# The impact of shipyard and shipowner heterogeneity on contracting prices in the newbuilding market

Roar Adland (Department of Economics, Norwegian School of Economics, Bergen, Norway)
Kristian Norland (Department of Economics, Norwegian School of Economics, Bergen, Norway)
Even Sætrevik (Department of Economics, Norwegian School of Economics, Bergen, Norway)

ISSN: 2397-3757

Article publication date: 15 June 2017

## Abstract

### Purpose

The purpose of this paper is to investigate the impact of shipyard and shipowner heterogeneity on the price formation for individual newbuilding contracts.

### Design/methodology/approach

The model controls for the shipbuilding market cycle, input costs, firm size, yard experience and contract-specific variables and captures the impact of yard and owner heterogeneity in fixed-effects regressions. The data sample contains contract information on 3,759 tankers, bulkers and container vessels constructed at 77 shipyards between 1990 and 2014.

### 3.5 Omitted variables

Currency exchange rates affect the relative competitiveness of shipyards as nearly all shipbuilding contracts are quoted in US$, while shipbuilders have most of their costs in local currency (Wijnolst et al., 2009). However, currency effects will eventually materialize in current market prices, and so the newbuilding price indices should indirectly capture this element. #### 3.5.2 Government subsidies. Shipbuilding is a labour-intensive and export-oriented industry with spill-over effects on the domestic economy, making it the repeated beneficiary of various forms of government aid in pretty much any nation with shipbuilding output of some size (Strandenes, 2010). These subsidies are known to distort pricing mechanisms in the market, as the true production costs might not be covered by the shipyard (Jon, 2010). However, subsidies are hard to quantify, in part because not all aid is given in the form of direct monetary support. However, these effects will be at least partly captured by either shipyard fixed effects – for subsidies such as beneficial financing that remain relatively constant over time – or by the current competitive market prices for more short-term aid. ### 3.6 Econometric approach When analysing data with repeated interactions of entities, in our case shipyards and shipowners, fixed- or random-effects models are suitable. However, only fixed-effects models allow estimates to account for heterogeneity, as they adjust for time-invariant unobserved effects that vary across entities, and/or constant time-varying effects across entities (Wooldridge, 2013, p. 477). Let the dependent variable, the US$/CGT price for contract i, be denoted by Ui. Furthermore, Gi is a vessels’ gross tonnage, Di is the delivery time, Ei is a measure of yard experience, Yi,y is the dummy variable for shipyard size, Oi,o is the dummy variable for owner size and θk represents the coefficients for the size dummy variables. We first propose three purely microeconomic model specifications, starting with only firm size effects:

(3)U i =β 0 +β 1 G i +β 2 D i +β 3 E i + y θ k Y i,y + o θ k O i,o +ε i

In the second specification, we control for shipyard characteristics, excluding yard size, as this is rather constant and explained by yard fixed effects. The γ represents fixed-effect coefficients, while δ represents dummy variables generated for the respective yards and owners:

(4)U i =β 0 +β 1 G i +β 2 D i +β 3 E i + y γ y δ y + o θ k O i,o +ε i

In the third specification, we have a two-way fixed-effect model controlling for both shipyard and owner fixed effects:

(5)U i =β 0 +β 1 G i +β 2 D i +β 3 E i + y y y δ y + o γ o δ o +ε i

As we want to isolate firm fixed effects, we also introduce a second set of specifications that include macroeconomic variables to control for time-varying effects. First, we control for firm sizes [Equation (6)], second, we introduce shipyard fixed effects [Equation (7)] and thirdly, we include both yard and owner fixed effects [Equation (8)]. We here introduce an interaction variable between delivery time and freight rates, with τ being the coefficient and Fi representing dummies for low, medium or high freight rate level. Additionally, our macroeconomic specifications include It representing the competitive market price for average ships, St representing the steel price, Pt is the oil price and Gt is the shipyard nations’ GDP per capita:

(6)U i =β 0 +β 1 G i +β 2 D i +β 3 E i +β 4 I t +β 5 S t +β 6 P t +β 7 G t + f τ k F i,f + f τ k F i,f ×D i + y θ j Y i,y + o θ j O i,o +ε i
(7)U i =β 0 +β 1 G i + . . . +β 7 G t + f τ f F i,f + f τ f F i,f ×D i + y γ y δ y + o θ j O i,o +ε i
(8)U i =β 0 +β 1 G i + . . . +β 7 G t + f τ f F i,f + f τ f F i,f ×D i + y y y δ y + o γ o δ o +ε i

We do not look at specific shipyard–owner relationships (match effects), as repeated transactions between a yard and an owner occur very rarely in our sample and is unlikely to cause distinct effects. All regressions are done with a clustered sandwich estimator[1] to correct standard errors in the presence of heteroscedasticity (Cameron and Miller, 2015).

