirics offer a consensus on how long the sorting out process should take and for the reforms to come to fruition.</mark> _First_ , young plants benefit from connectivity and their scaling up drives the positive aggregate economic effects identified by Ghani et al (2016a). Despite falling exit rates across all ages of plants with proximity to the GQ, there is an average insignificant or negative effect on mature and old plants. Examining how the coefficients on the impact of the GQ evolve over time suggests that adjustment to the GQ shock requires not years, but decades and we expect that a longer sample period would reveal an even stronger divergence in performance between younger and older plants.

_Second_ , for young plants, in line with standard micro theory, the responsiveness to connectivity is blunted where factor and product markets are incomplete and where the enabling business environment shows a higher degree of distortion. Critically, then, building roads is not sufficient to stimulate local industry – other elements of the business climate, such as financial institutions must be in place as well. With the exception of plant capabilities, older plants generally show little such sensitivity to initial conditions suggesting that they have established alternative ways of resolving missing markets, and their performance is perversely _better_ in districts with higher distortions, possibly suggesting that, as in de Loecker et al. (2016), distortions may allow them to defend their market share while they can take advantage of cheaper inputs.

_Third_ , the young plants that respond most to connectivity tend to be more capital intensive, durable goods producers, linked to value chains but in activities that do not require close monitoring by the final producer, as might be associated with a kind of internal “offshoring” along the GQ.

_Fourth_ , in line with the Aghion et al. and the Bloom et al. managerial quality literature, both (district-level) pseudo panel as well as true plant-level panel estimations suggest that more capable firms can escape competition and take advantage of new opportunities. Strikingly, older plants in high literacy districts show a quantitatively similar positive response to GQ as that of the young plants, while it is those in low literacy districts that drive the aggregate negative trend for the cohort. This finding is broadly supported by the true plant-level panel analysis: young plants above median human capital and higher capital intensity drive the aggregate growth response while those below show no response to GQ; and below median older plants drive the contraction of the cohort while above median plants show no decline. Firm capabilities appear to be central to both the magnitude of the response to increased markets and competition, and to the composition of plants driving it.

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# **II.** **<mark>Data</mark>**

Government records detail the timing of completion of specific segments of the 5,846 km (3,633 mi) of road connecting the major industrial, agricultural, and cultural centers of India. Work began in 2001 and 23%, 80%, 90%, 97% and 98% was completed by the end of 2002, 2004, 2005, 2007 and 2010 respectively. The distance of economic districts to the highway is calculated as the shortest straight-line distance using official highway maps and ArcMap GIS software. Measuring to district edge or centroid yields similar results.<sup>5</sup> The information on location and timing of GQ upgrades is combined with the plant-level Annual Survey of Industries (ASI), obtained from India’s Ministry of Statistics and Program Implementation, to create two new data sets that allow us to study adjustment dynamics in a more detailed fashion than was previously possible. The ASI is broadly a census of establishments with 100 workers or more, and a rotating sample of one third (or one-fifth after 2004-05) of all other formal plants, defined as those with more than 10 workers.<sup>6</sup> One third (or one-fifth after 2004-05) of the units in each strata of state and 4-digit industries are systematically covered in each annual survey subject to a minimum sample size of 6 units in each stratum. The design ensures that the universe of units is covered in five years.<sup>7</sup> Each wave, referenced by its terminal year, was sampled over a two-year period from 1999-2000 to 2008-2009 which spans the entire period of construction of the GQ allowing before and after comparisons. In any given wave there are between 20,000 and 30,000 manufacturing plants, covering all states and districts.

This allows the creation of a 283 district-level pseudo panel tabulating seven outcome measures and extensive covariates for 90% of the country’s plants, employment and output. The reduction from a total of 630 districts arises from either the limited district presence of organized manufacturing, or incomplete series across the 2000 and 2009 period. Since we follow synthetic cohorts, plants born after the GQ upgrades began in 2000 are dropped<sup>8</sup> and all economic outcome variables are winsorized at the bottom 1 percentile to limit outliers and unavailable values in plant size and labor productivity coming from zeros in establishments counts, employment or output levels. Nine districts are categorized as _nodal_ (Delhi, Mumbai, Chennai and Kolkata, and the several contiguous suburbs Gurgaon, Faridabad, Ghaziabad and

> 5 For more details on data preparation, see Ghani et al. (2016a).

> 6 The sampling frame for the ASI is based on the lists of registered factories or units maintained by the Chief Inspector of Factories (CIF) in each state.

> 7 See Annual Survey of Industries Manual (2008, p. 12-13). A supplementary frame is prepared each year for new units, while closed factories only affect the sampling weights calculated for the respondent units. At the end of the cycle, when the data on all the units in the frame become available, the frame is updated for new factories, closed factories, and the composition of census and sample schemes.

> 8 In this sense, our analysis of young plants is quite different from that presented in Ghani et al. (2016a) who also compare plants entering after the GQ upgrades.

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Noida for Delhi; Thane for Mumbai); 67 districts located within 0 – 10 kilometers away from the GQ are the “treated” ones; 32 districts are 10–50 kilometers away; and 175 “control” districts located over 50 kilometers away are assumed to be minimally affected by the GQ, but track broad movements in the economy. Long differences in outcomes resulting from nearby GQ completion can thus be compared. To study adjustment patterns across plant ages, the data are further divided into age cohorts: 0-5 years (young), 6-24 years (mature) and plants with 25 and above years of age (old). Our results are not sensitive to moderate changes in category definition.

The data also permit creating a new geo-coded plant-level panel from two available versions of the ASI data: (i) a repeated cross-section of plant-level data with district identifiers, but no plant identifiers (“common factory ID”) and (ii) with plant identifiers, but without district identifiers. Using characteristics of firms such as industry code, ownership, establishment year, months of operation, capital assets, employment, wages, income from services etc., the two data sets are combined following Martin et al. (2017) with both district and plant identifiers with a match rate greater than 98%. This allows measuring year-to-year plant-level changes in revenue-based total factor productivity (TFPR), total revenues, output, labor and skill demand, and plant exit. Annex Table A.1 tabulates the summary statistics, splitting the outcomes by age groups and distance bands for pre-GQ and post-GQ years.

# **III. Estimation**

## (i) **Long difference in difference estimation:**

As a first approach to the data, we compare district activity in 2000, the year prior to the start of the GQ upgrades, with district-level activity in 2009, the year when GQ was nearly and totally complete. Indexing districts with _i_ , the specification takes the form: where , is the change in relative economic outcome observed for a district _i_ during the period 2000-09 and captures seven outcome variables: three aggregate measures at the district level- the natural log of ii ΔYY establishment counts, employment, and output; two capturing average plant size measured as employment and output; and changes in average labor productivity, defined as output per employee and Total Factor Productivity (TFP), estimated using LP-Sivadasan methodology. The set _D_ contains the three distance bands from the GQ network: nodal district, 0–10 kilometers, and 10–50 kilometers. The coefficients, measure for each distance band the change in outcome, over the 2000–09 period relative to the dd ββ ii YY 4 4
corresponding change for the 50+ control category. The specification is estimated separately for each of the three synthetic age cohorts and their outcomes are compared with the corresponding plant cohort when the GQ was completed in 2009. For example, the outcomes of 0-5 year-old plant cohort in 2000 are compared with 9-14 year-old cohorts in 2009.

All estimations include as a control the initial level of ( 2000) in the district, although the results are little affected by its inclusion. The vector contains district-level controls: national highway access, state ii YY YY highway access, broad-gauge railroad access and district-level measures from the 2000 Census of log total ii XX population, age profile (measured as demographic dividend),<sup>9</sup> female-male sex ratio, population share in urban areas, population share in scheduled castes or tribes, literacy rates and an index of within-district infrastructure measuring local access to electricity, roads, telecom, and water/sanitation facilities (see Ghani et al., 2012 for details).

