number 2
Logistics Cost Dynamics in International Business: A Causal Approach
Ashutosh Kar1 and Pratyay Ranjan Datta1
Abstract
The cost of logistics plays a vital role in the pricing of goods in international trade. Besides, the recent imposition of additional tariff by even upper-middle income countries such as the USA, China etc., has led to an increase in the total landed cost of goods. However, a seller has no option but to adapt to changing tariff requirements and can articulate only the logistics cost to a certain extent. This aspect requires an understanding of the logistics cost dynamics in international business. Since a higher volume of goods moves by marine transportation, this study focusses on the same. In this article, authors have attempted to establish a statistically significant relationship between prices and other factors like fuel, number of vessels, freight, and weight value ratio. The paper introduces a logistics- coefficient to indicate the extent of integration of logistics activities to keep the total-landed-cost (TLC) unchanged. Finally, the author proposes the system dynamics model to study the impact of changes in any one or some or all these factors on the price of the product. This model will enable the global firm to decide the entry and exit in the market.
JEL Codes: F23
Keywords
International business, pricing, logistics cost, logistics coefficient, system dynamics
Article
1 Assistant Professor, NSHM College of Management and Technology, Kolkata, India.
Corresponding author: Ashutosh Kar, Assistant Professor, NSHM College of Management and Technology, Kolkata 700053, West Bengal, India. E-mail: ashukar1@gmail.com
Foreign Trade Review 55(4) 478–495, 2020
© 2020 Indian Institute of Foreign Trade
Reprints and permissions: in.sagepub.com/journals-permissions-india
DOI: 10.1177/0015732520947861 journals.sagepub.com/home/ftr
Kar and Datta 479
Introduction and Conceptual Background
Price of a product is dependent on internal and external factors. Internal factors like marketing objectives, marketing strategy, costs, whereas external factors are elasticity of demand, customer expectations, competitive and other products and government regulations. In case of international trade, the cost of logistics plays an important role in pricing of goods (Gani, 2017). The extent of impact is likely to vary with type of goods.
The theories of trade suggests that trade is based on principles of comparative advantage (Ricardo, 1817), demand & supply (Mill, 1899), factors of endowment (Heckscher-Ohlin Theory (HO Model), 1933), distance between seller and buyer (Gravity Model by Tinbergen, 1962) and consumer preference (New Trade Theory by Krugman, 1996). Armington’s trade theory (1969, 2003, and 2004) state that non-trade cost plays an important role in trade between two countries. He identi- fied that these primarily includes transportation and other related costs that have not been collected by the producer. A rough approximation suggests that the sec- tor producers in the exporting countries do not incur 30 per cent of the trade costs supported by consumers in importing countries. In fact, the pricing decisions of the manufacturers do not tend to consider this factor.
Studies in international trade and economic geography have shown evidence of the role of trade costs for economic outcomes, such as trade patterns (Krugman, 1980), location of industry (Krugman, 1991) and multinational activity (Horstmann & Markusen, 1992). In these models, trade costs for example give rise to the well-known home market effects, agglomeration effects and the proximity- concentration trade-off, respectively. Additionally, empirical evidence shows that trade costs, transportation technology and the transportation sector play a major role for economic exchange (Boylaud & Nicoletti, 2001; Combes & Lafourcade, 2005; Head & Mayer, 2004; Sjostrom, 2004; Teixeira, 2006). In spite of its impor- tance, transportation has mostly been left out of the theoretical analyses. In fact, Paul Samuelson’s (1954) seminal formalization of trade costs, the well-known iceberg trade costs, was introduced to avoid precisely dealing directly with the transport sector.
Transport costs in international Trade can be said to be inclusive of two casts termed as direct and indirect transport costs. Direct transport cost includes freight charges and insurance which is customarily added to freight charges.
Logistics cost is not on account of exporter when exporter offers FOB (Free on Board) price. The basic model for cost of product in international market can be stated as follows:
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Logistics decisions of a company impact costs of products in international market. From the above model it is evident that the total cost of product can be kept unchanged by either decreasing the FOB price or reducing the logistics cost. The former may be restricted under anti-dumping act in the importing country, and hence the only option left is to regulate the logistics cost. That is, if a firm decides to sell on FOB terms, it has no control on logistics cost and may tend to lose the market due to increase in logistics cost especially for goods with price- elasticity greater than one.