## 4. Data

Our data sample contains newbuilding contracts extracted from Clarksons (2016a), covering vessels built in the period between 1970 and 2014 that are still trading. We note that while the database contains some newbuilding contract information on 91,112 vessels, only 7,604 observations included a usable contract price. The observations contain information regarding vessel name, contract parties, contract and delivery dates, carrying capacity [deadweight (DWT) and GT], CGT and vessel type. Information specific to the contract parties is also included, such as name, size of shipyard or shipowner and year of first delivery from the shipyard. These variables enable us to calculate delivery times, yard experience, contract price in US$per CGT, in addition to creating firm size dummies. Contracts stated in currencies other than US$ are converted based on exchange rates at the time of contract signing. We inflate prices to 2014 values by using the US consumer price index (CPI). Although the USA is not a major player in either shipping or shipbuilding, its domestic inflation measure appears to be the most widely used to obtain real prices in similar studies (Jiang et al., 2013; Akram, 2009; Lizardo and Mollick, 2010).

We remove observations with missing data for at least one of our variables. Furthermore, we remove all vessel types other than tankers, bulkers and container vessels (FCCs) – for two reasons. First, the remaining three vessel types all have a large number of observations. Second, they have relatively low standard deviation of US$/CGT values, reflecting a high degree of standardization. Also, specialized vessels, such as offshore service vessels, will show a greater variation in equipment levels. As CGT does not explicitly account for variations in technical specifications within a vessel category, our model will be less suitable in these cases. The final data set includes 3,759 observations for vessels built between 1990 and 2014, constructed for 835 different shipowners by 77 different shipyards located in 11 countries. We acknowledge that the low sample size relative to the true population may distort the outcome of our analysis, for instance, if certain types of contracts are more likely to have their value publicly known. Furthermore, the database only includes vessels in the current fleet, and one could imagine that vessels from certain (poor) yards are more inclined to be scrapped early. As an indication of whether our sample is representative, Figure 1 compares the US$/CGT annual average values observed in our data set to the price indices reported by Clarksons (2016b). The observation that the two move closely in tandem gives us confidence that our data sample provides a satisfactory representation of the shipbuilding market. The larger spread in values found in the data set is expected, simply because there is one observation per vessel – as opposed to one per month for the index.

Macroeconomic data were collected from several sources. Clarksons (2016b) provided newbuilding price indices and freight rates. Oil prices were extracted from the International Monetary Fund’s (2016) commodity database, while steel prices were collected through the World Bank Commodity Database (2016a) supplemented with missing data points from Bloomberg (2016). Currency exchange rates and GDP per capita were obtained from the World Bank WDI database (2016b). All macroeconomic price variables were inflated in line with contract prices.

Presenting data for each individual shipowner and shipyard is impractical due to the large number of firms. Therefore, Table II presents yard experience and delivery times by country rather than by yard. This aggregation seems reasonable because individual yards within a nation are likely to have emerged in the same period and share other common traits (Stopford, 2009). Yard experience appears affected by established shipyards appearing frequently throughout our sample period, as there is one observation per newbuilding contract. Chinese shipyards exhibit the greatest spread in terms of experience, as expected due to the industry’s relatively recent emergence in the country. We observe outliers in delivery times, as a ship rarely arrives as early as four months, or as late as 96 months, after contract signing. However, none of the values are implausible, and could be caused by the state of the shipping market and size of the orderbook (Adland and Jia, 2015; Adland et al., 2006).

Table III reports owner and builder frequency for the ten largest players, with the remainder grouped as “other”. Market concentration among the top ten shipbuilders varies across segments, from a 42.4 per cent market share for bulkers, to 79.8 per cent for tankers and 84.5 per cent for FCCs. This reflects the lower technological complexity of bulk vessels, and the generally larger size of tankers and FCCs – presumably favouring established shipbuilders of some size (Stopford, 2009). In terms of shipowner concentration, we find that the top ten owners represent just above 20 per cent for tankers and bulkers and 39.5 per cent of the market for FCCs. COSCO, Maersk and China Shipping are the most frequent owners in the data set as a whole, which seems reasonable, as they are among the world’s largest ship operators across segments (Fan et al., 2011).

Table IV presents the descriptive statistics for our macroeconomic variables. We note the impact of the shipping and commodity supercycle in the middle of the last decade, which has caused great volatility in our sample.

## 5. Empirical results

### 5.1 Testing for multicollinearity

The Variance Inflation Factor (VIF) test is a common tool to examine the risk of multicollinearity. A VIF value of 10 is usually set as a warning level, above which determinants are said to carry risk of multicollinearity in regression estimates, though such a cut-off value is necessarily arbitrary (Wooldridge, 2013, p. 94). In Table V, the only variables displaying high VIF values are the interaction variables and freight rates. Freight rates appear both in the interaction variable and separately as control variables, resulting in four values surpassing the threshold value. However, it is generally accepted that high VIF values for variables constituting an interaction variable safely can be ignored (Allison, 2012). No other values even remotely close to 10 are observed and, thus, we conclude that the remainder of determinants show no risk of multicollinearity.

