When was golden quadrilateral launched

Sutanu Guha. Yatharth Chauhan. Jatin Jhamb. View More. Nitin Kadian. Popular Cars. The total length of national highways in the country is 58, km. The Ministry of Road Transport and Highways has also taken up improvement of riding quality and widening of national highways other than those covered under the NHDP that constitutes a length of 44, km.

Riding quality of the balance length is targeted for improvement by The tremendous thrust to road development under the present Government can be gauged from the fact that whereas in the last 50 years, only km of national highways were 4-laned, but between to , over 14, km are being 4-laned. In certain States, computerisation has already started to enable building up a proper nation-wide database on vehicles.

The NHDP and other road development projects will result in immense economic and social benefits for our country, like:. Employment generation. Thrust to road construction, cement and steel industries. Benefits to trade especially in movement of perishable goods such as agricultural produce from hinterland. Savings in vehicle operating costs. Faster, comfortable journeys. Prior to the GQ project, there was some infrastructure linking these cities. In a minority of cases, the GQ project built highways where none existed before.

In other cases, however, a basic highway existed that could be upgraded. Of the 70 districts lying near the GQ network, new highway stretches comprised some or all of the construction for 33 districts, while 37 districts experienced purely upgrade work. In panel d , we split the 0—10 kilometres interaction variable for these two types of interventions. The entry results are slightly stronger in the new construction districts, while the labour productivity results favour the road upgrades.

This latter effect is strong enough that the total output level grows the most in the road upgrade districts. Despite these intriguing differences, the bigger message from the breakout exercise is the degree to which these two groups are comparable overall.

Panel e extends the spatial horizons studied in panel b to include two additional distance bands for districts 50— kilometres and — kilometres from the GQ network. These two bands have 48 and 51 districts respectively. In this extended framework, we measure effects relative to the 97 districts that are more than kilometres from the GQ network. Two key observations can be made. First, the results for districts 0—10 kilometres away are very similar when using the new baseline.

Second, the null results generally found for districts 10—50 kilometres from the GQ network mostly extend to districts 50— kilometres from the GQ network.

Even from a simple association perspective, the manufacturing growth in the period surrounding the GQ upgrades is localised in districts along the GQ network. Chandra and Thompson described their results within a theoretical model of spatial competition whereby regional highway investments aid the nationally oriented manufacturing industries and lead to the reallocation of economic activity in more regionally oriented industries like retail trade.

Unreported estimations suggest that this local reallocation is not happening for Indian manufacturing, at least in a very tight geographic sense. Either way, the lack of statistical precision for these estimations prevents strong conclusions in this regard. Appendix Table A3 provides several robustness checks on these results. We first show very similar results when not weighting districts and including dropped outlier observations.

We obtain even stronger results on most dimensions when just comparing the 0—10 kilometres band to all districts more than 10 kilometres apart from the GQ network, which is to be expected given the many negative coefficients observed for the 10—50 kilometres band.

We also show results that include an additional 10—30 kilometres band. This estimation confirms a very rapid attenuation in effects. The Appendix also shows similar inverted findings when using a linear distance measure over the 0—50 kilometres range.

Appendix Table A4 documents alternative approaches to calculating labour productivity and TFP consequences. The stability of the results in Table 2 is encouraging, especially to the degree to which they suggest that proximity to the GQ network is not reflecting other traits of districts that could have influenced their economic development.

There remains some concern, however, that we may not be able to observe all of the factors that policy makers would have known or used when choosing to upgrade the GQ network and designing the specific layout of the highway system. For example, policy makers might have known about the latent growth potential of regions and attempted to aid that potential through highway development.

We examine this feature by comparing districts proximate to the GQ network to districts proximate to the NS—EW highway network that was not upgraded. The idea behind this comparison is that districts that are at some distance from the GQ network may not be a good control group if they have patterns of evolution that do not mirror what districts immediately on the GQ system would have experienced had the GQ upgrades not occurred.

This comparison to the NS—EW corridor provides perhaps a stronger foundation in this regard, especially as its upgrades were planned to start close to those of the GQ network before being delayed.

The identification assumption is that unobserved conditions such as regional growth potential along the GQ network were similar to those for the NS—EW system conditional on covariates. To ensure that we are comparing apples with apples, we identified the segments of the NS—EW project that were to begin with the GQ upgrades and those that were to follow in the next phase.

We use separate indicator variables for these two groups so that we can compare against both. The online Appendix provides greater detail on this division.