The performance of logistics chain has impact on price of goods as because the total landed cost of goods increases. The logistics cost depends on logistics per- formance, fuel, weight/value ratio, freight and supply of vessels. In this research performance of logistics chain is expressed in terms of logistics coefficient described as: /L D TVe = (1)
where D = Demand for goods. It is the logistics coefficient that varies between 0 and 1.
L 0when there is no integration 1when logistics activities are fullyintegrated with core business
e = )
TV = Threshold volume of cargo to shift to integrated mode.
The Existing Model
The causal relationship between the price and demand of a good in terms of extant practice can be modelled as shown in Figure 1:
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The above causality shows that there are two (2) governing loops. These are:
1. Loop number 1 Demand Price Total revenue
This is a reinforcing loop. Demand decreases in case of increase in the price of the product and vice versa. This will result in increase or decrease in total revenue respectively.
2. Loop number 2 Markup Price Total cost Demand
This is also a stabilizing loop that enables the price to be controlled. As increase in price will lead to decrease in demand which in turn will call for reduction of the mark-up, that is, the firm may like to reduce the price so as to remain competitive.
The causal relationship shown in Figure 1 can be explained as follows:
1. The firm decides pricing of goods on the basis of product differentiation which has been expressed as price elasticity. That is, higher the differentia- tion less is the price-elasticity and hence significant is the mark-up.
Figure 1. Causal Diagram Showing Interrelationship Between Price and Demand
Source: The authors’ own creation in Vensim.
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2. The cost of logistics is kept out of purview of firm’s pricing strategy as it quotes for FOB pricing and the freight and other logistics cost is not collected by them.
3. The logistics cost does have an impact on overall cost of goods but is treated as exogenous to the system, and hence the firm can only treat its change as a shock leaving the price to be impacted in a reactive manner.
The effort has been made in the research to show the impact of trade logistics chain in pricing of goods.
Review of Literature
Transport is treated as an exogenous friction τ in the traditional iceberg formula- tion. It is fixed and proportional to the value shipped, with the value-added of transportation services treated as pure waste, or ‘melt’. Hummels (2010) pointed out that in such circumstances, transport costs shift relative prices so that the delivered price equals the origin price multiplied by the iceberg factor.
p* = p(1+ τ) p*/ p = 1 + τ
In production along with scale economies, as in the New Geography or Home Market Effect literatures, iceberg costs create interesting feedback loops, as better (lower τ) access to foreign markets becomes a source of comparative advantage for firms. Hummels (2010) pointed out that shipping cost is not independent of the total cost of goods and is dependent on the price of the goods and other factors such as distance, port efficiencies and so on. He showed that the shipping charge f may be increasing in price (p) of goods because higher value goods require more careful handling and more insurance premium. He modelled the idea of per kilo- gram shipping charge as f = pβX , where X represents other costs of shifting such as distance, port quality, and so on. He defined
* , or in ratiosp p p X= + b
* / 1 ( / )p p weight value X1= + b- (2)
Unless β = 1, the weight/value ratio of a product will be an important determinant of the transportation expenses incurred when trading that product. Hummels and Skiba (2004) estimated that a 10 per cent increase in product weight/value leads to a 4–6 per cent increase in shipping costs measured as ad-valorem, that is, rela- tive to the value of the goods shipped. Hummels (2010) described four implica- tions for trade and trade shocks. One, transportation is no longer an exogenous constant; instead, it depends on the composition of what is being shipped. Two, product weight/value, which varies widely across goods, explains far more varia- tion in ad-valorem transportation costs than other observables like the distance of
Kar and Datta 483
the goods that are to be shipped, the technology with which they are shipped, the quality of port infrastructure, or the intensity of competition between carriers on a trade route. Differences across countries in the product composition (weight/ value ratio) of their trade clearly explains why lower-middle income countries pay nearly twice as much as high-income countries for transporting goods internation- ally (Hummels et al., 2009). Three, since consumers are sensitive to delivered prices, non-iceberg costs change relative demands for products. In particular, the existence of per unit transport charge raises the relative demand for high-quality goods. This is known as the Alchian-Allen effect, which significantly alters the pattern of international trade. Even within narrowly defined product categories, exporters shift the mix of goods sold towards higher price varieties when selling to destinations for which transport costs are high. The strength of this effect is greater when the value of is X larger in the above expression. It is stronger for distant markets, in countries with poor transport infrastructure and when oil price is higher (Hummels & Skiba, 2004). Four, suppose the price of the same good changes over time, due to quality upgradation or the general equilibrium effect of trade liberalization on production costs. Holding shipping charges per unit, f, fixed, product price increases lower the ad-valorem cost imposed by transporta- tion while product price decreases raise the ad-valorem cost of transport. The same is true for high frequency movements in product prices. In essence, the non- iceberg nature of transport costs acts as a kind of shock absorber, dampening the transmission of product price shocks to delivered prices.