### 5.2 Microeconomic regressions

Table VI shows the regression results for our set of microeconomic specifications, grouped by bulk carriers, FCCs and tankers to isolate segment-specific effects. For firm sizes, we use mega yards and medium owners as base levels to test our hypotheses. Specifications without fixed effects are estimated using ordinary least squares (OLS). One should keep in mind when assessing results that these estimates do not account for time effects or current market conditions.

The basic OLS specifications with only firm- and contract-specific variables result in an R2 of only 17-20 per cent across segments, but this increases to 34-42 per cent once we add shipyard fixed effects, and as much as 60-78 per cent once we add both shipyard and shipowner fixed effects. This indicates that time-invariant unobservable and observable characteristics of both yards and, especially, owners are important in determining contract prices. Heterogeneity across yards could, for instance, be related to specialization premiums, bargaining power or superior ship designs. For owners, it could reflect differing market timing ability, where some owners look at the newbuilding market as a source of asset play profits, while others take a more strategic long-term view of fleet renewal.

There is a positive and highly significant effect of delivery time across segments and specifications. While this is contrary to our expectations, this can here be explained by the fact that we do not account separately for the market cycle. Consequently, as delivery time and market prices are positively correlated (Adland et al., 2006; Adland and Jia, 2015), what this variable picks up is simply the effect of higher newbuilding demand increasing prices and filling up the orderbook, thus lengthening delivery times. The impact of design speed, where significant, is positive as expected. The impact of vessel size (GT) is positive and highly significant for tankers, in accordance with our expectations, but negative for container vessels. This could well be an artefact related to the different impact of vessel size (GT) in the CGT calculations (ref. Table A1 in the Appendix) for the various vessel types.

We note that shipyards of all sizes obtain lower prices on a US$/CGT basis than the mega yards, though apart from this observation the trend with regards to yard size is not clear. This indicates the possible presence of a two-tier market where the behemoth yard groups in the Far East have a degree of pricing power. While an alternative explanation could be that these yards are capable of building vessels towards the more sophisticated end of the spectrum for each vessel type, and that such effects are not properly captured by our US$/CGT price measure, we would expect this to be picked up in part by our vessel size (GT) and speed variables.

Broadly speaking, shipyard experience has the predicted positive impact on prices in the tanker segment, negative for bulkers and mixed for FCC (depending on whether fixed yard effects are included). Keeping in mind that the market cycle is not accounted for in this set of specifications, and knowing that a lot of the new greenfield yards in China were set up to build basic bulkers during the 2003-2008 shipping boom, we suggest that this relationship is simply reflecting the combination of high bulker contracting prices and low/no experience of certain yards at the time. This explanation may also hold for parts of the FCC segment.

In Table VII, our second set of model specifications adds macroeconomic variables, including an interaction variable between freight rates and delivery time that is assessing the impact of high/low rate deviations. Discussions of the two variables separately are irrelevant, as they appear merely to avoid specification errors when we introduce the interaction variable.

The key difference when including benchmark newbuilding prices in the regression is that even the basic OLS specification, with firm size effects only, has a relatively high explanatory power ranging from 55 per cent for tankers to 74 per cent for bulkers. Adding yard fixed effects increases the R2 to the 70-80 per cent range, increasing further to 82-95 per cent for both yard and owner fixed effects. Again, therefore, fixed yard and owner effects play a considerable role in the price formation for individual newbuilding contracts. Moreover, owner heterogeneity remains more important than yard heterogeneity in the explanation of contract price variation.

As expected, both the price benchmark and GDP/capita (as a proxy for wages) show a positive and highly significant relationship with the US$/CGT pricing across specifications and market segments. The coefficients for steel prices, where significant, are also positive as expected. The statistically significant estimates for yard experience are now positive across segments, which supports our earlier explanation that the observed negative values for the microeconomic specifications were caused primarily by a failure to account for the addition of new yard capacity at high price levels. The impact of delivery time is now negative for tankers, as predicted by theory, though still positive for container vessels. We can only speculate that in a market with oligopolistic competition such as the container market, there may be a strategic value of occupying newbuilding slots further out in time in strong markets to prevent competitors from gaining market share. Where significant, all yard sizes display negative price deviations when compared to mega yards, confirming the earlier findings. However, there are no other clear trends in contracting prices with regards to neither yard size nor owner size. The remainder of our variables either show similar behaviour as in the microeconomic regression (speed and GT) or show mixed results with regards to signs across specifications, making any conclusions difficult. ## 6. Concluding remarks In this paper we have proposed and estimated a new model for price formation in the newbuilding market where shipowner and shipyard heterogeneity is accounted for. Using data on individual contracts in the tanker, bulker and container segments, our empirical methodology relies on the estimation of fixed-effect models. Although the newbuilding price benchmarks (market conditions) and GDP/capita (salary costs) are influential covariates, our main conclusion is that shipyards and, particularly, shipowners play an influential role on the US$/CGT price level in individual contracts. Additionally, steel prices and yard experience have a positive impact on prices. Delivery times have the expected negative impact on prices for tankers, but positive for container vessels, which we suggest relate to the possible strategic value of newbuilding slots in an oligopolistic market.

