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«Price Formation of Dry Bulk Carriers in the Chinese Shipbuilding Industry Liping Jiang December 2010 © University of Southern Denmark, Esbjerg and ...»

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The second group of research presents a very different point of view. Beenstock (1985) argues that the supply and demand theory is not appropriate for analyzing the ship price, because a ship has a considerable longevity. He adopts the asset pricing approach and Rational Expectation Hypothesis (REH) into maritime field as the starting point of his research. In the following cooperation with Vergottis (1989a; 1989b; 1992; 1993), Beenstock contributes several papers focusing on modeling shipping market by using asset pricing method. He argues that new building and secondhand ships are perfect substitute only differing in age, therefore prices of new ships adjust to the expected prices of secondhand ships overtime. It should be noted that Beenstock’s argument has been debated by the following research. Haralambides (2005) explains that secondhand prices are volatile whereas new prices are relatively sticky and two prices are not perfectly correlated. In Adland (2007) the newbuilding order is a forward contract for the delivery of an age zero vessel in the future and not the value of a vessel per se. For this reason, we adopt the classical supply and demand theory for analyzing the price formation of the Chinese dry bulk carrier.

There are several recent studies on the Chinese shipbuilding industry due to its dramatic growth. Most of extant literatures about the Chinese shipbuilding have mainly focused on product technology (Bai et al. 2007), shipbuilding management (Lu et al. 2000), labour cost (Chou 2001), industrial policy (Song 1990) and restructuring (Smyth 2004). Using the actual prices from Chinese shipyards, this paper makes the first attempt to analyze the price formation of dry bulk carrier in the Chinese shipbuilding industry based on a traditional supply and demand view.

3. The Econometric Model and Price Determinants Prices are determined by the interaction of supply and demand in the market. In this section, we assume dry bulk carrier prices and observed quantity represent equilibrium by the interaction of supply and demand in dry bulk carrier market.

3.1. The econometric model

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includes three variables indicating changes in supply curve, which are shipWt building capacity utilization (CAPA), shipbuilding cost index (C ) and ship export credit rate (CRATE ). The dry bulk carrier demand Qtd is a function of price Pt, exogenous demand side shifters Z t and error term vt

Qtd  h( Pt, Z t, vt )

includes two variables indicating movements in demand curve, which are Zt time charter rate (TRATE) and price-cost margin (PCM). It is assumed that supply and demand of new tankers operate simultaneously to determine ship prices. A reduced form of price in equilibrium Qts  Qtd will relate price to determinants that influence both supply and demand Pt  f (CAPAt, Ct, CRATEt, TRATEt, PCM t, et )

3.2. The Price determinants

Shipbuilding capacity utilization (CAPA) Shipbuilding capacity is largely constrained by physical facilities. But under the same condition, the output may also be different when yards produce at different productivity levels and product mixes (Strandenes 1990). The reason is the shipbuilding capacity utilization varies rather than the shipbuilding capacity goes up and down. A low level of utilization rate means yards run at less than full capacity and hence shipbuilders have a weak pricing power. Conversely shipyards operate at almost full capacity and shipowners may have to pay higher prices for the limited berth. Utilization of the shipbuilding capacity is measured by deliveries relative to total capacity (Strandenes 1990). In this paper, the capacity utilization of the Chinese shipbuilding is calculated by the Chinese annual delivery relative to China’s total shipbuilding capacity.

Shipbuilding cost index (C) In terms of the traditional production theory, the producer will produce a positive amount as long as the price is at least equal to the average variable cost. If the prices are lower than that, then the producer will not produce at all (Wijnolst et al.1997). The main variable costs for building ships contain labour cost, steel cost and cost of equipment (including main engine). These three components account for 90% of total variable costs and it is within these components that the biggest difference will be found (Wijnolst et al.1997). A simple form of cost index for the Chinese shipbuilding can be expressed as follow, C  ERATE * (W1 *ULC  W2 * S )  W3 * E Where ULC is the unit labour cost index, S is the steel cost index, E is the cost index of equipment, ERATE is the RMB exchange rate against US dollar, and Wi (Wi  1, i  1,2,3) is the weight of respective cost. Most of labour and steel costs occur in the Chinese currency, while shipbuilding equipment has a high import content counted in US dollar. Therefore, we use ERATE to convert the domestic labour and steel prices into US dollar instead. The data availability prevented us from constructing cost for individual shipyard, and instead an integrated national level of Chinese shipbuilding cost is used. Among the cost components, variations of labour cost between shipbuilders will bring on a great difference in shipbuilding cost. It is mainly due to other components are available as products in the world market and technology in dry bulk carrier market is fairly equal (Wijnolst et al.1997). The main strength of the Chinese shipyards is the significantly low wage which provides the price advantage comparing to other shipbuilders. But the advantage of cheaper Chinese labour is partly offset by the low productivity compared to industry leaders such as Japan and South Korea (Lu 2005). For this reason, labour cost needs to be adjusted for productivity and the unit labour cost index (ULC ) which measures the labour cost per unit of output is as follow, Wage ULC  V / No.