Therefore, the first proposition that can be drawn is as follows: ‘Price of the product in international trade depends on the weight/value ratio’.
Cargo freight rates are dependent on oil price (Hummels, 2010). Like other modes of transport, maritime transport relies heavily on oil for propulsion and in view of the limitations imposed by existing technology and costs, it is not yet in a position to adapt effective energy substitutions (Kar & Sinha, 2011). On an aver- age, low income economies, namely landlocked lower-middle income countries and small island developing states face relatively higher transport costs than other economic groupings (UNCTAD, 2017).
The average transport costs for the world is 15 per cent. However, the average transport cost represents 21 per cent of the value of imports for the least deve- loped countries, 19 per cent for landlocked lower-middle income countries and almost 22 per cent for small island developing states (UNCTAD, 2017).
As fuel costs, is the key factor heading in overall transport costs. An increase at the global level of oil prices from $25 to $75 per barrel increases estimated CIF (Cost Insurance and Freight)-FOB (Free on Board) margin increases by 1.4 per cent keeping all other factors being equal (Miao, 2017). Likewise, a reduction in oil prices for example from $100 per barrel to $50 per barrel reduces the CIF-FOB margin by nearly 1 percentage point. In UNCTAD a study was corroborated for estimating the elasticity of shipping freight rates to oil prices and bunker fuel costs. The study concluded that container freight rates as well as rates for shipping iron ore and oil were positively correlated with fuel costs (UNCTAD, 2010).
However, recent trends suggest that the relatively lower oil and fuel cost environ- ment, which was prevailing since mid-2014 has not been reflected in the CIF-FOB margins. This is particularly evident in the case of the landlocked lower-middle
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income countries and small island developing states. This may suggest that other transport cost determinants, such as product and trade composition, size and econo- mies of scale or their lack, remoteness, transport connectivity, insufficient or inad- equate infrastructure, as well as trade imbalances may have had a larger impact. Furthermore, it is also possible that lower fuel costs will produce a rebound effect through increased demand and expenditure for transport services (UNCTAD, 2017). Thus, research shows that lowering transport costs and improving infrastructure can foster trade and reduce the impact of barriers such as remoteness and distance in the case of small island developing states (Borgatti, 2008).
Therefore, the second proposition that can be drawn is as follows: ‘Price of the product in international trade depends on the freight’.
Oil is the major source of energy powering the global economy and supplying 95 per cent of the total energy fuelling in the world transport (UNCTAD, 2010). The fluctuations of oil prices have a clear impact on the overall costs of transport activities, even though the effects differ according to specific transport mode (Martino et al., 2009). Oceanic shipping accounts for the large majority of inter- national trade and on trade in bulk commodities, such as minerals, grain, ores and chemicals which in turn account for about half of all seaborne trade tonnage (UNCTAD, 2015). The literature quantifying the impact of oil on trade as well as transport costs is thin (Brancaccio et al., 2018)
Von Below and Vezina (2016) employed a gravity equation that incorporates oil prices to measure their impact on trade through transportation costs. The esti- mated elasticity is between –1.2 and –1.8, which is fairly high. Hummels (2007) measured the elasticity of freight cost with respect to fuel costs and estimated it about 0.2 to 0.3. While a few recent papers have explored the elasticity of trade with respect to freight rates as well as the role of transportation sector overall (Hummels, 2007; Asturias 2016; Wong, 2017, Brancaccio et al., 2017). Wong (2017) estimated trade elasticity with respect to shipping prices to about –3 and (Limao & Venables, 2001) reported a similar estimate. North (1958) and Estevaeordal et al., (2003) also found that changes in transportation prices have been historically important determinants in trade. Rubin (2009) argued that a shortage in oil and increasing oil prices limits globalisation.
There is a strand of literature that investigates how oil prices affects trade more broadly, besides its impact through transport prices (Backus & Crucini, 2000; Kilian et al., 2009; Chen & Hsu, 2012 and others). Brancaccio et al. (2018) inves- tigated the impact of fuel costs in determining world trade with respect to fuel costs. It was found that the average estimated elasticity is 0.35 but ranges from 0.1 to about 1.2 depending on fuel costs.