Panels A-C in Table 1 present the results from estimation of specification (1) across the three age groups and for each of the seven outcome variables, reported across columns in the three panels. Columns 1-3 report district-level aggregate changes in logged output, employment and plant count; columns 4 and 5 report changes in average plant size measured as employment or output; columns 6 and 7 report average plant performance measured as labor productivity and revenue TFP. These results are robust to splitting the residual category of 50 plus kilometer distance band into finer groupings (Annex Table A.2).

Panel A shows that the arrival of the GQ had a positive and significant impact for the young cohort on output, average plant size, measured by both employment and output levels (columns 4 and 5), and on median plant performance, measured as labor productivity and TFP (columns 6 and 7). By comparison, for mature and older cohorts we observe either no significant positive or adverse effects in Panels B and C.<sup>10</sup> The results suggest that the positive aggregate responses to the GQ observed by Ghani et al. (2016a, 2016b) were largely driven by the young cohort of plants.<sup>11</sup> Because of the imprecision of the estimates for the older plants, it is difficult to reject equivalence in the coefficients in most cases, with only three test rejecting

> 9 We construct the demographic dividend measure as the ratio of working age population to non-working age population using 2001 population census counts.

> 10 Effects in terms of employment for non-nodal districts within 0-10 kilometers of GQ are consistently positive and insignificant across all age classes. Thus, there is overall a minimal effect on labor demand among any age group, although these estimates are noisily measured. These results are in line with Sedláček and Sterk (2017) who estimate a general equilibrium firm dynamics model that suggests that aggregate conditions at birth, rather than post-entry choices, drive the majority of cohort-level employment variation.

> 11 Notice that the results on new establishments (or activity cropping along the GQ network) in Ghani et al. (2016) are distinct from our results on the average effects being driven by the young cohort of plants because: (i) these plants are not young by the time of completion of GQ; (ii) Unlike Ghani et al. they _do not_ represent new activity as our dataset drops all new plants that were born after the GQ upgrades began.

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at the 10% level, but the next section will present evidence that the differences are, indeed, important and likely significant over the longer term.

One exception to the null or negative results for mature and old cohort of plants is the large increase in plant count for mature and old plants. Since the synthetic cohort structure restricts our sample to the set of existing plants as of 2000, this necessarily implies a reduced exit rate among these firms. This is counterintuitive given that we might expect the increased product market competition precisely to challenge weak incumbents. We return to this puzzle in section IV using plant-level data where exit decisions can be studied more directly.

Annex Tables undertake three tests of the possibly confounding effects of firm geographical selection and endogenous highway placement by exploiting new segments vs upgrades (A.3a), the unfinished NW-EW highway as a placebo (A.3b), and straight-line highway layouts as an instrument (A.3c) and confirms the findings of the previous literature that neither is importantly biasing the results.

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Table 1: Long-differenced estimations of the impact of GQ improvements, comparing 2009 to 2000

|DV-Change inloggedvaluesof<br>manufacturing trait listed in column<br>|Output <br>|Employment<br>|Plant<br>count<br>|Avg.plant<br>size(emp.)<br>|Avg.plant<br>size(output)<br>—<br>|Laber7<br>_productivity<br>|TFPR<br>|
|---|---|---|---|---|---|---|---|
|header:|(1)|(2)|(3)|(4)|(5)|(6)|(7)|
||||A.|Young plants|(0-5 years)|||
|(0,1) Nodal district|1.037|0.917|0.711|0.489|0.522|-0.049|0.2844++|
||(1.011)|(0.893)|(0.643)|(0.481)|(0.498)|(0.247)|(0.129)|
|(0,1) District 0-10kmfromGQ|0.701++|0.270|0.088|0.311+|0.643++|0.409++|0.239++|
||(0.290)|(0.233)|(0.202)|(0.185)|(0.272)|(0.179)|(0.120)|
|(0,1) District 10-50 km fromGQ|0.062|-0.093|-0.046|-0.203|-0.040|0.226|0.121|
||(0.469)|(0.324)|(0.230)|(0.266)|(0.397)|(0.276)|(0.120)|
||||B. M|ature plants|(6-24 years)|||
|(0,1) Nodal district|1.420|1.235+|0.813|0.467|0.623|-0.078|0.105|
||(0.945)|(0.681)|(0.531)|(0.389)|(0.510)|(0.261)|(0.113)|
|(0,1) District 0-10km fromGQ|0.358|0.218|0.416++|-0.139|0.027|0.032|-0.014|
||(0.333)|(0.225)|(0.176)|(0.172)|(0.253)|(0.150)|(0.088)|
|(0,1) District 10-50km fromGQ|-0.055|-0.108|-0.173|-0.133|0.070|0.143|-0.007|
||(0.418)|(0.257)|(0.214)|(0.243)|(0.382)|(0.228)|(0.122)|
||||C. Old|plants (25 ye|ars and above)|||
|(0,1) Nodal district|1.142|1.054|1.198+|-0.036|0.064|0.036|0.278++|
||(0.809)|(0.787)|(0.651)|(0.364)|(0.359)|(0.166)|(0.126)|
|(0,1)District0-10kmfromGQ|0.335|0.197|0.342++|-0.013|0.012|0.157|0.048|
||(0.273)|(0.203)|(0.172)|(0.161)|(0.207)|(0.144)|(0.082)|
|(0,1)District10-50kmfromGQ|-0.540|-0.553+|-0.239|-0.507++|-0.374|0.150|-0.032|
||(0.415)|(0.288)|(0.260)|(0.233)|(0.314)|(0.192)|(0.099)|

Notes: Long-differenced estimations consider changes in the location and productivity of organized-sector manufacturing activity in 283 Indian districts from 2000 to 2009 from the Annual Survey of Industries (ASI). Explanatory variables are indicators for distance from the GQ network that was upgraded starting in 2001. Estimations consider the effects relative to districts more than 50 km from the GQ network. Column headers list dependent variables. Young plants are those less than four years old. Labor productivity is total output per employee in district, and TFP is weighted average of Sivadasan (2009) approach to Olley-Pakes estimations of establishment-level productivity with repeated cross-section data. Outcome variables are winsorized at their 1% and 99% levels, and entry variables are coded at the 1% level where no entry is observed to maintain a consistent sample. Estimations report standard errors, have 283 observations, control for the level of district activity in 2000, and weight observations by log total district population in 2001. Initial district conditions include variables for national highway access, state highway access, and broad-gauge railroad access and district-level measures from 2000 Census of log total population, age profile, female-male sex ratio, population share in urban areas, population share in scheduled castes or tribes, literacy rates, and an index of within-district infrastructure. Estimations include state fixed effects as well.

Vie _= LicaVYrc=6 3 Bee * (0,1) years. to GQ completion,H * (0,1)GQDist;; gesohm + 9; +O + Ese
Yi is as before, t is the estimated coefficient for the group of non-nodal districts located within 10 kilometers of GQ network with time to completion of GQ upgrades ranging from 3 years prior to six years ββ or more after the completion of GQ segment. For example, if the outcome is observed in 2006, while the GQ upgrade for the district was completed in 2003, the regression measures the impact three years after the upgrade; in 2000, the coefficient measures the effect three years prior to the upgrade. ∅ is a vector of district and state fixed effects and is a vector of year fixed effects. Standard errors are clusteredii by district and we weight by district log population in 2001. The sample comprises 1,188 observations. For tt θθ parsimony, we drop the lagged dependent variable which the above exercises suggest has little effect on the coefficients, and we use the 10-50 category as reference since it behaves similarly to the 50+, dropping the latter from the sample.

Figure 1 plots t from equation (4) and reveals three striking patterns of evolution that cast additional light on the results above. We report only the young and old plants and confidence intervals for readability and ββ because the mature follow an intermediate path between the two extremes.

_First_ , as in Table 1, only the series of coefficient for the young is strongly statistically different from zero for output, plant size (output), labor productivity and TFPR. However, as the number of years after connectivity increases, the impact heightens dramatically positively for the young and usually negatively for the old. The exception is the number of plants, which, again, seems to suggest that there is progressively less exit for all cohorts. In all cases for the young, even if the coefficient may not become significantly different from zero, it does drift out of its previous confidence interval suggesting that the positive trend is, indeed, significant and the coefficient may become so.