Table 3 repeats panel b of Table 2 and adds in four additional indicator variables regarding proximity to the NS—EW system and the planned timing of upgrades.

In this estimation, the coefficients are compared to districts more than 50 kilometres from both networks. The lack of precision is not due to too few districts along the NS—EW system, as the district counts are comparable to the distance bands along the GQ network and the standard errors are of very similar magnitude.

The null results continue to hold when we combine the NS—EW indicator variables. Put differently, with the precision that we assess the positive responses along the GQ network, we estimate a lack of change along the NS—EW corridor. See Table 2. The regressions control for the initial district conditions listed in Table 2. Tables 4 and 5 consider this problem using IV techniques. Rather than use the actual layout of the GQ network, we instrument for being 0—10 kilometres from the GQ network with being 0—10 kilometres from a mostly straight line between the nodal districts of the GQ network.

This sample contains districts. Panel c reports IV estimation that instruments being within 10 kilometres from the GQ network with being within 10 kilometres of the straight line between nodal districts. The null hypothesis in the exogeneity tests is that the instrumented regressor is exogenous. Table 4 Including District Controls. See Table 4. Estimation includes district controls from panel b of Table 2 other than road and railroad access variables.

The identifying assumption in this IV approach is that endogenous placement choices in terms of weaving the highway towards promising districts or struggling districts 15 can be overcome by focusing on what the layout would have been if the network was established based upon minimal distances only.

This approach relies on the positions of the nodal cities not being established as a consequence of the transport network, as the network may have then been developed due to the intervening districts. The districts may also have possessed more favourable spatial positions. To guard against these concerns, we will estimate the IV with and without the battery of covariates for district traits in Panel b of Figure 1 shows the implementation.

IV Route 1 is the simplest approach, connecting the four nodal districts outlined in the original Datta study. We allow one kink in the segment between Chennai and Kolkata to keep the straight line on dry land. IV Route 1 overlaps with the GQ layout and is distinct in places. Yet, as IV Route 2 shows, thinking of Bangalore as a nodal city is visually compelling. We thus test two versions of the IV specification, with and without the second kink for Bangalore.

For this IV estimation, we drop nodal districts sample size of districts and measure all effects relative to districts more than 10 kilometres from the GQ network. This approach only requires us to instrument for a single variable — being within 10 kilometres of the GQ network. In most cases, we do not statistically reject the null hypothesis that the OLS and IV results are the same.

Wage and labour productivity are the two exceptions, where the IV indicates that OLS underestimates the true impact. In Table 5 , we repeat this analysis and further introduce the district covariates measured in that we modelled in panel b of Table 2.

Among the controls added, the inclusion of the total population control is the most important for explaining differences between Tables 4 and 5. We again do not statistically reject the null hypothesis that the OLS and IV results are the same for most outcomes.

On the whole, we find general confirmation of the OLS findings with these IV estimates, which help with particular concerns about the endogenous weaving of the network towards certain districts with promising potential.

IV estimates indicate that there may be an upward bias in the entry findings, perhaps due to endogenous placement towards districts that could support significant new plants in terms of output.

A second alternative is that the GQ upgrades themselves had a particular feature that accentuates these metrics e. Table 6 implements this approach using the , , , and data. These estimates cluster standard errors by district, weight districts by log population in and include observations from the cross of five periods with districts where manufacturing plants, employment and output are continually observed in all five surveys.

The results are quite similar to the earlier work, especially for the entry variables. The total activity variables in columns 1—3 are somewhat diminished, however, and we later describe the time path of the effects that is responsible for this deviation. We report them for completeness but we do not discuss their dynamics further. Panel a repeats the base specification in the narrower range. Estimation in panel b separates upgrades by completion date. Estimation reports standard errors clustered by district, includes district and year fixed effects, with observations and observations weighted by log total district population in Panel b studies the actual completion dates of the GQ upgrades.

Due to the size of the GQ project, some sections were completed earlier than other sections. The results are strongest for sections completed by March , closely followed by those sections completed by March The solid line quantifies the differential effect for the GQ upgrades by year, with as the reference year.

Panels b and d consider comparable output estimations. Appendix Table A6 reports complete regression results.

By separately estimating effects for each year, we can observe whether the growth patterns appear to follow the GQ upgrades hypothesised to cause them. Conceptually, we also believe this dynamic approach is a better way of characterising the impact of the GQ upgrades than the specific completion dates of segments. Once the upgrades started, work began all along the GQ network and proceeded in parallel. Every state along the GQ network had at least one segment completed within the first two years of the programme.