Therefore, the third proposition that can be drawn is as follows: ‘Freight, a determinant of price of the product in international trade, depends on the fuel cost’.
The pressure to deliver faster and cheaper has made vehicle utilisation an important aspect of fleet management (Waters, 2009). Operating costs can be reduced to a considerable extent by proper vehicle utilisation. The operation and coordination of large fleets can be difficult for logisticians in the field (Huang et al., 2012). The information systems for coordination and routing at field level will
Kar and Datta 485
have a positive impact on fleet performance and route optimisation (Martinez et al., 2011). The commercial shipping fleet grew up to 3.5 percent in January 2017. Despite this further decline in the annual growth rate, the supply increased faster than demand, at 2.6 per cent, leading to a continued situation of global overcapac- ity and downward pressure on freight rates. In terms of vessel numbers, the growth rate was 2.47 per cent—lower than tonnage—reflecting a further increase in aver- age vessel sizes. In total, the world commercial fleet on 1 January 2017 consisted of 93,161 vessels, with a combined tonnage of 1.86 billion deadweight tonnage (UNCTAD, 2017).
Carriers of liquefied natural gas and other gases recorded continued high growth (+9.7%); growth was also recorded in the oil tanker (5.8%) and chemical tanker (4.7%) segments. In contrast, a long-term decline continued in the general cargo ship segment, which experienced negative growth (–0.2%); its share of world’s ton- nage is currently 4 per cent, compared to 17 per cent in 1980 (UNCTAD, 2017). Current trends in vessel types and sizes, however, suggest that the pressure from the shipping industry will remain, and port and maritime authorities must carefully plan about how to accommodate larger and specialised vessels (UNCTAD, 2017).
Another trend that affects many lower-middle income countries, especially exporters of fruit, fish and meat, is the continued replacement of reefer ship capac- ity by reefer capacity on container ships (UNCTAD, 2017). The reason behind this trend is improved door-to-door transport, reliability and intermodal connec- tivity of containers, as compared with bulk reefer ships and not cost savings achieved on the maritime leg (Arduino et al., 2015).
So, the last proposition which can be derived on the basis of the above discus- sion that ‘Freight, affecting the price of the product in international trade, depends on fleet strength, that is, on the number of vessels’.
System Dynamics Model
The global pricing model described earlier followed by the model is based on the principles of system dynamics. System dynamics is a computer-aided approach to theory building, policy analysis and strategic decision support emerging from an endogenous point of view. It applies to dynamic problems arising in complex social, managerial, economic or ecological systems, literally any dynamic system characterised by interdependence, mutual interaction, information feedback and circular causality. Developed by Jay W. Forrester, it identifies stocks or levels in the system and their inflow and outflow rates. It captures delays and feedback loops that determines the stabilizing and de-stabilizing behaviour of the system (Sterman, 2000).
The variables and their causality as identified from the review and analysis have been collated to describe the structure that conforms the interdependence, mutual interaction, information feedback and circular causality. All dynamics arise from the interaction of just two types of feedback loops: positive (or self- reinforcing) and negative (or self-correcting). Negative loops counteract and
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oppose change. Feedback refers to the situation of X affecting Y and Y in turn affecting X may be through a chain of causes and effects.
In this study based on literature review and propositions Equation 2 is tested for export cargo price in the Indian context.
Methods of Study
The literature review on pricing decisions, international trade theories and theo- ries of causality suggests the two equations. These equations are:
( ) ( : ) ( : )Freight Fuel F Number of vessels VF 1 20R Ua a a= + + (3)
( / : ) ( : )Price weight value W Freight F1 20 V Rb b b= + + (4)
Thus, this study suggests a two-stage approach to evaluate the price of goods in international trade. A system dynamics model will enable to simulate simultaneous impact of change in values of fuel or fleet strength or ‘weight/value ratio for trade with a particular country or destination (i.e., the distance remaining constant)’.
Data Collection
The secondary data of the export of rice to the African countries especially to Senegal has been obtained from The Directorate General of Commercial Intelligence & Statistics (DGCI&S), World Integrated Trade Solution (WITS) and Trade Map and from Bloomberg regarding FOB, freight, the fuel price, quantity, number of vessels. The monthly data is being obtained from October 2011 to May 2018.
Test for Stationary
Dicky Fuller unit root test is performed to test whether the model is stationary for the price. The concept of the test is
Xt = α + ρXt–1 + €t, where Xt is the value in time t and Xt–1 is the value in time t–1 where €t is the error and α and ρ are mathematical constant
Xt– Xt–1 = α + (ρ–1) Xt–1+€t ∆xt = α+∂Xt–1+ €t where ∆xt = Xt– Xt–1 and ∂ = (ρ – 1)
The hypothesis which is set as H0: ∂ = 1 and Ha: ∂ < 1.