_Second_ , this implies that, though formal tests of coefficient equivalence across age cohorts on only three occasions at the 10% level in table 2, the coefficients drift out of the own, but also the other age group coefficient’s confidence interval, strongly suggesting that eventually tests will reject equivalence over longer duration. The results for the cumulative systemic effect are broadly similar. It appears that, with time, the predominant role of young plants would become only more pronounced.

_Third_ , this evolution shows few signs of convergence to a new value suggesting that the transition to the new steady state is a multi-decade process of which this analysis is capturing only the initial phase.

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infrastructure, literacy, distance to nodal cities, access to finance, population, and then several measures of distortions, but again, disaggregated by age.<sup>12</sup> Panel B shows that for local infrastructure, as with the overall results, only the young cohort of plants shows significant results, and these are mixed. Overall district output, and firm size measured by employment are higher for young plants with better local infrastructure, but size measured by output, TFP and labor productivity are actually higher in places with worse, a result shared with older firms. The possibility that poor local infrastructure and other distortions may serve to protect local markets, while access to cheaper inputs may lead to higher markups and hence higher measured productivity is discussed below.

The most striking result from the disaggregation by district-level education (panel C) is that in high literacy areas, mature firms now show significant responses for output and plant size, and older firms also have a significant response in output comparable to that of young firms as well as rising TFP. That is, the null or declining impact of proximity to the highway network on older firms is being driven by those plants in low literacy areas.

This proxy might literally be measuring the availability of skilled workers. Alternatively, if literacy is proxying for capabilities/managerial practices, plants closer to the technological frontier are more likely to innovate out of increased competition while laggards will be less able to compete and will decline a la Aghion et al. Somewhat puzzling is that young plants appear to do slightly better across all indicators for _lower_ skill districts although as the true plant-level panel estimates below will show, this does not imply better performance by lower capability firms – the reverse is true. It may be that entering firms have better managerial capital but seek low skilled labor. This is the only cut of the data that reveals statistically significant responses for mature and older firms (although with lower point estimates) and hence we focus on the young plants going forward.

Several variables suggest the positive effect of enabling environment on the response of young firms to connectivity. Those plants in higher TFP districts (panel G), do generally better with the exception of increases in labor productivity which may be capturing some productivity mean reversion. Response is higher for districts close to nodal cities across all performance measures except employment, perhaps reflecting relatively lower transport and information costs, or access to other inputs (Panel D). Access to bank finance increases performance response across the board (Panel E). Plants in higher population density

> 12 We prefer this simpler approach of interacting separately for each district trait because the data variation becomes very thin when partitioning the sample by additional traits beyond proximity to the GQ network.

11
districts (Panel F) show higher response of output and both size measures and may again proxy for the services and factor markets (including finance) that are generally associated with the benefits of agglomeration.

The fact that older plants show no difference in response to connectivity in low finance areas may point to more established relationships with existing financial institutions, or the ability to self-finance. In fact, more established relations of all types may also help preserve market power while increasing access to cheap inputs as in de Loecker et al (2016). Such factors may explain some of the patterns observed, including the slow decline of incumbent firms. Annex Table A.4 approaches this question, splitting the sample by measures of misallocation in value added, inputs, employment, capital and land, following Duranton et al. (2015a, 2015b), which proxy for distortions that might reflect incumbent power. Though results vary by measure, for value added, inputs and capital, young plants show high and significant responsiveness in less distorted districts for outcomes pertaining to output and the two measures of firm size, but insignificant response to proximity in more distorted districts, consistent with the findings around enabling environment above. This suggests that where young productive firms have access to necessary complementary inputs (a free market in land, for example) or are not facing resistance by incumbents with market power, they are more able to take advantage of the increased connectivity. Strikingly, for old plants, the response of labor productivity, as well as TFP in some cases, is much higher in _more_ distorted areas across all measures, again consistent with de Loecker et al (2016). Some of this effect is visible for young plants but it is far more muted.

12
Table 2: Long-differenced estimations of the impact of GQ improvements, comparing 2009 to 2000, interacted with initial conditions