From a practical point of view, our proposed model and methodology can in principle be used to benchmark the performance of individual yards and owners in negotiations for newbuilding contracts. Similarly, knowledge about the effect of yard identity, size and experience on contracting prices can assist shipowners in the selection of which yards to approach.

We acknowledge that the lack of observable contract prices for such a large share of the trading world fleet means we are analysing only a relatively small sample and cannot be sure of unbiasedness. Another limitation of our study is that our empirical framework is dedicated to the measurement of time-invariant owner and yard effects rather than to their explanation. There are also limitations in our data that are difficult to rectify, for instance, the fact that only relatively few contracts per year have a known value.

While shipbuilding productivity almost certainly has increased over the course of our sample, we note that the impact on prices of such macro effects is embedded in our market price index, which reflects the US$/CGT price of a standard vessel built at a “first class” yard at the time. As such, this variable incorporates both an element of technological change and the “size creep” that we can observe in the fleet. Future research should focus on developing the ideas presented here further, particularly in identifying variables that can shed further light on the sources of price variation due to yard and owner heterogeneity. ## Figures #### Figure 1. Yearly means of contract prices: Bulk, FCC and Tanker markets ## Table I. Predicted effects on US$/CGT from explanatory variables

Variable Unit Predicted effect on US$/CGT price Firm-specific variables Shipowner size Dummy Negative Shipyard size Dummy Negative Shipyard experience Years Positive Contract-specific variables Delivery time Months Negative Top speed Knots Positive Gross tonnage m3 Positive Macroeconomic variables Delivery time * Freight rate interaction Negative Freight rate US$/day Positive
Newbuilding price index US$/CGT Positive GDP per capita US$ Positive
Steel price US$/tonne Positive Oil price US$/bbl Positive

## Table II.

Shipyard experience and delivery time (pr. shipyard nation)

Country Observation No. of yards Yard experience (years) Delivery times (months)
Average Min Max SD Average Min Max SD
China P.R. 1,319 37 13.2 −5 41 13.3 35.0 5.3 95.9 13.4
Croatia 50 2 43.0 36 49 4.0 35.0 19.2 53.8 7.8
Germany 20 1 34.6 32 38 1.8 17.9 11.5 30.1 4.4
Japan 332 12 29.8 19 45 6.1 25.7 7.9 81.9 9.4
Philippines 30 1 1.8 −2 5 2.4 28.1 15.7 40.0 6.6
Poland 56 3 31.7 28 40 2.9 28.6 8.0 63.3 10.9
Romania 21 1 8.0 6 10 1.3 39.1 24.4 52.0 8.1
South Korea 1,789 16 19.3 −3 43 11.1 29.8 3.7 70.8 10.2
Spain 14 1 29.3 19 33 4.5 26.3 18.3 43.2 6.9
Taiwan 100 2 25.1 17 36 5.8 29.5 15.6 88.7 14.3
Vietnam 28 1 0.3 −2 4 2.2 28.4 13.6 57.8 11.5
Grand Total 3,759 77 18.5 −5 49 12.9 31.2 3.7 95.9 11.9

## Table III.

Top ten shipyards and owners in the data sample

Builder yard Contracts (%) Cumulative Owner (buyer) Contracts (%) Cumulative
A. Bulk market (1,354 contracts)
Jiangnan SY Group 75 5.54 5.54 COSCO Group 56 4.14 4.14
Sinopacific 71 5.24 10.78 China Shipping (H.K.) 54 3.99 8.12
Hyundai HI 66 4.87 15.66 Pan Ocean 29 2.14 10.27
STX SB 64 4.73 20.38 Eagle Bulk Shipping 27 1.99 12.26
Oshima SB Co 64 4.73 25.11 Genco Shipping and Trading 22 1.62 13.88
Shanghai Waigaoqiao 55 4.06 29.17 Grieg Star 20 1.48 15.36
CSC Jinling Shipyard 48 3.55 32.72 U.Ming Marine Transport 18 1.33 16.69
Hyundai Mipo 45 3.32 36.04 Sino Shipping Group 18 1.33 18.02
Hudong Zhonghua 43 3.18 39.22 Jinhui Shipping and Transportation 18 1.33 19.35
Sungdong SB 43 3.18 42.39 Dryships 17 1.26 20.61
Other 780 57.61 100.00 Other 1,075 79.39 100.00
Total 1,354 100.00 1,354 100.00
B. FCC market (1,239 contracts)
Hyundai HI 287 23.16 23.16 Maersk Company 91 7.34 7.34
Samsung HI 189 15.25 38.42 COSCO Group 60 4.84 12.19
Daewoo (DSME) 154 12.43 50.85 CSC Group 58 4.68 16.87
CSBC Group 74 5.97 56.82 MSC 47 3.79 20.66
Hanjin HI (Yeongdo) 70 5.65 62.47 OOCL 45 3.63 24.29
Dalian Shipbuilding 54 4.36 66.83 CMA.CGM 41 3.31 27.60
Jiangsu New YZJ 48 3.87 70.70 APL 41 3.31 30.91
Hyundai Mipo 41 3.31 74.01 Seaspan Corporation 40 3.23 34.14
Shanghai Shipyard 39 3.15 77.16 Rickmers Reederei 33 2.66 36.80
Jiangnan SY Group 33 2.66 79.82 Hapag.Lloyd Cont 33 2.66 39.47
Other 250 20.18 100.00 Other 750 60.53 100.00
Total 1,239 100.00 1,239 100.00
C. Tanker market (1,166 contracts)
Hyundai HI 197 16.90 16.90 China Shipping (H.K.) 49 4.20 4.20
Hyundai Mipo 154 13.21 30.10 Scorpio Group 45 3.86 8.06
Samsung HI 143 12.26 42.37 Teekay Tankers 38 3.26 11.32
Daewoo (DSME) 121 10.38 52.74 Bahri 31 2.66 13.98
Dalian Shipbuilding 108 9.26 62.01 Nat Iranian Tanker 25 2.14 16.12
STX SB 91 7.80 69.81 TORM A/S 24 2.06 18.18
Guangzhou SY Intl 72 6.17 75.99 AET Tanker 23 1.97 20.15
SPP Sacheon SY 38 3.26 79.25 Maersk Company 21 1.80 21.96
Shanghai Waigaoqiao 33 2.83 82.08 BW Maritime 21 1.80 23.76
Brodosplit 28 2.40 84.48 Minerva Ship Mngt 20 1.72 25.47
Other 181 15.52 100.00 Other 869 74.53 100.00
Total 1,166 100.00 Total 1,166 100.00