Auto Correlation Factor
The Autocorrelation Factor (ACF) has been calculated on the basis of the follow- ing formula
Kar and Datta 487
( )
( ) ( )
.
cov variance
ariance at lag k
The ACF at lag k Y Y
Y Y Y Y 0
2
k k
t
t t k
k
t c c
t
= =
= -
- -
-
+
r
r r
| |
• The Q statistic can be used to determine whether the sample ACFs are jointly equal to zero.
• If jointly equal to zero we can conclude that the series is stationary. • It follows the chi-squared distribution, where the null hypothesis is that the
sample ACFs jointly equal to zero.
( ) deg
sample size lag length
rees of freedom
Q n
n m m
2
1
2
k k
m t
|
=
-
-
-
=
t|
The partial autocorrelation function (PACF) is similar to the ACF; however, it measures correlation between observations that are k time periods apart, after con- trolling for correlations at intermediate lags. As the ACF and PACF is not zero the Autoregressive Moving Average (ARMA) model is applied for forecasting. In this case ARMA (2, 2) is applied for the test.
Empirical Results and Discussions
Table 1 shows the results of Autoregressive Integrated Moving Average (ARIMA). The relationship between freight and two independent variables—fuel and number of vessels—are shown against Equation 5.
( ) ( : ) ( : )Freight F Fuel F Number of vessels V0 1 2R Ua a a= + +
( ) 163060.7 4.19 ( : ) 14.52 ( : )Freight F Fuel F Number of vessels VR U= + + (5)
Figure 2 shows that Auto Correlation factor of price becomes stationary with two lags.
Figure 3 shows Partial Autocorrelation factor of price become stationary after two lags.
After applying the ARMA (2,2) model the variables freight, weight/value ratio, fuel, no of vessels comes out to be significant with a negative impact on the price of the product
488 Foreign Trade Review 55(4)
Table 1. ARIMA for Price
ARIMA freight fuel no. of vessels (setting optimization to BHHH) Iteration 0: log likelihood = –682.6204 Iteration 1: log likelihood = –682.6204 ARIMA regression Number of obs = 80 Sample: 1960m2 – 1966m9 Wald chi2(2) = 632.89 Log likelihood = –682.6204 Prob > chi2 = 0.0000
opg
freight Coeff. Std. Err. Z p >|z| [95% Conf Interval]
freight fuel 4.190193 1.274585 3.29 .001 1.692052 6.68833 Noofvessels 14.52418 1.057944 13.73 .000 12.45065 16.59772 _cons 163,060.7 4,950.484 32.94 .000 153,358 172,763.5
/sigma 1,228.828 99.74468 12.32 .000 1,033.332 1,424.324
Source: The authors’ own creation. Notes: The test of variance is one-sided, and the two-sided confidence interval is truncated at zero.
Figure 2. Autocorrelation of Price
Source: The authors’ own creation.
The forecasting model which is derived from the above model is as follows
( / : ) ( : )Price weight value W Freight F0 1 2V Rb bb= + +
660.086 ( 94638.49) ( / : ) .01426( : )Price weight value W Freight FV R= + - + (6)
Kar and Datta 489
Table 2. Results of the ARIMA (2, 2) Model
ARIMA regression Number of obs = 80 Sample: 1960m2 – 1966m9 Wald chi2 (2) = 28,875.31 Log likelihood = –189.9226 Prob > chi2 = 0.0000
opg
price Coeff. Std. Err. Z p >|z| [95% Conf Interval]
price Freight 0.0142693 0.0032151 4.44 0.000 0.0079679 0.0205708 *W
V –94638.49 920.1216 –102.85 0.000 –96,441.9 –92,835.09
_cons 660.0862 11.36719 11.36719 0.000 637.807 682.3655 ARIMA
ar L1 –1.414638 0.1011461 –13.99 0.000 –1.61288 –1.216395 L2 –0.8633747 0.0773263 –11.17 0.000 –1.014932 –0.7118179
MA ma L1 1.388283 0.1247564 11.13 0.000 1.143765 1.632801 L2 0.9999989 0.1590924 6.29 0.000 0.6881835 1.311814
/sigma 2.65504 0.2243452 11.86 0.000 2.216311 3.093768
Source: The authors’ own creation. Notes: The test of variance is one-sided and the two-sided confidence interval is truncated at zero. *W
V : Weight Value Ratio
Figure 3. Partial Autocorrelation of Price
Source: The authors’ own creation.