Work continued thereafter across all states, with the average spread of completion times between the first and last segments for states being 6. Since manufacturing activity and location choice decisions can easily be influenced by upgrades on nearby segments and even anticipation of future upgrades to a segment , we believe it more appropriate to model the GQ event as a whole, timing the impact of all segments from The entrant patterns are pretty dramatic. Once the GQ upgrades commence, the log entry counts in neighbouring districts outpace those a bit farther away.

These gaps increase throughout the period and are statistically significant in and In panel b , output rises more dramatically and increases up until the upgrades are mostly complete. The differences begin to diminish in and then stabilise for —9.

New output and employment growth substantially lead the new establishment effects, a pattern reflective of large plants being the earliest to respond to the GQ upgrades.

Panels c and d show the series for log total plant counts and output. Aggregate plant counts are very stable before the upgrades start. After the GQ upgrades start, total plant counts and output also climb and then stabilise, before climbing again as the sample period closes.

At all points during this post period, the coefficient values are positive, indicating an increase over levels, but the differences are not statistically significant until the end. The paths depicted in these Figures provide important insights.

The young entrant measures in panels a and b are in essence flow variables into the district. By contrast, the metrics in panels c and d are stock variables.

Thus, their gradual development over time as more entrants come in and the local base of firms expands makes intuitive sense. This choice, however, is not a sensitive point for the analysis. Utilising or delivers a very similar baseline, while the period would generally lead to larger effects due to the dip in some variables. The choice of averaging and is also illuminated.

The dynamics of most aggregate outcomes provide a similar picture to Figure 2. By averaging and , we give a better representation of the aggregate impact than using alone. We return to projections about future impacts from the GQ upgrades in the closing Section. The ideal scenario for this analysis is to have panel data on plants Glaeser et al. While we unfortunately lack this panel structure, we can use information on the ages of plants to consider cohorts over time.

The positive coefficient in column 1 for the 0—10 kilometres group suggests that a greater fraction of the firms already present in the 0—10 kilometres districts by i. Columns 2 and 3 further show that employment and output increased disproportionately for these incumbent firms.

Moreover, the relative magnitudes of columns 1—3 emphasise a point made earlier about the productivity results. For the 0—10 kilometres districts, output is rising at a faster pace than employment, leading towards higher labour productivity at the same time that plant survival is also growing.

By contrast, incumbents in the 10—50 kilometres districts are closing at a similar rate or even faster than the control group. As a result, their labour productivity is also rising but the origin of this productivity growth is very different from that in the districts near to the GQ network. Estimation compares activity among incumbents and entrants in districts along the GQ network.

Total activity for the district in is taken as the baseline for all estimation. Positive values indicate greater accumulated entry at the end of the sample period. Columns 7—9 consider as the outcome variable the raw share of activity among older incumbent firms. Estimation includes the covariates for initial district conditions and additional road and railroad traits used in panel b of Table 2.

The outcome measures are all very strong for the 0—10 kilometres districts. There is also some evidence suggestive of larger entrants being less likely to locate in the 10—50 kilometres band. Thus, both entrants and incumbents contribute to the aggregate growth evident in Table 2. Despite their better survival rates and growth compared to distant incumbent peers, the share of activity accounted for by incumbent firms in districts along the GQ network declines.

Table 8 further analyses the productivity distributions and selection margins in districts by distance from the GQ network. We then calculate in column 1 the average normalised TFP in for plants within districts by distance from the GQ network. These entries sum over all industries and plants within each district group, weighting individual observations by employment levels. Normalised productivity levels are naturally centred on one and are somewhat higher in nodal districts — a typical finding in urban productivity patterns — with the further initial differences over the other distance bands being marginal.

Column 1 reports initial values in Column 3 reports the value for plants at least ten years of age and their ratio to the initial value in Column 4 reports a similar statistic for subsequent entrants and their ratio to the initial value in Districts in the 10—50 kilometres show a very strong selection effect towards incumbent plants, while districts in the 0—10 kilometres range show more homogeneous adjustments over entrants and incumbents. The normalisation process again centres values on one, such that aggregate TFP growth is removed at the industry level.

Overall, there is limited movement for any of the groups; the 0—10 kilometres range increases slightly, while the other three ranges show very small declines. This pattern is possible because the 0—10 kilometres group is becoming larger during the period of study.

The more interesting tabulations are in columns 3 and 4. Productivity rises with plant age, such that the values in column 3 are higher than in column 4. This may be due to differences in technical efficiency.

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