||||Young|||||Mature|||||Old|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|DV-Loggedvaluesof|Output|Avg.<br> plantsize <br>(emp.)|Avg.<br> plantsize <br>(output)|veduceit<br>P|y TFPR<br>.|Output|Avg.<br> plantsize <br>(emp.)|Avg.<br> plantsize<br>(output)|voduetity<br>P<br>.|TFPR|Output|Avg.<br> plantsize <br>(emp.)|Avg.<br> plantsize <br>(output)|vodacenit<br>p|y TFPR<br>.|
|||A. Base|<br>OLS estimat|<br>ionthat excl|udes nodal dist|ricts andmeas|ures effects|relativeto di|stricts 10-50|kmfromthe|GQ network|||||
|(0.1) District 0-10kmfromGQ|0.7017+|=<br>=0.311+|= -0.643++|0.4097+|= 0.239++|0.358|-0.139|0.027|0.032|-0.014|0.335|-0.013|0.012|0.157|0.048|
||(0.290)|(0.185)|(0.272)|(0.179)|(0.120)|(0.333)|(0.172)|(0.253)|(0.150)|(0.088)|(0.273)|(0.161)|(0.207)|(0.144)|(0.082)|
|||||B. Panel|Awithinterac|tion splitusin|gmedian ofd|istrictinfrast|ructure index|||||||
|(0,1) District0-10kmfromGQ|0.746++|=0.402+|0.554+|0.208|0.090|0.264|-0.047|0.012|-0.108|0.083|0.215|-0.045|-0.147|-0.005|0.094|
|<br>Abovemedian|(0.342)|<br>(0.208)|(0.307)|(0.178)|(0.132)|(0.325)|(0.176)|(0.261)|(0.150)|(0.082)|(0.309)|(0.182)|(0.228)|(0.146)|(0.086)|
|(0.1) District0-10kmfromGQ|0.673+|0.228|0.774++|0.65274+|90.422+7++|0.394|-0.249|0.025|0.188|-0.116|0.444|-0.023|0.162|0.337+|0.004|
|<br>Medianvalue and below|(0.373)|(0.264)|(0.348)|(0.235)|<br>(0.154)|(0.471)|(0.244)|(0.339)|(0.214)|(0.129)|(0.345)|(0.211)|(0.267)|(0.190)|(0.109)|
|||||C.|PanelAwith|interaction spli|tusingmedi|an ofdistrictl|iteracy|||||||
|(0.1) District0-10kmfromGQ|0.749++|0.332|0.602++|0.341+|0.051|0.818++|0.251|0.491+|0.095|-0.033|0.687++|0.133|0.262|0.208|0.156+|
|<br>Abovemedian|(0.363)|(0.215)|(0.301)|(0.175)|(0.120)|(0.370)|(0.193)|(0.272)|(0.138)|(0.093)|(0.295)|(0.176)|(0.195)|(0.135)|(0.085)|
|(0,1)District0-10kmfromGQ|0.919+|=0.403+=|0.872+++|=0.5144|=0.463+4+|0.256|-0.377|-0.184|0.043|-0.054|0.403|-0.174|-0.068|0.221|-0.021|
|<br>Medianvalueand below|<br>(0.334)|<br>(0.228)|<br>(0.312)|<br>(0.213)|<br>(0.142)|(0.444)|(0.232)|(0.332)|(0.193)|(0.120)|(0.358)|(0.209)|(0.280)|(0.185)|(0.108)|
|||||D. PanelA|with interactio|n splitusingm|edian ofdist|rict distance|fromnodal ci|ties||||||
|(0.1) District0-10kmfromGQ|0.781+|0.360|0.775++|0.543++|=0.444+>|0.113|-0.351|-0.107|0.032|0.044|0.291|0.063|0.096|0.235|-0.014|
|<br>Abovemedian|(0.405)|(0.262)|(0.352)|(0.241)|<br>(0.180)|(0.436)|(0.242)|(0.360)|(0.226)|(0.154)|(0.284)|(0.203)|(0.273)|(0.189)|(0.117)|
|(0,1) District 0-10kmfromGQ|0.661-+|0.288|0.582++|0.344+|= 0.146|0.473|-0.044|0.087|0.032|-0.037|0.354|-0.047|-0.027|0.123|0.074|
|Medianvalueandbelow|(0.312)|(0.201)|(0.294)|(0.183)|(0.124)|(0.379)|(0.187)|(0.266)|(0.155)|(0.093)|(0.320)|(0.185)|(0.228)|(0.148)|(0.088)|
|||E. Pan|elAwith in|teraction spli|tusingmedian|ofdistrict acc|ess tobanki|ng services (|% Household|s with access|to Bank)|||||
|(0,1)District0-10kmfromGQ|0.827++|0.436++|=0.741++|0.434++|=0.329+++|0.384|-0.018|0.084|-0.037|0.038|0.444|0.068|0.169|0.159|0.114|
|<br>Abovemedian|(0.340)|(0.218)|<br>(0.340)|(0.216)|<br>(0.118)|(0.315)|(0.206)|(0.260)|(0.155)|(0.097)|(0.311)|(0.181)|(0.222)|(0.151)|(0.098)|
|(0,1) District0-10kmfromGQ|0.596|0.208|0.561+|0.387+|0.160|0.337|-0.243|-0.022|0.099|-0.066|0.242|-0.081|-0.121|0.155|-0.012|
|<br>Medianvalue and below|(0.366)|(0.230)|(0.300)|(0.197)|(0.163)|(0.489)|(0.230)|(0.349)|(0.202)|(0.118)|(0.333)|(0.203)|(0.259)|(0.179)|(0.099)|
|||||F. Pane|lAwith intera|ction splitusin|gmedian of|district popula|tion density|||||||
|(0,1) District0-10kmfromGQ|0.713++|0.442++|= 0.774++|0.360+|0.254+|0.185|-0.217|-0.131|-0.043|-0.067|0.420|0.149|0.160|0.172|0.057|
|Abovemedian|(0.332)|(0.212)|(0.305)|(0.208)|(0.142)|(0.357)|(0.203)|(0.283)|(0.164)|(0.101)|(0.266)|(0.164)|(0.203)|(0.145)|(0.087)|
|(0,1) District0-10kmfromGQ|0.682+|0.112|0.439|0.488++|0.210|0.630|-0.020|0.272|0.143|0.079|0.200|-0.267|-0.221|0.132|0.029|
|<br>Medianvalue and below|(0.360)|(0.232)|(0.335)|(0.188)|(0.133)|(0.501)|(0.231)|(0.335)|(0.193)|(0.122)|(0.428)|(0.239)|(0.317)|(0.203)|(0.129)|
|||||G. P|anelA within|teraction split|usingmedia|n ofdistrictT|FP level|||||||
|(0,1) District0-10kmfromGQ|0.888+++|=0.481++<br>|=0.815+++|0.374+|=0.289+|0.499|-0.085|0.091|0.020|0.012|0.288|0.109|-0.045|0.004|-0.001|
|<br>Above median|(0.311)|(0.210)|<br>(0.310)|(0.205)|<br>(0.148)|(0.424)|(0.205)|(0.292)|(0.171)|(0.110)|(0.323)|(0.197)|(0.243)|(0.160)|(0.101)|
|(0,1) District0-10kmfromGQ|0.488|0.126|0.458|0.448++|0.184|0.200|-0.199|-0.044|0.046|-0.044|0.385|-0.144|0.073|0.330++|0.103|
|<br>Medianvalueand below|(0.382)|(0.233)|(0.323)|(0.202)|(0.137)|(0.370)|(0.208)|(0.304)|(0.177)|(0.098)|(0.310)|(0.187)|(0.236)|(0.158)|(0.088)|

Notes: See Table 1. Long-differenced estimations consider changes in the location and productivity of organized-sector manufacturing activity in 106 non- nodal districts located within 0-50 km of GQ for the time period starting from 2000 to 2009 from the Annual Survey of Industries (ASI). Panel (a) repeats the base estimation for this group. In panels (B)-{L). the base effect is interacted with indicator variables for above or below median values for indicated district traits. Estimation controls for unreported main effects of district traits. District level access to infrastructure, literacy, access to banking, population density is extracted from 2001 census, while distance from nodal cities to district is computed from district edge using India's district level GIS maps. Lastly, initial TFP is plant-level TFP for the year 2000-01 computed following LP-Sivadasan methodology on ASI data. misallocation matrices are computed using the methodology proposed in Duranton et al. (2015a).
# **_iv. Industry characteristics correlated with responsiveness to GQ_**

The ASI data permits studying the industry related characteristics of the plants most responsive to connectivity. To this end, we replicate equation 1 splitting the regression with sub-samples partitioned by median industry characteristics. Critically, each age group is well-represented across industry characteristics (Annex Table 5) so the previous results for young plants are not simply capturing newly arrived industries – the relevant characteristic is, in fact, youth.

This said, there are significant differences in the stimulative effect of connectivity across industry characteristics within age category. Table 3 shows that responsiveness is almost entirely concentrated in capital intensive industries for all measures of activity in young plants and for output in mature plants (Panel B), and to a lesser extent in land intensive industries (Panel C). There is less clear differentiation for human capital-intensive industries where responsiveness is higher only for output and plant size measured by employee count (panel D).

Consistent with the strong results for capital intensity, the table suggests that plants most responsive to connectivity are those producing durable goods (category including wood products; paper products; leather; textiles; rubber plastic products; motor vehicles, panel E). In particular, output is significantly more responsive for all age categories, while plant size is also significant for the young. Similar results are observed for sectors with strong backward and forward value chain linkages (panel F; e.g., textiles; wearing apparel; office computing; radio, television communication; motor vehicles); and for those that are the least amenable to remote work (panel G).

This said, responsive sectors are also low in contractual intensity (panel H), that is, the proportion of a sector’s intermediate inputs that require relationship-specific investments and differentiated products; and where relationship stickiness, as measured by firm to firm relationship durations, is low (panel I) particularly for output and firm size in the young plants.<sup>13</sup> Together, these patterns are suggestive of a more “anonymous” internal offshoring behavior, that is, young plants can become parts of value chains that do not require proximity to clients to tailor intermediate inputs and may be substituted by other providers relatively easily.

> 13 Based on Martin et al. (2023). This is a measure of stickiness in business relationships constructed at the product-level using firm-to-firm relationship duration data. The higher degree of stickiness translates into a lower probability of switching and longer firm-to-firm trade relationships, conditional on match quality.

14
Industries with above median reliance on formal trade finance (e.g., letters of export credit, banking transactions for trade insurance) show strong significance across all variables for the young and for output and productivity for mature, and all but labor productivity for older firms (Panel J). This is consistent with Fisman and Love (2003) who show that in countries with weak financial markets, access to such short-term credit instruments provides an alternative source of investable funds.<sup>14</sup> Similarly splitting the sample by access to Buyer-Supplier trade credit shows strongly significant results in industries with above median access on outcomes pertaining to output, plant size (workers) for young and mature (Panel K).

However, somewhat surprisingly, younger plants with a higher degree of dependence on _traditional_ finance, per the Chor and Manova (2012 ) measures, show more responsiveness for output, employment, and plant size (Panel L) than those less dependent. Das et al. (2023) may shed light on this paradox by noting the strong supply response of credit to supply the new firms’ demand along the GQ.<sup>15</sup> To square this finding with those of the previous section on the increased responsiveness in areas with a higher access to banks, Das et al. document that expansion along this extensive margin – the expansion of bank branches into areas where previously there was no service – took several years after the establishment of the GQ while increased flows through existing institutions ramped up quickly. Hence financially dependent plants moving into areas with established bank infrastructure may find accommodating financial flows, while plants in areas with low bank density find problems responding or rely on non-traditional financing mechanisms.