490 Foreign Trade Review 55(4)
Conclusion and Policy Implementation
The above Equation 4 can be made generic by including nonlinearity as well. The system dynamics model use linear and non- linear relationship and allow for pol- icy experimentation. This model will reflect the change in price of goods in inter- national market one, many or all of the choice variables affecting the price. As per extant literature, price of the goods in international market is treated as sum of the variable cost and profit margin. The profit margin may include proportional over- head, contribution over variable cost and/or premium depending on demand and supply of goods. This can be represented using system dynamic approach as given in the model below
Figure 4 depicts the causal relationship between logistics cost and other com- ponents of price, and demand. It also defines the interaction between demand and total revenue, and total revenue with price of the goods. The impact of the logi stics cost on price is guided by the logistics-coefficient denoted as ‘Le’. That is, the logistic-coefficient ‘Le’ takes values between 0 (zero) and 1 (one). The price can be computed as shown in Equation 7:
( ) ( ) (1/ )arg cos cosPrice CFR M inal t Logistics t Markup Le#= + + (7)
Where Le = Demand/Threshold quantum When Le = 1, that is, a ship moves with cargo as per its carrying capacity. This
condition can be fulfilled when the logistics activities are completely integrated,
Figure 4. The Proposed Global Pricing Model
Source: The authors’ creation in Vensim.
Kar and Datta 491
that is, the demand does not reduce as logistics costs and mark-up are articulated to keep the TLC (CFR) unchanged.
When Le < 1, that is, a ship moves with cargo less than its carrying capacity, and as such the Le takes value less than 1 and the price increases.
The above causality shows that there are four (4) governing loops. These are:
1. Loop number 1 Total Revenue Markup Price
This is a stabilising loop that enables the price to be controlled. Increase in total revenue may enable to reduce the mark-up, that is, the firm may like to pass on the benefits to the consumer, as measure of customer retention policy. This will, thus, enable the total revenue to grow at a modest rate and at the same time retain and attract customers to increase its sales. Or else, decrease in revenue will lead to increase in price so as to keep its financial position balanced. Thus, this loop addresses the financial perspective of the firm.
2. Loop number 2 Markup Price Total cost Demand
This is also a stabilising loop that enables the price to be controlled. As increase in price will lead to decrease in demand which in turn will call for reduction of the mark-up, that is, the firm may like to reduce the price so as to remain competitive. Thus, this loop addresses the marketing perspective of the firm.
3. Loop number 3 Logistics cost Price Total cost Demand
This is a stabilising loop arising out logistics cost. Increase in demand will increase logistics cost leading to increase in price. But price increase will cause the demand to fall, leading to normalisation of logistics cost. Thus, this loop addresses the supply chain perspective of the firm.
4. Loop number 4 Total revenue Markup Price Demand
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This is a reinforcing (positive) loop. Demand decreases in case of increase in the price of the product and vice versa. This will result in increase or decrease in total revenue respectively. The increase in total revenue may lead to adjustment (decrease) of the mark-up so as pass part of the benefit to the customers in order to retain and attract new customers. On the other hand, decrease in total revenue may lead to adjustment (increase) of the mark-up so as keep its financial position balanced. The increase or decrease of mark-up may further lead to increase or decrease of price causing inverse effect to the demand of the product.
This study shows that cost of logistics has significant impact on demand of product with price elasticity greater than one. Cost of logistics depends on supply of vessels and fuel. The freight depends on weight/value ratio subject to supply of vessels meeting the demand; else there is a possibility of switch over from exist- ing supplier to suppliers from different countries and/or for substitutes.
Performance of logistics affects the total landed cost that in turn reduces the demand of goods from a particular firm. In this case buyers will seek suppliers who can assure better logistics services. A firm can articulate logistics cost through vertical integration leaving out the cost of fuel that acts as an exogenous variable. Even if exporter prefer FOB pricing, customer in international business would take into account Total Landed Cost (TLC) to arrive at the choice of supplier.
Future Scope of Work
The above model captures the dynamics arising out of inter-play amongst variables, primarily price-elasticity, demand, logistics cost, logistics-elasticity- coefficient, price and total revenue. In other words, this pricing model captures the objectives of firm, namely,
1. Be competitive by setting the right price for the right goods in the global market.
2. Maximise stakeholders’ wealth through governance of total revenue. 3. Interplay between FOB and CIF or CFR pricing on the basis of market
advantage and not on cost parameters alone.
