Aggregate Estimate of Panel Data from the Change Value

Table 3 presents the results of both the OLS regression and linear mixed model for

estimating the HSR impact on agglomeration economies based on panel data25. In columns

25 _{Note: This study also explores the relation between employment density and HSR service. Estimates are} similar to those using second measures on agglomeration economies. Therefore, only results from the latter measures are provided in this study.

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1, 2 and 3, changes in the agglomeration economies are regressed on multiple features of

introducing HSR service, such as travel time savings and train frequencies, with gradual

consideration of the control of economic performance, city characteristics, and geographic

location variables by using OLS. In column 4, the same regression is performed as in

column 3 but using the feature of HSR service with the GC index, track mileage amount in

1853 and elevation. Compared to the corresponding OLS coefficients, this study finds that

the estimated coefficient of the pooled OLS-IVs model is slightly less than previous simple

pooled OLS models. Most of these coefficients are significant at the same level, indicating

that the mutual relationship between HSR investment and agglomeration economies is not

a major source of estimation bias.

In columns 1, 2, 3 and 4, surprisingly, the measured travel time savings to/from

Paris elasticity of mean agglomeration economic growth in all models ranges from 4% to

8%. Although estimates are lower than theory suggests, this phenomenon is not statistically

significant among all cities. The coefficient of the level of HSR service (i.e., train

frequencies of HSR service) to/from Paris is 0.44 at a significance level of 1%. This

suggests that although almost all studies on this topic indicate that the travel time savings

is the key factor, the level of HSR train frequencies to/from Paris is actually the most

important and reasonable predictor for growth of agglomeration economies. This is close

to previous suggestions by Crozet (2013) that frequency is a decisive factor in favor of

TGV travel. Daily travel between Paris to Lyon, to Nantes, Rennes and Lille are often more

than 20 journeys. Given the same amount of travel time savings, more HSR train

frequencies indicate that there is more likely to be more interaction in economic activities

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To compare the impact of HSR train frequencies with all other rail mode, this study

also incorporates the level of all rail frequencies to/from Paris as an explanatory variable

into the models. All rail frequencies to/from Paris includes not only HSR frequencies but

also other regular rail, such as express trains and overnight trains. The coefficient of this

variable in all models ranges between –0.34 and –0.23 at a significance level of above 5%,

indicating a significantly negative relationship with urban agglomerating economies

growth. The reason for this is that overall train frequencies have generally been reduced

over the past three decades, and the conventional rail service has been gradually replaced

by HSR service. However, due to the operational costs of HSR service, complete

replacement of other rail services by HSR is not possible. For example, the city of Dijon

had 13 daily trains to/from Paris and 22 in total rail services from 1990 to 1999. Ten years

later, HSR train frequencies rose to 16 per day while the overall rail service dropped to 18

daily. The negative estimates reflect the very important role of HSR train frequencies in

boosting agglomeration economies.

In columns 5 to 8, results are shown for the number of linear mixed models that use

the same variables as the OLS models. After controlling for the correlation between cities

and also within a city of different periods, the coefficient on the level of HSR train

frequencies is positive and highly significant in all cases. However, the level of all rail train

frequencies is not statistically significant in the mixed model+IVs model, possibly because

the mixed model examines the correlation between variables of the level of all train

frequencies and HSR train frequencies and then reduces the influence of this variable.

Of note is that the coefficient of market access is nearly zero and not significant.

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comprehensive rail network that now covers most of the populated urban areas. Most

French cities have had improved accessibility not just to Paris but also to other cities.

Because of the equal improvement of overall market access, this cannot be used to predict

changes in agglomeration economies.

Most importantly, the significant positive estimate of the LGV dummy variable

suggests LGV cities have increased agglomeration economies, which is the reverse of what

the OLS-IVs model suggests. To further explore this relationship, this study carefully

observed agglomeration growth within 13 LGV cities, including Marseille, Lyon and Tours.

Nearly all LGV cities dispersed local agglomeration for 1982–1990 and 1990–1999;

however, all showed a strong increase for 1999–2009. Lyon and Tours, in particular,

increased their agglomeration economies by more than 11% over the previous time period.

This significant increase during the last period may overestimate the role of an LGV service.

This is consistent with the findings of Koning et al. (2013) who found that the two different

models gave opposing results for the relationship between LGV and changes in

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Table 6-3: Results of Aggregate Estimates

Dependent Variables: Changes in Agglomeration Economies

(1) (2) (3) (4) (5) (6) (7) (8)

Variables OLS OLS OLS OLS+IVs Mixed

Model Mixed Model Mixed Model Mixed Model+IVs

Travel Time Savings to/from Paris 0.08 0.04 0.05 0.06 0.12 0.02 0.14 0.18

Level of HSR Train Frequencies 0.44*** 0.28*** 0.30*** 0.29*** 32.26*** 27.07*** 29.13*** 28.01***

Level of All Rail Train Frequencies –0.34*** –0.23** –0.24** –0.23** –18.38** –14.95* –15.96* –14.87 Market Access Index 0.18*** 0.10 0.10 0.10 0.01** 0.00 0.00 0.00

HST 0.03 –0.05 –0.03 –0.03 –111.43 –143.03* –124.61 –119.53 LGV –0.21*** –0.15** –0.17** –0.15** 289.21** 226.61** 234.77** 212.64* Center Station – – – – –131.26 –230.88* –294.73** –260.15* Edge Station 0.05 0.02 –0.05 0.05 50.01 168.28 202.23 150.89 Periphery Station 0.07 0.1* 0.11 0.12** – – – – EconControl – Y Y Y – Y Y Y CityControl – Y Y Y – Y Y Y GeoControl – – Y Y – – Y Y Instrumental Variables – – – Y – – – Y Adjusted R-Squared 0.150 0.257 0.272 0.276 Observations (N) 321 321 321 321 321 321 321 321

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