## Table IV.

Descriptive statistics of macroeconomic variables

Macroeconomic determinants N Mean SD Minimum Maximum
Oil price 3,759 62.7 32.3 15.1 145.7
Steel 3,759 620.7 172.6 263.2 1,099.6
Freight rates (US$/day) Bulk carrier 1,354 28,334.9 18,970.7 6,857.2 73,759.4 FCC 1,239 19,445.4 7,719.0 5,097.3 34,710.4 Tanker 1,166 33,605.5 16,009.2 6,909.3 99,832.9 Market prices (US$/CGT)
Bulk carrier 1,354 2,452.7 577.3 1,522.4 3,513.3
FCC 1,239 2,472.0 501.7 1,482.0 4,552.2
Tanker 1,166 2,649.1 464.8 1,903.3 3,715.1

## Table V.

Variance inflation factor (VIF) test of multicollinearity

Determinants VIF
GT 1.580
Speed 1.480
Delivery time 3.160
Low freight rate 14.15
High freight rates 13.63
Delivery time × Low freight rates 12.62
Delivery time × High freight rates 15.03
Yard experience 1.280
Market price 3.300
Steel price 4.510
Oil price 2.600
GDP per capita 1.230
Yard size
Large 1.120
Medium 1.190
Small 1.280
Very small 1.140
Unknown 1.370
Owner size
Extra large 1.180
Very large 1.590
Large 1.990
Small 1.520
Very small 1.390
Unknown 1.060