However, price changes, as well as income changes, play the major role in theo- retical explanations of shifts in trade from one country to another. It has been natural, that economists have made many attempts to estimate the response of trade to price changes by calculating demand and substitution elasticity for indi- vidual countries’ exports and imports. These impacts have direct bearing with pricing strategy of products in global markets, especially when the logistics cost form a significant component of the total landed cost of goods. This is so because the exporter cannot reduce the price of the goods below the domestic price, as this will result in dumping of goods, while at the same time to remain competitive it has to regulate the total landed cost of the goods. This is so possible with articula- tion of logistics cost and income and substitution effect of the product in the inter- national market.
Kar and Datta 493
A further research can be carried out as a proactive approach in pricing of products. It is to be taken by considering the total landed cost and regulating it with reduction in logistics cost through collaborations, partnerships and vertical integration, reduction in substitution effect through product differentiation and selecting the right market where variation of price has less impact on the income effect of the consumer.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding
The authors received no financial support for the research, authorship and/or publication of this article.
References
Armington, P. (1969). A theory of demand for products distinguished by place of production. International Monetary Fund, Staff Papers, 26, 159–178.
Arduino, G., Carrillo, M. D., & Parola, F. (2015). Refrigerated container versus bulk: Evidence from the banana cold chain. Maritime Policy and Management, 42(3), 228–245.
Asturias, J. (2016). Endogenous transportation costs [mimeo]. School of Foreign Service in Qatar, Georgetown University.
Backus, D. K. & M. J. Crucini. (2000). Oil Prices and the terms of trade. Journal of International Economics, 50, 185–213.
Borgatti, L. (2008). Pacific islands bilateral trade: The role of remoteness and of transport costs. Journal of International Development, 20(4), 486–501.
Boylaud, O., & Nicoletti, G. (2001). Regulatory reform in retail distribution. (OECD Economic Studies No. 32, 2001/I).
Brancaccio, G., Kalouptsidi, M., & Papageorgiou, T. (2018). Geography, search frictions and endogenous trade costs [NBER working paper no. 23581]. The National Bureau of Economic Research.
Chen, S.-S. and Hsu, K.-W. (2012). Reverse globalization: Does high oil price volatility discourage international trade. Energy Economics, 34(5), 1634–1643.
Coe, D. T., Subramanian, A., & Tamirisa, N. (2002). The missing globalization puzzle [working paper]. International Monetary Fund.
Combes, P.-P., & Lafourcade, M. (2005). Transport costs: Measures, determinants, and regional policy implications for France. Journal of Economic Geography, 5(3), 319– 349.
Estevadeordal, A., Frantz, B. & Taylor, A. (2003). The rise and fall of World Trade, 1870– 1939. Quarterly Journal of Economics, 118(2), 359–407.
Gani, A. (2017). The logistics performance effect in international trade. The Asian Journal of Shipping and Logistics, 33(4), 279–288.
Grossman, G. (1998). Comment on deardorff. In J. A. Frankel (Ed.), In the regionalization of the world economy (pp. 33–57). Chicago Press.
Head, K., & Mayer, T. (2004). The empirics of agglomeration and trade. In J. Henderson & J.-F. Thisse (Eds.), Handbook of regional and urban economics (vol. 4). North- Holland.
494 Foreign Trade Review 55(4)
Horstmann, I., & Markusen, J. (1992). Endogenous market structure in international trade. Journal of International Economics, 32(1–2), 109–129.
Huang, M., Smilowitz, K., & Balcik, B. (2012). Models for relief routing: Equity, efficiency and efficacy. Procedia – Social and Behavioral Sciences, 17(1), 416–437. https://doi. org/10.1016/j.sbspro.2011.04.525
Hummels, D. (1999). Have international transportation costs declined? [working papers]. Purdue University.
Hummels, D. (2001). Time as a trade barrier [working papers 17758]. Purdue University. Hummels, D. (2008). Trends in Asian trade: Implications for transport infrastructure and
trade costs. In D. H. Brooks & D. Hummels (Eds.), Infrastructure’s role in lowering Asia’s trade costs: Building for trade. Edward Elgard Publishers.