> 14 Sectors at the high end of formal trade finance dependence are textiles, stone and glass, wood products, vehicles, oils and fat, machinery, metals.

> 15 Here again, however, we document that this effect is entirely through young plants. Older plants may either have secured alternate sources long ago informally or rely on retained earnings.

15
Table 3: Long-differenced estimations of the impact of GQ improvements, comparing 2009 to 2000, interacted with initial conditions

||||Young|||||Mature|||||Old|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|DV-Loggedvaluesof|Output|Avg.<br>plantsize <br>(emp.)|Avg.<br> plantsize <br>(output)|eety<br> P<br>,|TFPR|Output|Avg.<br>plantsize <br>(emp.)|Avg.<br> plantsize <br>(output)|eety <br>P<br>,|TFPR|Output|Avg.<br>plantsize <br>(emp.)|Avg.<br> plantsize <br>(output)|oe ty<br> P<br>,|TFPR|
|||A. Base|OLS estim|ation that exc|ludes nodal di|stricts andme|asures effec|ts relative to|districts 10-50|km from the|GQ network|||||
|(0,1) District0-10km fromGQ|O.7017+<br>|0.3117<br>|0.6437><br>|0409+<br>|0.2997<br>|0.358<br>|-0.139<br>|0.027<br>|0.032<br>|-0.014<br>|0.335<br>|-0.013<br>|0.012<br>|0.157<br>|0.048<br>|
||(0.290)|(0.185)|(0.272)|== (0.179)|(0.120)|(0.333)|(0.172)|(0.253)|(0.150)|(0.088)|(0.273)|(0.161)|(0.207)|~— (0.144)|(0.082)|
||||B. P|anelA split r|egressions usi|ngmedian of|industry phys|ical capital i|ntensity (ASI d|ata, 2000)||||||
|(0,1)District0-10kmfromGQ=<br>|0.980+++<br>|=0.438+<br>|0.7627+<br>|0444-4<br>|0.38177<br>|0.5687<br>|0.217<br>|0.098<br>|-0.047<br>|0.102<br>|0.449<br>|0.031<br>|0.137<br>|0.276<br>|0.165<br>|
|<br>Abovemedian|<br>(0.373)|<br>(0.234)|= (0.315)|(0.209)|(0.122)|(0.342)|(0.221)|— (0.271)|(0.167)|(0.092)|(0.316)|(0.218)|(0.275)|= (0.169)|(0.113)|
|(0,1) District0-10km fromGQ<br>|0.307<br>|0.351<br>|0.249<br>|-0.051<br>|0.031<br>|0.281<br>|-0.065<br>|0.120<br>|0.101<br>|-0.004<br>|-0.129<br>|-0.133<br>|-0.270<br>|-0.238<br>|-0.062<br>|
|Medianvalue andbelow|(0.321)|(0.243)|(0.284)|(0.184)|(0.144)|(0.370)|(0.216)|— (0.326)|(0.157)|(0.107)|(0.399)|(0.216)|(0.298)|(0.182)|—-(0.105)|
|||||C. PanelA|split regressio|ns using media|n ofindustr|y land intensi|ty(ASI data, 2|000)||||||
|(0,1)District0-10kmfromGQ|0.97044|0.4444|0.71444|0.245|0.198|0.410|0.071|-0.042|-0.011|0.23144|O.788++|0.338|0.394|0.114|0.183|
|<br>Abovemedian|(0.425)|= (0.257)|(0.345)|(0.215)|(0.150)|(0.332)|= (0.210)|= (0.275)|(0.164)|(0.104)|(0.351)|= (0.229)|= (0.297)|(0.190)<br>|= (0.125)|
|(0.1) District0-10km fromGQ<br>|0.435<br>|0.332<br>|0.432<br>|0.112<br>|0.196<br>|0.455<br>|-0.029<br>|0.294<br>|0.2527<br>|-0.026<br>|-0.004<br>|~0.301<br>|-0.164<br>|0.026<br>|0.026<br>|
|Medianvalue andbelow|(0.369)|(0.222)|(0.315)|(0.164)|(0.120)|(0.328)|(0.195)|(0.269)|(0.145)|(0.100)|(0.315)|(0.185)|(0.216)|~— (0.165) _|—-(0.078)|
||||D. P|anelA split r|egressions us|ing median of|industryhum|an capital in|tensity(ASI da|ta, 2000)||||||
|(0.1)District0-10kmfromGQ<br>|0.9597<br>|0.4517<br>|0.461<br>|-0.094<br>|0.048<br>|0.471<br>|0.217<br>|0.007<br>|-0.257<br>|-0.121<br>|0.642<br>|0.243<br>|0.186<br>|0.072<br>|0.121<br>|
|<br>Abovemedian|(0.490)|(0.262)|(0.379)|(0.195)|—-(0.165)|(0.423)|(0.265)|—(0.358)|(0.201)|(0.119)|(0.528)|(0.268)|(0.344)|(0.180)|(0.119)|
|(0,1) District0-10km fromGQ|0.417|0.284|0.5007|0.279|0.158|0.342|-0.124|0.128|0.115|0.032|0.205|0.031|0.009|0.088|0.025|
|Median value andbelow|(0.302)|(0.206)|(0.294)|(0.197)|(0.134)|(0.312)|(0.178)|(0.253)|(0.157)|(0.106)|(0.264)|(0.178)|(0.217)|(0.155)|(0.097)|
|||||E. Pan|elA split regr|essions using|median ofi|ndustry durabl|ilty ofgoods|||||||
|(0,1) District0-10km fromGQ<br>|0.99377<br>|O.731t+<br>|0.8047<br>|0.022<br>|0.010<br>|0.9807+<br>|0.354<br>|0.431<br>|-0.0352<br>|0.081<br>|1.047+7<br>|0.415<br>|0.368<br>|0.069<br>|0.043<br>|
|Abovemedian|(0.475)|(0.277)|= (0.380)|~— (0.185)|(0.159)|(0.405)|(0.233)|(0.278)|(0.143)|(0.089)|(0477)|(0.264)|(0.304)|= (0.174)|(0.092)|
|(0,1)District0-10kmfromGQ|0.442|0.232|0.374|0.357+|0.36144|0.298|-0.174|0.083|0.149|-0.012|0.188|-0.266|0.024|0.286+|0.028|
|<br>Medianvalue andbelow|(0.362)|(0.211)|= (0.340)|(0.213)|(0.140)|(0.334)|= (0.192)|(0.286)|(0.175)|(0.117)|(0.283)|= (0.167)|(0.226)|(0.156)<br>|= (0.093)|
|||||F. P|anelA split re|gressions usin|g median of|industryGV|C intensity|||||||
|(0,1) District0-10km fromGQ|0.9504|= 0.5404+|0.5394|0.103|0.124|0.662+|0.280|0.026|-0.131|0.022|0.585|0.408+|0.134|-0.141|-0.005|
|Abovemedianindustry|(0.388)|= (0.264)|= (0.320)|(0.134)|(0.119)|(0.372)|= (0.220)|= (0.245)|(0.140)|(0.084)|(0.363)|= (0.226)|= (0.260)|(0.158)<br>|= (0.111)|
|(0.1) District 0-10km fromGQ<br>|0.129<br>|0.182<br>|0.368<br>|0.286<br>|0.155<br>|0.111<br>|-0.219<br>|0.087<br>|0.154<br>|0.026<br>|0.139<br>|-0.38877—<br>|-0.191<br>|0.154<br>|-0.025<br>|
|Medianvalueandbelow|(0.342)|(0.211)|(0319)|(0.224)|(0.151)|(0.301)|(0.185)|(0.276)|(0.171)|(0.125)|(0.295)|(0.182)|(0.246)|=(0.172)~|—-(0.102)|
Table 3: Long-differenced estimations of the impact of GQ improvements, comparing 2009 to 2000, interacted with initial conditions