## Table VI.

Microeconomic regression results

Regression # 1 2 3 4 5 6 7 8 9
Group Bulk Bulk Bulk FCC FCC FCC Tanker Tanker Tanker
Dependent variables US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT Constant 354.7 (438.1) 1,357*** (423.0) 1,669*** (563.2) 1,296*** (184.4) 1,574*** (193.7) 1,755*** (339.9) 1,545*** (266.5) 877.1*** (316.2) 560.0 (419.8) GT 0.000143 (0.000483) 0.000120 (0.000611) 0.00178* (0.00102) −0.00364*** (0.000317) −0.00345*** (0.000365) −0.00304*** (0.000429) 0.00430*** (0.000399) 0.00292*** (0.000541) 0.00205*** (0.000681) Speed 107.4*** (31.10) 35.88 (29.11) −2.160 (38.92) 40.99*** (7.742) 17.57** (7.706) 25.64** (9.977) 7.575 (17.78) 3.916 (19.59) 31.15 (30.42) Delivery time 16.29*** (1.242) 14.66*** (1.343) 8.910*** (2.021) 15.16*** (1.101) 19.61*** (1.128) 18.10*** (1.159) 10.67*** (1.531) 16.12*** (1.772) 12.24*** (2.945) Experience −1.076 (1.189) −13.18*** (2.858) −7.598* (4.567) 3.180*** (1.200) −6.689*** (2.494) −2.515 (2.773) 5.148*** (1.224) 8.114*** (2.163) 6.725** (3.295) L-Yard −93.64** (37.00) −132.1** (61.25) −58.90 (65.66) M-Yard −91.69** (42.98) −135.7*** (49.45) −51.20 (49.69) S-Yard −82.43 (59.56) −137.5** (65.42) −262.4*** (76.53) VS-Yard 53.06 (116.7) −656.3*** (94.42) −113.9 (112.2) XL-Owner 80.20 (115.9) 7.060 (116.4) 176.8*** (50.13) 177.8*** (44.65) 150.5* (79.50) 170.8** (79.26) VL-Owner 112.6** (43.67) 67.41 (44.22) 85.43** (42.37) 124.7*** (37.06) −20.29 (46.84) −26.54 (46.98) L-Owner −36.38 (39.68) −40.52 (40.76) 86.99* (48.79) 125.3*** (44.96) 77.70 (47.97) 66.53 (45.24) S-Owner −130.9*** (46.26) −140.9*** (48.12) 131.2* (69.60) 135.8** (67.81) 103.6** (49.22) 168.3*** (49.44) VS-Owner 93.26** (46.07) 20.25 (47.66) 184.6* (94.39) 99.81 (76.34) 108.3* (61.52) 137.9** (61.77) Yard FE NO YES YES NO YES YES NO YES YES Owner FE NO NO YES NO NO YES NO NO YES Observations 1,354 1,354 1,354 1,239 1,239 1,239 1,166 1,166 1,166 R-squared 0.179 0.339 0.779 0.171 0.418 0.597 0.202 0.407 0.721 Notes: Robust standard errors in parentheses; * statistically significant at a 10% level; ** statistically significant at a 5% level; *** statistically significant at a 1% level; XL = extra large, VL = very large, L = large, M = medium, S = small, VS = very small ## Table VII. Macroeconomic regression results Regression # 1 2 3 4 5 6 7 8 9 Group Bulk Bulk Bulk FCC FCC FCC Tanker Tanker Tanker Dependent variables US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT US$/CGT
Constant −1,671*** (325.5) −921.2*** (335.9) 152.0 (328.5) −13.09 (153.8) −361.3** (163.9) −365.9 (249.2) −412.9* (228.6) −11.86 (242.8) −170.5 (325.8)
GT −0.000275 (0.000290) −0.000653* (0.000365) −0.000249 (0.000477) −0.000616*** (0.000227) −0.00104*** (0.000263) −0.000995*** (0.000296) 0.00347*** (0.000271) 0.00177*** (0.000314) 0.00144*** (0.000431)
Speed 97.61*** (23.09) 52.07** (23.09) −5.723 (20.21) 22.81*** (4.892) 20.01*** (6.175) 29.34*** (8.218) 33.51** (13.63) 9.051 (13.85) 9.820 (22.19)
Delivery time −0.452 (1.233) 1.043 (1.352) −0.404 (2.144) 3.771*** (0.990) 4.922*** (1.043) 4.940*** (1.146) −4.639** (2.167) −3.058* (1.814) −5.973*** (2.187)
L-Rates −84.42 (86.94) −56.51 (82.26) 52.43 (103.7) −166.0** (73.92) −217.2*** (80.42) −309.1*** (90.55) −154.7* (92.01) −157.2* (86.77) −262.8*** (100.4)
H-Rates −32.59 (70.72) 16.96 (73.50) −25.92 (92.87) −107.5 (83.32) −123.6 (83.02) −73.43 (86.53) −486.3*** (86.08) −396.3*** (81.44) −402.9*** (112.3)
L-Rates*Delivery time 7.781** (3.254) 4.863* (2.931) 0.461 (3.254) 0.628 (2.391) 3.326 (2.650) 7.804** (3.171) 3.118 (3.029) 2.434 (3.101) 9.307*** (3.535)
H-Rates*Delivery time −3.061* (1.680) −4.396** (1.739) −3.195 (2.460) 3.658* (1.944) 3.776* (2.002) 3.065 (2.073) 10.66*** (2.609) 8.085*** (2.442) 10.39*** (3.553)
Experience 4.246*** (0.672) −0.110 (1.456) −1.365 (1.751) 1.069 (0.824) 3.376** (1.583) 0.965 (1.689) 9.016*** (0.986) 1.598 (1.290) 2.403 (1.931)
Index 0.969*** (0.0336) 0.930*** (0.0341) 0.971*** (0.0511) 0.668*** (0.0419) 0.668*** (0.0402) 0.662*** (0.0460) 0.805*** (0.0450) 0.735*** (0.0458) 0.825*** (0.0657)
Steel price 0.390*** (0.101) 0.264** (0.106) −0.0786 (0.187) 0.392** (0.155) 0.220 (0.164) 0.263 (0.193) 0.357** (0.149) 0.0182 (0.135) −0.00750 (0.202)
Oil price −1.097** (0.476) −0.799 (0.582) 0.678 (0.968) −0.403 (0.845) −0.743 (0.915) −0.230 (1.056) −4.719*** (0.812) −1.422* (0.787) −0.943 (1.228)
GDP/capita 0.00305*** (0.000573) 0.00839*** (0.00176) 0.0106*** (0.00250) 0.00760*** (0.000929) 0.0176*** (0.00285) 0.0159*** (0.00354) 0.00231** (0.000959) 0.0290*** (0.00398) 0.0203*** (0.00614)
L-Yard −38.24* (20.06) −89.89*** (31.63) 11.87 (45.59)
M-Yard −6.475 (26.61) −52.42* (31.56) −44.02 (36.68)
S-Yard 12.75 (36.70) −71.32* (41.25) −193.2*** (72.62)
VS-Yard −90.80** (38.13) −303.6*** (68.63) 46.02 (69.27)
XL-Owner −30.64 (62.55) −8.469 (62.83) 82.11*** (26.84) 113.4*** (26.79) 79.86 (53.23) 27.53 (54.15)
VL-Owner 29.70 (22.53) 34.38 (21.76) 56.12** (24.32) 61.34** (23.98) −51.71 (34.85) −53.59 (34.53)
L-Owner 25.93 (21.88) 34.85* (20.39) 55.96* (30.34) 100.3*** (32.25) 64.42* (36.77) 52.01 (32.90)
S-Owner −7.228 (23.47) 8.805 (24.27) 64.84* (33.61) 111.4*** (33.21) −25.75 (38.44) 12.40 (36.95)
VS-Owner 53.01** (26.52) 39.48 (28.60) −35.30 (69.39) −56.77 (60.78) −42.83 (50.85) −41.80 (45.50)
Yard FE NO YES YES NO YES YES NO YES YES
Owner FE NO NO YES NO NO YES NO NO YES
Observations 1,354 1,354 1,354 1,239 1,239 1,239 1,166 1,166 1,166
R-squared 0.739 0.795 0.946 0.715 0.754 0.825 0.554 0.703 0.876
Notes:

Robust standard errors in parentheses;

*

statistically significant at a 10 per cent level;

**

statistically significant at a 5 per cent level;

***

statistically significant at a 1 per cent level; XL = extra large, VL = very large, L = large, M = medium, S = small, VS = very small

## Table A1.

CGT coefficients by vessel type

Ship type A B
Oil tankers 48 0.57
Chemical tankers 84 0.55
Bulk carriers 29 0.61
Combined carriers 33 0.62
General cargo ships 27 0.64
Reefers 27 0.68
Full container carrier 19 0.68
RoRo vessels 32 0.63
Car carriers 15 0.70
LPG carriers 62 0.57
LNG carriers 32 0.68
Ferries 20 0.71
Passenger ships 49 0.67
Fishing vessels 24 0.71
NCCV 46 0.62

Source: OECD (2007)

## Note

1.

Huber-White Sandwich estimator in STATA.

Table AI

## References

Adland, R. and Jia, H. (2015), “Shipping market integration: the case of sticky newbuilding prices”, Maritime Economics and Logistics, Vol. 17 No. 4, pp. 389-398.

Adland, R., Cariou, P. and Wolff, F.-C. (2016a), “The influence of charterers and owners on bulk shipping freight rates”, Transportation Research Part E, Vol. 86, pp. 69-82.

Adland, R., Jia, H. and Strandenes, S. (2006), “Asset bubbles in shipping? An analysis of recent history in the drybulk market”, Maritime Economics and Logistics, Vol. 8 No. 3, pp. 223-233.

Adland, R., Alger, H., Banyte, J. and Jia, H. (2016b), “Does fuel efficiency pay? Empirical evidence from the drybulk timecharter market revisited”, Transportation Research Part A, Vol. 95, pp. 1-12.

Agnolucci, P., Smith, T. and Rehmatulla, N. (2014), “Energy efficiency and time charter rates: energy efficiency savings recovered by ship owners in the Panamax market”, Transportation Research Part A, Vol. 66, pp. 173-184.

Akram, Q.F. (2009), “Commodity prices, interest rates and the dollar”, Energy Economics, Vol. 31 No. 6, pp. 838-851.

Alizadeh A.H. and Talley, W.K (2011a), “Vessel and voyage determinants of tanker freight rates and contract times”, Transport Policy, Vol. 18 No. 5, pp. 665-675.

Alizadeh A.H. and Talley, W.K. (2011b), “Microeconomic determinants of dry bulk shipping freight rates and contract times”, Transportation, Vol. 38 No. 3, pp. 561-579.

Alizadeh, A. and Nomikos, N.K. (2009), Shipping Derivatives and Risk Management, Palgrave Macmillan, London.

Allison, P. (2012), “When can you safely ignore multicollinearity”, Statistical Horizons, 10 September.

Beenstock, M. (1985), “A theory of ship prices”, Maritime Policy and Management, Vol. 12 No. 3, pp. 215-225.

Beenstock, M. and Vergottis, A. (1989a), “An econometric model of the world market for dry cargo freight and shipping”, Applied Economics, Vol. 21 No. 3, pp. 339-356.

Beenstock, M. and Vergottis, A. (1989b), “An econometric model of the world tanker market”, Journal of Transport Economics and Policy, Vol. 23 No. 3, pp. 263-280.

Bertram, V. (2003), “Strategic control of productivity and other competitiveness parameters”, Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, Vol. 217 No. 2, pp. 61-70.

Bloomberg (2016), Historical Prices for HRC1 (Hot Rolled Coilsheet).