Hummels, D. (2009). How further trade liberalization would change greenhouse gas emissions from international freight transport [working paper]. NBER, Purdue University. https://krannert.purdue.edu/faculty/hummelsd/papers/co2%20paper.pdf
Hummels, D. (2010). Transportation costs and adjustments to trade [working paper]. NBER, Purdue University. https://www.krannert.purdue.edu/faculty/hummelsd/papers /Transportation%20Costs%20and%20Adjustments%20to%20Trade.pdf
Hummels, D., & Skiba, A. (2004). Shipping the good apples out: An empirical confirmation of the Alchian and Allen conjecture. Journal of Political Economy, 112(6), 1384–1402.
Hummels, D., Ishii, J., & Yi, K.-M. (2001). The nature and growth of vertical specialization in world trade. Journal of International Economics, 54(1), 75–96.
Hummels, D., Lugovsky, V., & Skiba, A. (2009). The trade reducing effects of market power in international shipping. Journal of Development Economics, 89(1), 84–97.
Kar, A., & Sinha, D. (2011). The impact of cost of logistics in pricing of goods for global markets: A pricing framework [conference paper]. Excellence in Research and Education Conference (EREC), IIM Indore.
Kilian, L., Rebucci, A., & Spatafora, N. (2009). Oil shocks and external balances. Journal of International Economics, 77(2), 181–194.
Krugman, P. (1980). Scale economies, product differentiation, and the pattern of trade. American Economic Review, American Economic Association, 70(5), 950–959.
Krugman, P. (1991). Increasing returns and economic geography. Journal of Political Economy, 99(3), 413–499.
Krugman, P. (1996). Urban concentration: The role of increasing returns and transport costs. International Regional and Science Review, 19(1–2), 5–30.
Limao, N., & Venables, A. J., (2001). Infrastructure, geographical disadvantage, transport costs and trade. World Bank Economic Review, 15(3), 451–479.
Martinez, A. J. P., Stapleton, O., & Van Wassenhove, L. N. (2011). Field vehicle fleet mana- gement in humanitarian operations: A case-based approach. Journal of Operations Management, 29(5), 404–421.
Martino, A., Casamassima, G., & Fiorello, D. (2009). The impact of oil prices fluctuations on transport and its related sectors [report]. Directorate General Internal Policies of the Union.
Miao, F. (2017). Estimating transport and insurance costs of international trade [working paper no. 2017/4]. OECD.
Mill, J. S. ([1848] 1899). Principles of political economy with some of their applications to social philosophy (W. J. Ashley (Ed.), 7 edn.). Longmans, Green.
North, D. (1958). Ocean freight rates and economic development 1750–1913. Journal of Economic History, 18(4), 537–555.
Ohlin, B. (1933). Interregional and international trade. Harvard University Press.
Kar and Datta 495
Ricardo, D. (1817). On the principles of political economy and taxation (John Murray, London). In Sraffa, P. (Ed.), The works and correspondence of David Ricardo (Vol. 1). Cambridge University Press, 1951.
Rubin, J. (2009). Why your world is about to get a whole lot smaller: Oil and the end of globalization. Random House.
Samuelson, P. A. (1952). The transfer problem and transport costs: The terms of trade when impediments are absent. The Economic Journal, 62(246), 278–304.
Sjostrom, W. (2004). Ocean shipping cartels: A survey. Review of Network Economics, 3(2), 107–134.
Sterman, J. D. (2000). Business dynamics: Systems thinking and modelling for a complex world. Irwin McGraw Hill.
Teixeira, A. (2006). How important was the 19th century transportation revolution for U.S. development? (2006 Meeting Papers 831). Society for Economic Dynamics.
Tinbergen, J. (1962). An analysis of World Trade flows. In J. Tinbergen (Ed.), Shaping the world economy. Twentieth Century Fund.
UNCTAD. (2010). Oil prices and maritime freight rates: An empirical investigation [technical report UNCTAD/DTL/TLB/2009/2]. United Nations Conference on Trade and Development.
UNCTAD. (2015). Review of maritime transport [technical report]. United Nations Conference on Trade and Development, Geneva, Switzerland.
UNCTAD. (2017). Review of maritime transport: An empirical investigation [technical report]. United Nations Conference on Trade and Development, Geneva, Switzerland.
Von Below, D., & Vezina, P.-L. (2016). The trade consequences of pricey oil. IMF Economic Review, 64(2), 303–318.
Waters, D. (2009). Supply chain management: An introduction to logistics (2nd ed.). Palgrave Macmillan.
Wong, W. F. (2017). The round trip effect: Endogenous transport costs and international trade [staff working paper/Document de travail du personnel 2017–41].