||||Young<br>|||||Mature<br>|||||Old<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|DV-Loggedvaluesof|Output|Avg.<br> plantsize <br>(emp.)|Avg.<br> plantsize <br>(output)|ee ty <br> P<br>.|TFPR|Output|Avg.<br> plantsize <br>(emp.)|Avg.<br>plantsize<br>(output)|vedcenity <br>P<br>.|TFPR|Output|Avg.<br> plantsize <br>(emp.)|Avg.<br> plantsize <br>(output) P|vodwetnity<br><br>.|TFPR|
||1|2|3|4|5|6|T|8|9|10|12|13|14|15|16|
|||||G. PanelA|splitregressi|ons usingmedi|an ofindustr|y amenabilit|yto remotewo|rk||||||
|(0.1)District0-10kmfromGQ|0.543|0.059|0.347|0.051|0.139|0.157|-0.032|-0.068|0.001|0.221+|0.336|-0.029|0.069|-0.024|0.134|
|<br>Abovemedian|(0.491)|(0.281)|(0.396)|(0.301)|(0.152)|(0.392)|(0.235)|(0.355)|(0.196)|(0.119)|(0.369)|(0.240)|(0.316)|(0.208)|(0.123)|
|(0.1) District 0-10km fromGQ<br>|0.702++<br>|0.464++<br>|= 0.593+<br>|0.126<br>|0.064<br>|0.581+<br>|-0.048<br>|0.246<br>|0.162<br>|-0.015<br>|0.309<br>|0.023<br>|-0.058<br>|0.003<br>|0.018<br>|
|Medianvalue andbelow|(0.327)|(0.227)|(0.308)|(0.154)|(0.118)|(0.349)|(0.204)|(0.268)|(0.135)|(0.094)|(0.286)|(0.179)|(0.192)|(0.141)|(0.075)|
|||||H. Pane|l A<br>splitregre|ssions usingm|edian ofind|ustry contra|ctual intensity|||||||
|(0,1) District 0-10km fromGQ<br>|0.452<br>|0.279<br>|0.475<br>|0.193<br>|0.114<br>|0.671<br>|0.149<br>|0.425<br>|0.022<br>|0.034<br>|0.419<br>|0.207<br>|0.317<br>|0.089<br>|-0.182<br>|
|Abovemedian|(0.742)|(0.381)|(0.508)|(0.328) ~|-(0.163)|(0.627)|(0.393)|(0.500)|(0.188)|(0.137)|(0.658)|(0.399)|(0.453)|(0.208)|~—-(0.190)|
|(0.1)District0-10kmfromGQ|0.708%+|=0.383+=|0.744++|0.433++=|0.315++|0.329|-0.155|0.040|0.035|-0.026|0.299|-0.003|0.002|0.164|0.062|
|<br>Medianvalue andbelow|<br>(0.317)|<br>(0.196)|<br>(0.300)|<br>(0.194)|<br>(0.128)|(0.327)|(0.185)|(0.263)|(0.166)|(0.099)|(0.262)|(0.165)|(0.212)|(0.149)|(0.086)|
|||||IL. Panel|Asplitregres|sions usingme|dian ofindu|stryrelation|ship stickiness|||||||
|(0.1)District0-10kmfromGQ|0.543|0.161|0.453|0.425++|0.287++|0.367|-0.167|0.101|0.180|-0.007|0.166|-0.253|0.008|0.237|-0.019|
|<br>Abovemedian|(0.353)|(0.198)|(0.306)|<br>(0.208)|<br>(0.120)|(0.333)|(0.182)|(0.267)|(0.161)|(0.102)|(0.293)|(0.163)|(0.216)|(0.150)|(0.087)|
|(0.1) District 0-10km fromGQ<br>|1.015++<br>|0.866+++<br>|1.077+++<br>|0.100<br>|0.165<br>|0.931-+<br>|0.423<br>|0.385<br>|-0.044<br>|0.028<br>|1.198++ <br>|=0.578++<br>|0.517<br>|0.105<br>|0.097<br>|
|Medianvalue andbelow|(0.469)|(0.325)|(0.395)|(0.205)|(0.145)|(0.427)|(0.304)|(0.368)|(0.146)|(0.098)|(0.571)|(0.291)|(0.414)|(0.146)|(0.104)|
||J. PanelA|splitregressi|ons usingm|edian ofindus|try reliance o|n formal trade|finance (e.g|., letters of|export credit, b|anking transac|tions fortrad|einsurance)||||
|(0.1)District0-10kmfromGQ=|1.423+++|=0.633++_=|-1.087+++|—0.555++=|0.37344|0.656+|0.157|0.268|0.113|0.279+++|1119+|=(0.433+|0.528+|0.195|0.292-+|
|<br>Abovemedian|<br>(0.507)|<br>(0.270)|(0.366)|<br>(0.240)|<br>(0.147)|(0.359)|(0.233)|(0.313)|(0.166)|(0.100)|(0.343)|<br>(0.237)|(0.299)|(0.204)|(0.119)|
|(0.1)District0-10kmfromGQ|0.359|0.304|0.359|0.021|0.101|0.324|-0.059|0.156|0.152|-0.061|-0.063|-0.310+|-0.168|0.030|0.021|
|<br>Medianvalueandbelow|(0.363)|(0.235)|(0.338)|(0.156)|(0.119)|(0.359)|(0.208)|(0.292)|(0.137)|(0.097)|(0.327)|(0.178)|(0.219)|(0.173)|(0.081)|
||||K.|PanelA split|regressions us|ingmedian o|findustry acc|ess to Buyer|-Supplier trad|e credit||||||
|(0.1) District 0-10km fromGQ|0.880+|0.556++|0.557|-0.025|0.049|1.203++|=0.665++|0.730+|0.001|0.046|0.774|0.299|0.333|0.088|-0.079|
|Abovemedian|(0.518)|(0.276)|(0.388)|(0.190)|(0.131)|(0.526)|(0.321)|(0.415)|(0.184)|(0.106)|(0.550)|(0.326)|(0.399)|(0.144)|(0.104)|
|(0.1) District 0-10km fromGQ<br>|0.408<br>|0.248<br>|0.363<br>|0.352+<br>|0.221<br>|0.114<br>|-0.247<br>|-0.072<br>|-0.028<br>|0.015<br>|0.253<br>|-0.023<br>|0.035<br>|0.220<br>|0.074<br>|
|Medianvalueandbelow|(0.336)|(0.223)|(0.328)|(0.194)|(0.137)|(0.314)|(0.176)|(0.257)|(0.160)|(0.105)|(0.277)|(0.158)|(0.219)|(0.155)|(0.091)|
|||||L. Panel A<br>s|plitregressio|nsusingmedia|n ofindustry|external fi|nance dependen|ce||||||
|(0.1)District0-10kmfromGQ|1.117++|0.668+++|=0.745++|0.022|0.076|0.496|0.149|-0.127|-0.110|-0.038|0.529|0.327|0.186|-0.003|0.067|
|<br>Abovemedianindustry|(0.440)|<br>(0.251)|<br>(0.348)|(0.147)|(0.140)|(0.381)|(0.249)|(0.278)|(0.161)|(0.106)|(0.384)|(0.239)|(0.287)|(0.147)|(0.105)|
|(0.1) District 0-10km fromGQ|0.335|0.283|0.424|0.242|0.064|0.396|-0.033|0.199|0.142|-0.008|0.032|-0.136|-0.098|-0.013|-0.017|
|Medianvalue andbelow|(0.338)|(0.210)|(0.286)|(0.215)|(0.134)|(0.326)|(0.194)|(0.263)|(0.147)|(0.105)|(0.318)|(0.199)|(0.249)|(0.177)|(0.096)|
|Notes: See Table 1.The tablepresent<br>|s splitregres<br>|sions foragiv<br>|enindustryt<br>|rait.Measures <br>|ofcapital, land.<br>|humancapital <br>|or skillintensi<br>|tyusingASI <br>|dataismeasured<br>|withthe dataf<br>|romthe initialy<br>|ear2000.Pro<br>|duct durability<br>|is the sector'<br>|s share of<br>|
|durable consumerproducts, semi- du<br>semi- durable ortransportproducts, t<br>|able consum<br>he share take<br>|erproducts o<br>s the valueof<br>|transportpr<br> 0.GVCinten<br>|oducts. Product<br>sityismeasure<br>|s, atthe HS6- <br>dusingtheOE<br>|digit level, are<br>CD Tradeinval<br>|are identifie<br>ueaddeddata<br>|d based on <br>base account<br>|he UN BEC cla<br>ingforbothfor<br>|ssification. Ifs<br>wardandbackw<br>|ector(4-digit) d<br>ardlinkages. C<br>|oes notconta<br>ontract intensi<br>|inanyproduc<br>ty: The propo<br>|ts classifieda<br>rtionof a sec<br>|s durable.<br>tor's<br>|
|intermediate inputs thatrequire relat<br>resolve uncertaintyintradepayments<br>durationdata.Thehigher degree ofs<br>buyers orsuppliers thatinvolvethet<br>potentiallyreduces theirdependence <br>requirements foroutside capital. This|onship-specif<br>,followingC<br>tickiness tran<br>ransferofgoo<br>onbankfina<br> is also the de|c investment<br>rozet et al. (20<br>slates intoal<br>ds orservice<br>ncing (Fisman<br>finitionofRa|(e..those <br>21).Relation<br>owerprobabil<br>s without an <br>andLove 20<br>janandZinga|hatare differen<br>ship stickiness: <br>ityofswitching<br>advance orimme<br>03).Chor-Mano<br>les (1998),howe|tiatedproducts<br>BasedonMart<br> andlongerfir<br>diate transfer <br>vameasure ex<br>ver, it is comp|.basedonUS <br>in et al. (2023). <br>m-to-firmtrade <br>ofpaymentfun<br>temalfinance d<br>utedoverthep|IO data. Trad<br> This is<br>ameas<br>relationships. <br>ds. Insome s<br>ependence as <br>eriod 1996-20|finance inte<br>ure ofstickin<br>conditionalo<br>ectors,firms <br> the fraction o<br>05 usingcomp|sity:Ameasure<br>ess inbusiness r<br>nmatchquality. <br>routinely receive <br>ftotal capital ex<br>ustat data.|ofintensityin <br>elationships co<br>Buyer-Supplier <br> morebuyer-sup<br>penditurenotfi|tradefinanceb<br>nstructed atth<br>trade credit:Th<br>pliertrade cre<br>nancedbyinte|yproduct, us<br>e product-leve<br>isreferstotr<br>ditwhichgive<br>malcashflow|ng dependenc<br>lusingfirm-t<br>ansactionsbe<br>sthemanalte<br>s from operati|e onletters o<br>o-firmrelation<br>tweena firma<br>mative to and<br>ons,andrefle|fcreditto<br>ship<br>ndits<br>thus<br>cts firms”|
# **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.