Cameron, A.C. and Miller, D.L. (2015), “A practitioner’s guide to cluster-robust inference”, Journal of Human Resources, Vol. 50 No. 2, pp. 317-372.

Clarkson Research (2016), World Shipyard Monitor.

Clarksons (2016a), available at: http://wfr.clarksons.net/ (accessed 10 February 2016).

Clarksons (2016b), available at: https://sin.clarksons.net (accessed 1 April 2016).

Dikos, G. (2004), “New building prices: demand inelastic or perfectly competitive?”, Maritime Economics and Logistics, Vol. 6 No. 4, pp. 312-321.

Fan, L., Koehler, M.M. and Wilson, W.W. (2011), “Intermodalism and new trade flows”, The Blackwell Companion to Maritime Economics, pp. 121-137.

Haddal, R. and Knudsen, K. (1996), “One shipbuilding market?”, SNF-Working Paper 30/96, SNF, Bergen.

International Labour Organization (ILO) (2008), Global Wage Report 2008/09, International Labour Organization.

International Monetary Fund (IMF) (2016), IMF-Primary Commodity Prices, The International Monetary Fund, available at: www.imf.org/external/np/res/commod/External_Data.xls (accessed 1 April 2016).

Jiang, L. and Strandenes, S.P. (2011), Assessing the Cost Competitiveness of China’s Shipbuilding Industry, Syddansk Universitet, Institut for Miljø- og Erhvervsøkonomi.

Jiang, L., Bastiansen, E. and Strandenes, S.P. (2013), “The international competitiveness of China’s shipbuilding industry”, Transportation Research Part E, Vol. 60, pp. 39-48.

Jon, J.S. (2010), “Government policies and the shipbuilding industry”, in Grammenos, C. (Ed.), The Handbook of Maritime Economics and Business, 2nd ed., Lloyd’s List, London.

Köhn, S. and Thanopoulou, H. (2011), “A GAM assessment of quality Premia in the drybulk timecharter market”, Transportation Research Part E, Vol. 47 No. 5, pp. 709-721.

Lizardo, R.A. and Mollick, A.V. (2010), “Oil price fluctuations and US dollar exchange rates”, Energy Economics, Vol. 32 No. 2, pp. 399-408.

Mokia, Z. and Dinwoodie, J. (2002), “Spatial aspects of tanker lay-times”, Journal of Transport Geography, Vol. 10 No. 1, pp. 39-49.

Organisation for Economic Co-operation and Development (OECD) (2007), Compensated Gross Ton (CGT) System, OECD Directorate for Science Technology and Industry.

Porter, M.E. (1979), “How competitive forces shape strategy”, Harvard Business Review, Vol. 57 No. 2, pp. 137-145.

Rodrik, D. (1999), “Democracies pay higher wages”, Quarterly Journal of Economics, Vol. 114 No. 3, pp. 707-738.

Sauerhoff, C. (2014), Competitive Differentiation Within the Shipbuilding Industry: The Importance of Competence in the Field of Services, Springer Gabler, Verlag.

Stopford, M. (2009), Maritime Economics, Routledge, London.

Stott, P. (1995), “Marketing strategy for merchant shipbuilders”, Journal of Ship Production, Vol. 11 No. 3, pp. 149-158.

Strandenes, S. (1984), “Price determination in the time-charter and second-hand markets”, Center for Applied Research, Norwegian School of Economics and Business Administration, Working Paper MU, 6.

Strandenes, S.P. (1986), NORSHIP: A Simulation Model for Bulk Shipping Markets, Norwegian School of Economics and Business Administration.

Strandenes, S. (2010), “Economics of the markets for ships”, in Grammenos, C.T. (Ed.), The Handbook of Maritime Economics and Business, 2nd ed., Lloyd’s List, London.

Tamvakis, M.N. (1995), “An investigation into the existence of a two-tier spot freight market for crude oil tankers”, Maritime Policy and Management, Vol. 22 No. 1, pp. 81-90.

Tamvakis, M.N. and Thanopoulou, H.A. (2000), “Does quality pay? The case of the dry bulk market”, Transportation Research Part E, Vol. 36 No. 4, pp. 297-307.

Tsolakis, S., Cridland, C. and Haralambides, H. (2003), “Econometric modelling of second-hand ship prices”, Maritime Economics and Logistics, Vol. 5 No. 4, pp. 347-377.

Wijnolst, N., Wergeland, T. and Levander, K. (2009), Shipping Innovation, IOS Press, Amsterdam.

Wooldridge, J.M. (2013), Introductory Econometrics: A Modern Approach, South-Western Cengage Learning, Mason, OH.

World Bank (2016a), Commodity Markets, World Bank, (accessed 1 April 2016).

World Bank (2016b), World Developments Indicator Database, World Bank, (accessed 1 April 2016).

Zannetos, Z.S. (1966), The Theory of Oil Tankship Rates: An Economic Analysis of Tankship Operations, MIT Press, Cambridge, MA.