Эконометрика

We suggest the following methodological approaches should one study the link between logistic infrastructure and transportation costs while assessing trade and economic cooperation in the Asis Pacific for Russia. By evaluating this on real world data, this study intends to test the following hypotheses: (1) a lack of logistic infrastructure leads to an increase in transportation costs; (2) there is a correlation between high transportation costs and new investments in transport infrastructure; (3) an exceeding share of logistics costs in the price of raw materials leads to a reduction in their export volumes to the countries of the Asia-Pacific region.

Well, total transportation costs were calculated for the major commodity which is traded eastwards (coal) in order to figure out the portion of the commodity export price attributed to transportation costs. The following parameters were calculated for specific APR countries (China, South Korea, Vietnam, India – are major importers of Russian coal), covering a period from 2014 to 2024, to use in panel data modelling:

— railway and maritime transportation costs from Russia to APR countries (USD);

— CIF price for Russian coal export calculated from benchmark Newcastle FOB price for APR countries (USD per ton);

— transportation costs as a percentage of the CIF price (integrated and separate for railway and maritime) for each year (%).

Also, some basic statistics data were gathered:

— export flows of Russian coal to APR countries (APR imports as mirror data) from Trade Map database (mln tons and mln USD);

— volume of coal transshipment in Far Eastern ports from the RZD Research Centre (tons);

— throughput capacity of the Vostochny polygon railway network from the RZD website (tons);

— average exchange rate (USD/RUB);

— GDP growth rate (%);

— unemployment rate (%);

— inflation rate (%);

— key rate (%).

For the first hypothesis, the Pooled OLS model is going to be used, as it describes the initial data the most adequately, passed the statistical tests (Wald test, Breusch-Pagan test and Hausman test, which indicated no panel effect between countries) and passed the Gauss-Markov theorem conditions.

A correlation matrix is going to be build for the second hypothesis. Pearson’s correlation coefficients are going to be checked for statistical significance using the Student t-test.

As for the third hypothesis, the Fixed effect model is going to be used, as it describes the initial data the most appropriately (takes into account panel effect — country differences in transportation costs) and passed the abovementioned statistical tests. Standard robust errors in White form were used in adjusting the model, as it did not pass the Wald test (heteroskedasticity) and the Wooldridge test (autocorrelation).

The Pooled OLS model results (see Appendix 2) showed that with a 1% increase in the Vostochny polygon throughput capacity, the integrated logistics costs as a percentage of the CIF price decrease by 0.509 percentage points on average, all other things being equal.

The final equation of the Pooled OLS regression model includes:

- – integrated (maritime and railway) transportation costs as a percentage of the CIF coal price;

- – natural logarithm of the throughput capacity of the Vostochny polygon railway network;

- – average exchange rate;

- – GDP growth rate;

- – unemployment rate;

- – inflation rate;

- – key rate.

The tested hypotheses may provide a clear understanding of what transportation costs and its share in product price are favourable for international trade and what level of transportation costs restraints trade in the light of different coal prices. These conclusions are of great importance for infrastructure development in various scenarios of coal consumption and price.

For more details, please contact me in person.

Igor B. SEVASTIANOV

PhD, Solid Mechanics

New Mexico State Uni

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