18
Table 4: Plant-level effects of GQ access: estimation of plant exit/closure and performance outcomes

|||Sa|mple sche|me||
|---|---|---|---|---|---|
|DV-Loggedvaluesof|;<br>Exit[0/1]|Output|Avg.<br>.<br>plant size <br>(emp.)|Labor<br> productivity|TFPR|
||1|2|3|4|45|
|(0.1) District 0-10 km fromGQ|-0.11444<br>” (0.054)|(0.123444<br>" (0.040)|0.016<br>(0.021)|0.11377<br>~—(0.045) ~—-|«(0.015<br>(0.020)|
|N<br>|18321<br>|"<br>10213<br><br>|10211<br>|10172<br>|9853<br>|
|rR|0.022|"<br>0.947|0.926|0.912|0.907|
||B.|Young||||
|(0.1) District0-10km fromGQ|-0.140¢+ <br>|)<br>—0.145++<br>|0.071<br>|0.050<br>|0.013<br>|
||” (0.057)|" (0.063)|(0.046)|(0.061)|(0.033)|
|N<br>|6255<br>|"<br>3486<br><br>|3486<br>|3479<br>|3373<br>|
|rR|0.026|"<br>0.944|0.905|0.910|0.912|
||C.|Mature||||
|(0.1) District0-10km fromGQ|-0.100+<br>|O.111+<br>|-0.014<br>|0.14577<br>= <br>|(0.017<br>|
||” (0.056)|" (0.058)|(0.025)|(0.057)|(0.029)|
|N<br>|9459<br>|"<br>5245<br><br>|5243<br>|5222<br>|5051<br>|
|rR|oo19|"<br>0.950|0.936|0.911|0.909|
||D|. Old||||
|(0.1)District0-10kmfromGQ|-0.106++<br>|=0.114+<br>|-0.019<br>|0.1647<br>|0.015<br>|
||<br>” (0.050)|<br>" (0.061)|(0.044)|= (0.064)|(0.052)|
|N<br>|2607<br>|"<br>1482<br><br>|1482<br>|1471<br>|1429<br>|
|rR|0.024|"<br>0.939|0.937|0.915|0.878|

Note: The table limits the sample to plants from the sample scheme observed in the data in the five before and after the nearest GQ segment being completed. “Exit” is an indicator variable measured as the failure to observe the same plant in the post-GQ period. Table reports OLS coefficient estimates of the effect on plant outcomes due to proximity to the GQ segment. All specifications include an unreported constant term, regressors for completion interacted with an indicator for nodal district and district 10 - 50 km from the nearest GQ segment, and plant and year fixed effects. Sample is restricted to comprise the same set of districts available in the repeated cross-section analysis above. Standard errors are clustered by district. Significance levels are indicated by + < 21,+7-<:05,++7<:01.
and older plants increase labor productivity by 16.4%.<sup>17</sup> Older plants may be growing or gaining higher rents à la de Loecker et al., while shedding some labor, relative to younger plants that hire as they expand.

# _iii._ **_The role of initial plant capabilities/characteristics_**

Table 5 splits the regressions presented in Table 4 by plant position above or below median values of four pre-GQ levels of plant capabilities: skill intensity defined as the average plant wage per employee; capital intensity measured as the ratio of the value of tangible assets to total revenue; labor productivity; and TFPR.

The impact of proximity on exit follows the same decreasing gradient with age found previously although surprisingly, there appears little relationship with initial plant capabilities. Except for capital intensity for young plants, in general more able plants do not seem to survive better with proximity than those less so.

However, there are important differential effects on output and plant size that are consistent with findings in the aggregate data, that again suggest two types of adjustments for young and old plants. These adjustments indicate that exit may be a longer-term process as noted in Figure 1. With GQ proximity, young plants with above median skill intensity show a 10% increase for output compared to those below which show a negative but insignificant response. Older plants show the same pattern but where skill now mitigates contraction: output in below median skills plants fell by 11.8% and those with above median skills showing no significant effects.

A similar pattern emerges with capital intensity which is consistent with the industry characteristic effects discussed earlier: output in young plants with above median capital intensity increases by 13%, while those below the median contract by 9%; older plants with above median capital intensity show no effect while those below show a contraction of 8.5%. More sophisticated young plants grow with proximity to the highway network, while sophisticated older plants, contract less. Plant size (employment) also follows the same pattern, albeit with less of an age “twist”.<sup>18</sup>

> 17 The only significant coefficient in the census scheme suggests this gradient as well with young firms, again an insignificant 4.3% and older firms with a significant 14.4% (Annex table 4).

