Port basin choice with Russian container trade
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1 Port basin choice with Russian container trade By Simme Veldman, ECORYS Nederland, The Netherlands Olga Gopkalo, Morstroytechnology LLC, Russia Presentation at 4-th IFSPA in Chengdu, China
2 The study objective is To establish a demand choice function for Russian container port services per maritime basin
3 The study approach is To estimate the coefficients of a MultiNomial Logit (MNL) Model with regression analysis To calculate a demand function by simulating the impact of cost changes on port demand
4 Estimation of logit models on port choice Malchow & Kanafani (2001) and (2004): Tiwari et al. (2003) Veldman & Bückmann (2003) Blonigen & Wilson (2006) Veldman & Rachman (2008) Anderson et al. (2009) Garcia-Alonso & Sanchez-Soriano (2009): Veldman et al. (2010) related model applications concern modal split land versus maritime transport
5 The port choice: model proposed Pijk (p = k p = 1...P) = e utility attached to the routing via port k for trade between i and j Uijk p= P e U ijp p=1 probability of choosing port k from all possible ports p = 1..P, for province i = 1..I and trade partner j= 1 J Uijk = 0k + 1 CL ik + 2 CM jk + 3 Q k Quality of service aspects for i, j and port k Maritime transport cost between trade partner j and port k Inland transport cost between province i and port k
6 Basic model tested U ijk = a 0k + a1 CL ik + a 2 CM jk + a 3 TL ik + a 4 TM jk where: CLik: CMjk: TLik: TMjk: (2) inland transport costs between region i and port k; maritime transport cost between trade partner j and port k; inland transport time between region i and port k; maritime transport time between trade partner j and port k; Ln ( P ijk / P ijp ) = U U = + (CL CL ) + (CM CM ) + (TL TL ) ijp ijk 0 1 ip ik 2 jp jk 3 ip ik + (TM TM ) 4 jp jk (4) where α0 = αk0 - αp0.
7 The explanatory variables or attributes of the logit model Inland transport cost Transport takes place by rail and road. We take the distance by road and rail between the provincial gravity point and the seaport as basis multiplying this with unit costs Maritime transport cost The port basins of the Baltic basin, the Black Sea basin and the Far East basin do not have have direct liner services with the main areas of the world and therefore have to rely on feeder transport. This situation, however, is changing Inland transport time It is based on rail and road distance using specific unit costs Maritime transport time It is based on the sum of mainline and feeder line costs by taking half of the time of a roundtrip
8 Statistical analysis Data Russian containerised imports and exports channeled by sea. Through port basin, j j=1 3 For 8 Russian regions, i i = 1 8 Imports and exports All the trade partner countries are grouped into 10 foreland regions This leads to 8 x 3 x10 = 240 potential flows Source of data Russian Custom Statistics
9 Port basin choice in Russia
10 Russian districts
11 Russian container trade by border area (in 1000 tons) volume Border category Baltic South Far East subtotal Other categories Asian rail unknown subtotal Grand total Border category Baltic South Far East subtotal Other categories Asian rail unknown subtotal Grand total Data 2006 shares in subtotal 12,729 2,069 2,831 17,629 72% 12% 16% % 1,707 81% 2,115 19,744 Data 2007 volume shares in subtotal 3, ,084 5,127 68% 11% 21% ,874 18,145 23,273 1% 99% shares in grand total 89% 11% shares in total 22% 78%
12 Russian container trade by hinterland region (in 1000 tons) Hinterland region 1 Northwest 2 Central 3 South 4 Southeast 5 Ural 6 West Siberia 7 East Siberia 8 Far East 9 Unspecified Total import 6,289 5, , export total 1,934 8, , ,204 1, , ,988 19,744 shares 42% 27% 5% 8% 4% 4% 5% 5% 0% import 7,150 6,199 1, , , export total 2,193 9, , ,174 1,123 1, , ,044 1,046 1, , ,678 23,273 shares 40% 28% 5% 7% 5% 4% 5% 5% 0%
13 Russian container trade by foreland region (in 1000 tons) Foreland region 1 Northwest Europe 2 West Mediterranean 3 East Mediterranean 4 South Am. East Coast 5 Arabian Sea Area 6 Southeast Asia 7 Oceania 8 East Asia 9 Korea 10 Japan Total selected regions Other regions Total import 3, , , ,380 2,376 13, export total 1,246 5, , , , , , , ,493 15,874 1,495 3,870 5,988 19,744 shares 32% 5% 8% 7% 7% 7% 1% 22% 7% 4% - import 3, , ,995 1, ,183 3,411 16, export total 1,153 4, , ,300 1,314 1, , , , ,253 18,436 1,425 4,837 6,678 23,273 shares 27% 5% 8% 7% 10% 6% 1% 27% 6% 4% -
14 Results of regression analysis Variables Imports 2006 coefficient t-value Constant Maritime costs Inland costs Maritime time Inland time Dummy south Adj. R Square Constant Total costs Total time Dummy south Adj. R Square No of observ Exports 2006 coefficient t-value Imports 2007 coefficient Separate cost and time variables Total cost and time variables t-value Exports 2007 coefficient t-value
15 Result of regression analyses Maritime cost variable does well: both imports and exports and for 2006 and T-values range from 3.5 to 6.3 Related coefficient values vary between for imports and for exports Inland cost variable does less well: t-values are low Related coefficient values for exports even have the wrong sign Time variables show a mixed results: in some cases significant in other cases not Total cost variable does well in all cases with t-values ranging from 7.1 to 9.3 Total time variable is significant for 2006 data only The constant value is negative indicating an over-estimation of the share of the Baltic Region ports
16 Demand choice function 60% 2006 Imports % throughput change 50% Exports 2007 Imports 40% 2007 Exports 30% 20% 10% 0% % -20% -30% -40% Change in USD per FEU
17 Conclusions Statistical tests show a significant influence of inland and maritime transport costs (t-values are high) Results can be used to derive a demand choice function to be used for economic and financial evaluation The value of port choice elasticties for the South Basin ports is low with elasticity values ranging from to Veldman and Buckmann (2003) measured a value of for the port of Rotterdam Blonigen and Wilson (2006) a value of -1.5 for US containerised imports Anderson et al. (2009) values ranging from to for US imports Veldman et al. (2010) values of -0.2 for Spanish container imports and exports It can be concluded that the values we measured are low
18 Thank you for your attention!
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