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Where I is China’s annual import amount of shipbuilding equipment, r the loading rate of import equipment, and D the annual delivery of Chinese shipbuilding industry.

Ship export credit rate (CRATE) Export credit for ships is a major government support and provides Chinese shipyards with financing channels for newbuilding. It can take the form of interest rate support where the government provides a preferential rate for the life of credit. Clearly the availability of credit rate support will suppress the Chinese price.

Time charter rate (TRATE) The way time charter rate makes impact on the dry bulk carrier price is quite straight forward. The shipbuilding orders are determined by ship owner’s expectation of future earnings from new vessels. The higher the freight rate is, the more profitable for ship owners to operate the vessels, and the more ship owners are willing to place orders. It is customary to assume that time charter rate contains more information of future market comparing to the spot freight rate.

–  –  –

Where pi and ci represent average price index and marginal cost index of the Chinese dry bulk carrier respectively. si denotes the Chinese market share of dry bulk carrier in terms of new orders. In practice, the marginal cost is represented by the average variable cost and therefore cost index C will be adopted. The fiercer competition is reflected by the lower price-cost margin due to lower prices or smaller market share (Creusen 2006). If there is a low level of demand in dry bulk carrier market, then the strong competition with major shipbuilding nations may force Chinese yards to reduce prices until marginal costs.

In summary, hypotheses about the price determinants are presented below and five factors are assumed positively related to the price of Chinese dry bulk carrier.

Figure 1. Hypotheses of price determinants

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4. Data

4.1. Data Collection In this study, the Chinese dry bulk carrier prices are analyzed by referring to the Capesize (over 100,000 dwt), Panamax (60,000-100,000 dwt), Handymax (40,000-60,000 dwt), and Handysize (10,000-40,000 dwt) types. The sample period is from January 1995 to December 2009.

Prices of dry bulk carrier are collected from the Clarkson Shipping Intelligence Network (Clarkson SIN) for top 200 Chinese shipyards, which account for 98.63% of national total Compensated Gross Tonnage (CGT). Prices are reported for all new orders at contract time in million US dollar. Independent variables use the data in corresponding contract months. There are only few dry bulk carrier contracts in late 1990s, several between 2000 and 2005, and numerous between 2006 and 2009. Besides, small vessels started to be built much earlier than large ones due to gradual technology development. For the sake of effective analysis, we pool the data of all four types and distinguish the size effect by dummy variables. Other shipping and shipbuilding data are also collected from Clarkson SIN. Time charter rate is the monthly average rate (thousand $/day) for corresponding vessel type. Utilization of the shipbuilding capacity is measured by annual delivery of China (CGT) relative to yearly Chinese shipbuilding capacity. Shipbuilding capacity is calculated as the maximum annual output (CGT) in China since 1991 (according to the definition from Clarkson).

The data of average annual industrial wage (RMB / person per year), annual industry added value (RMB / year) and number of employee are especially for the Chinese manufacturing of transport equipment industry. These data are collected from yearbooks of the Chinese National Bureau of Statistics and Chinese Ministry of Labour and Social Security. Monthly price (RMB/ ton) of medium and heavy steel plate in China is collected from the MYSTEEL database. The annual import amount of shipbuilding equipment (US dollar) is collected from China Customs. Monthly exchange rate of RMB against US dollar and ship export credit rate are collected from the People’s Bank of China. In this study we assume labour, steel and equipment respectively account for 10%, 38% and 52% of average variable cost according to previous studies (Wijnolst et al.1997). These are rough data of the cost structure since they are heavily depend on ship type, ship size and more detailed design particulars, related to onboard trans-shipment facilities, cargo facilities an speed of the ship(Hopman and Nienhuis 2009).

Three dummy variables are used to control for four types. D1 equals to 1 if Capesize, D2 equals to 1 if Panamax, D3 equals to 1 if Handymax and others is Handysize. Prices and costs in RMB are deflated by the Chinese CPI (1995=100) and those in US dollar are deflated by the US CPI (1995=100).

There are 851 observations for each variable, but there is a limitation of our sample. 72% of the data concentrate on year 2006-2008 during which the market is clearly in a boom state. In contrast, only 16% of the data was in start decade and about 11% of sample stands for the recent period of post financial crisis

2008. This unbalanced data structure will limit the explanation power of our model, which will be improved in the future study.

According to the correlation matrix of variables, three of 10 coefficients are larger than 0.6 and one of them is larger than 0.9. This is consistent with the reality that many parameters in shipping sector are interdependent. The test results of VIF (larger than 10) and Tolerance values (close to 0) in multiple linear regression also suggest there is a multicollinearity problem. The Principal Component Regression approach will be used as an alternative way to model the price formation of dry bulk carrier.

5. Methodology5.1. Principal Component Analysis

When highly correlated predictors are used in a multiple linear regression, the model can face a serious multicollinearity problem. This high correlation or multicollinearity can be even serious when the goal is to understand how the various independent variables impact dependent variable. When high multicollinearity is present, confidence intervals for coefficients tend to be very wide and t-statistics tend to be very small. This can become the cause of incorrect rejection of variables and inaccuracies in computing the estimates of model coefficients (Jennrich 1995).



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