> 18Young firms with above median skills are 7.4% larger and those below are 6% (insignificant), very similar to older firms. Those above average in labor productivity show increases of 9.2% and those below, 6.9%. A similar pattern appears, although again, more mutedly, for mature firms for skills, capital intensity and labor productivity. Similar effects appear for labor productivity for small firms (capital intensity), mature firms (skills) although signs flip for above and below average labor productivity suggest perhaps mechanical mean reversion effects dominating. Similarly, TFPR for small and mature firms shows the same pattern for capital intensity differences, and perhaps mean reversion effects for labor productivity.

20
Table 5: Plant heterogeneity and the impact of market access: Sample Scheme

||||Young|||||Mature|||||Old|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|DV-Loggedvaluesof|Exit[0/1]|Output|mnnt.)<br>emp.)<br>|oeit<br>productivity|y TEPR<br>|Exit[0/1]||Output|mnnt)<br>emp.)|eercy<br>productivity|TFPR —|Exit{0/1]|Output|mnnt)<br>emp.)|veerty<br>productivity|FPR|
|||1)|Q)|@)|(4)||(5)|(6)|(7)|(8)||(9)|(10)|(11)|(12)|
||||||A. Sample s|cheme interact|ions with ini|tial skill intens|ity|||||||
|(0.1)District0-10kmfromGQ|-0.138++=|0.098|0.07444|-0.012|0.024|-0.107+|0.004|0.034|-0.076++|0.016|-0.104+|0.017|0.098+|-0.05|0.002|
|<br>*abovemedian|<br>(0.066)|<br>(0.043)|(0.031)|(0.043)|(0.022)|(0.061)|(0.037)|(0.022)|(0.037)|(0.017)|(0.054)|(0.043)|(0.056)|(0.041)|= (0.027)|
|(0,1)District 0-10km fromGQ<br>|-0.139++<br>|~—-0.045<br>|-0.06<br>|0.041<br>|-0.017<br>|-0.095+<br>|0.011<br>|~0.044-=<br>|0.096++<br>|-0.005<br>|0.110<br>|-0.118++ <br>|— -0.066+<br>|-0.001<br>|-0.019<br>|
|*belowmedian|(0.053)|(0.042)|(0.039)|(0.035)|~— (0.022)|(0.054)|(0.039)|(0.021)|(0.037)|—(0.016)|(0.053)|(0.053)|(0.040)|~—s (0.045)|(0.036)|
||||||B. Sample sch|eme interactio|ns with initi|al capital inten|sity|||||||
|(0.1)District 0-10km fromGQ|-0.170+++|0.132+++|= 0.036|0.103+4+|= 0.05974|0.100|=90.0817+|= 0.038+|0.025|0554+|-0.098+|-0.007|0.05|0.024|0.024|
|*abovemedian|(0.063)|(0.035)|(0.027)|(0.036)|(0.022)|(0.055)|(0.034)|(0.020)|(0.033)|(0.018)|(0.056)|(0.051)|= (0.056)|(0.044)|(0.041)|
|(0,1)District 0-10km fromGQ<br>|-0.112-=<br>|-0.091++<br>|-0.04<br>|-0.064<br>|= -0.061+++<br>|-0.101+<br>|-0.069+<br>|-0.041+<br>|-0.02<br>|0.044--><br>|-0.110-+<br><br>|= -0.085+<br>|-0.017<br>|-0.013<br>|-0.038<br>|
|*belowmedian|(0.056)|(0.041)|(0.030)|(0.041)|~— (0.021)|(0.059)|= (0.037)|— (0.021)|(0.033)|(0.016)|(0.051)|(0.045)|(0.028)|~— (0.038) ~|—_(0.033)|
||||||C. Sample|scheme inter|actions with|initialTFPR||||||||
|(0.1)District0-10kmfromGQ<br>|-0.148++<br>|0.027<br>|0.044<br>|-0.013<br>|-0.012<br>|0.092<br>|0.005<br>|0.02<br>|-0.029<br>|-0.016<br>|-~0.099+<br>|—-0.023<br>|0.066<br>|-0.006<br>|-0.053<br>|
|<br>*abovemedian|(0.058)|(0.040)|+= (0.035)|(0.037)|~— (0.025)|(0.059)|(0.030) _|~— (0.019)|(0.032)|(0.018)|(0.049)|(0.036)|= (0.055)|(0.045)|—(0.036)|
|(0,1)District 0-10km fromGQ|-0.147++|0.028|-0.044|0.065|0.019|-0.114++|0.015|-0.017|0.034|0.03|-0.110-+|-0.087|-0.033|-0.045|0.051|
|*belowmedian|(0.061)|(0.041)|(0.032)|(0.043)|(0.021)|(0.055)|(0.043)|(0.019)|(0.036)|(0.020)|(0.053)|(0.062)|(0.038)|(0.048)|(0.036)|
|||||D|. Sample sche|me interaction|s with initia|l labor product|ivity|||||||
|(0.1)District 0-10km fromGQ<br>|-0.126-+<br>|0.028<br>|0.0924<br>|-0.087+<br>|-0.03<br>|-0.104+<br>|0.018<br>|0.039+<br>|0.097<br>|-0.011<br>|-0.093+<br>|-0.032<br>|0.062<br>|-0.039<br>|0.008<br>|
|*abovemedian|(0.061)|(0.048)|(0.032)|(0.047)|— (0.025)|(0.060)|(0.038)|~—-(0.021)|(0.033)|(0.015)|(0.049)|(0.050)|= (0.054)|— (0.050) ~|— (0.025)|
|(0,1)District0-10kmfromGQ|=-0.152+++|900.029)|-0.069%+|=0.1084|0.038+|-0.096+|0.039|-0.0477+|0.120-++|0.026|-0.110+|-0.043|0.034|0.003|-0.017|
|<br>*belowmedian|<br>(0.057)|(0.040)|<br>=(0.035)|<br>(0.038)|<br>(0.023)|(0.053)|(0.037)|(0.021)|(0.033)|(0.018)|(0.058)|(0.051)|=(0.035)|(0.047)|=(0.045)|

Notes: See Table 4. Estimations in the base effect presented in Table 4 is interacted with indicator variables for above or below median values for indicated plant traits.
In the large firm sample of the census scheme there are virtually no significant coefficients on any outcome variables in the aggregate (Table A.6), although breaking apart by initial conditions offers some evidence of similar patterns – higher skill and capital intensity are correlated with high output and plant size responsiveness for young and mature plants, and with higher TFPR for mature and old plants (Annex Table A.7).

These results support the expected heightened effect of increased competition and the opportunity tapped by more capable firms. For younger plants, the effect is to expand size, while for the older ones, it is to defend the existing size. The less capable older plants are eventually likely to exit. The relative rise in labor productivity of older plants, despite lower output gains suggests some relative downsizing, but also in line with district-level pseudo panel results, a possible de Loecker et al. (2016) effect.

# **V. Conclusion**

The natural experiment offered by the GQ upgrades allows examining the adjustment process across plants of different age cohorts and characteristics to an increase in competition and access to markets. Young plants drive the previously documented aggregate positive effects of proximity to the GQ network, while older plants show insignificant or negative effects. Younger plants tend to be more capital intensive and engaged in value chains, suggesting an important domestic “offshoring” dynamic along the GQ. Though in the short-run, exit falls for all plant cohorts in districts along the GQ network, the diverging responsiveness over time between young and older plants suggests that in the long-run older plants will contract and possibly exit. Strikingly, the evidence suggests that this process may take decades.

It is also clear that building roads is not sufficient for local industrial development. The responsiveness of young plants is blunted where factor markets are incomplete, or the business climate is distorted. With the exception of the capabilities of plants and the work force, older plants are less sensitive to misallocation in input and output markets, suggesting that over time they have devised ways around them. The fact that older plants respond better to proximity to the GQ in more distorted districts suggests a de Loecker et al effect where they protect their output market while benefiting from cheaper inputs.

Finally, plant and workforce capabilities are critical complements to physical infrastructure, allowing escape from new competition and taking advantage of new markets: young plants with high human capital or capabilities drive the aggregate positive response to connectivity among these plants, while older plants with low human capital or capabilities drive their contractionary tendencies.

22