metadata: authors: 0: author: Iimi,Atsushi
count: Europe,Central Asia,World
docna: 0: docna: Estimating Road Freight Transport Costs in
            Eastern Europe and Central AsiaUsing Large Shipping Data
docty: Policy Research Working Paper
theme: Urban Infrastructure and Service Delivery,Data Development and Capacity Building,ICT,Economic Policy,ICT Solutions,Economic Growth and Planning,Public Transport,Private Sector Development,Regulation and Competition Policy,Public Sector Management,Urban and Rural Development,Business Enabling Environment,Data production, accessibility and use,Spatial Growth,Urban Development
abstracts: cdata!: The recent global crises, such as the
            COVID-19 crisis, remind us of the importance of efficient
            transportation and logistics. Notably, however, even before
            the crises, some regions were already experiencing a gradual
            increase in freight costs, with more and more empty trucks
            observed. The paper recasts light on the question of how
            road freight costs are determined using large, unique
            shipping data from Eastern European and Central Asian
            countries. It finds that economies of scale are significant
            in both freight weight or load factor and distance. The
            elasticity with respect to freight weight is particularly
            high at about 0.3 to 1.0 in absolute terms. Thus, to contain
            trucking costs, it is important to maximize the load factor
            through freight consolidation at origins and destinations.
            The elasticity with respect to distance is relatively modest
            at 0.04 to 0.16 in absolute terms but still statistically
            significant, indicating that distance may not necessarily be
            a constraint on trade and regional integration. Trucking
            costs also decrease with driving speed, a proxy for
            efficiency of movements or road conditions. The elasticity
            is significant for food products (&#x2212;0.03) and
            other consumer goods (&#x2212;0.11). Finally, the paper
            finds that border crossing adds 3&#x2013;4 percent to
            freight costs.
display_title: Estimating Road Freight Transport
            Costs in Eastern Europe and Central AsiaUsing Large Shipping Data
listing_relative_url: /research/2023/07/34127179/estimating-road-freight-transport-costs-eastern-europe-central-asiausing-large-shipping-data
topicv3: Firm Dynamics and Productivity Growth
            m1504195 2864,Firm Behavior and Jobs m1504204 2873
publishtoextweb_dt: 2023-07-31T00:00:00Z
dois: 10.1596/1813-9450-10533
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 914707
model_extractions: Policy Research Working Paper 10533 Estimating Road Freight Transport Costs in Eastern Europe and Central Asia Using Large Shipping Data _Atsushi Iimi_ Transport Global Practice July 2023
Policy Research Working Paper 10533

# **Abstract**

The recent global crises, such as the COVID-19 crisis, remind us of the importance of efficient transportation and logistics. Notably, however, even before the crises, some regions were already experiencing a gradual increase in freight costs, with more and more empty trucks observed. The paper recasts light on the question of how road freight costs are determined using large, unique shipping data from Eastern European and Central Asian countries. It finds that economies of scale are significant in both freight weight or load factor and distance. The elasticity with respect to freight weight is particularly high at about 0.3 to 1.0 in absolute terms. Thus, to contain trucking costs, it is important to maximize the load factor through freight consolidation at origins and destinations. The elasticity with respect to distance is relatively modest at 0.04 to 0.16 in absolute terms but still statistically significant, indicating that distance may not necessarily be a constraint on trade and regional integration. Trucking costs also decrease with driving speed, a proxy for efficiency of movements or road conditions. The elasticity is significant for food products (−0.03) and other consumer goods (−0.11). Finally, the paper finds that border crossing adds 3–4 percent to freight costs.

This paper is a product of the Transport Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The author may be contacted at aiimi@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Estimating Road Freight Transport Costs in Eastern Europe and Central Asia**

# **Using Large Shipping Data**

Atsushi Iimi<sup>¶†</sup> Eastern & Southern Africa, Transport The World Bank Key words: Road freight rates, shipping costs, regional integration.

JEL classification: R41; C21; O52.

> ¶ Corresponding author.

> † This paper is a background paper of the Shrinking Economic Distance study.
<!-- Start of picture text --> _cD) 12000<br>.—io‘3 © 10000<br>2e£omOrv= 8000<br>5oebelt 8<br>£3 6000<br>8B 4000<br>25<br>—cw= £ 2000<br>of<br>a<br>0)<br>ANAANMMATNNOORRANMDAOCOGAANN<br>SS SS stg Stat at al st at at al st a al gt al<br>SSSESESSESSESESESESESESS<br>S77 SEV SVS VISA SV SVNSVSISVSVST<br><!-- End of picture text --> <!-- Start of picture text --> 130<br>eS 120<br>=<br>iW 110<br>5<br>®& 100<br>>—<br>2<br>S<br>=ha® g0<br>O 70<br>60<br>segesdanmyrneneagua<br>SSBSBESSBSESESESESSSSSS<br>NNN N NNN N NNN NNN<br><!-- End of picture text -->
- 3 -

increase drivers’ wages. While drivers over 55 years old account for 34 percent of the total labor force in this sector in Europe, only 7 percent are under 25 years old (Ti et al., 2022).

3. Load factor is an important determinant of freight rates. In Europe, the share of road freight kilometers carried by empty vehicles has been increasing especially for international freight ( **Figure 4** ). From the efficiency point of view, holding everything else constant, freight rates may be higher when there are more empty containers. Freight rates are also determined by the road condition. The higher quality of roads generally leads to lower freight rates. In theory, road user costs, including vehicle operating and maintenance costs and drivers’ time costs, can be reduced if roads are well maintained. Road roughness is negatively associated with driving speed (e.g., Alessandroni et al., 2017). However, the relationship among road conditions, traffic speed and freight rates may be complicated by other factors involved, such as the trucking industry’s market structure. Given all these factors, it remains open to argument whether overall freight rates increased or declined, and why.

4. The current paper recasts light on the question of how road freight transport costs are determined, using large sample shipping data from Eastern European and Central Asian countries. Since the early 2010s, the region has made significant infrastructure investments to improve interregional connectivity between Europe and East Asia. Despite our general belief that the enhanced connectivity would facilitate regional integration and trade (e.g., ADB, 2020; Kalyuzhnova and Holzhacker, 2021), there is little empirical evidence to the best of my knowledge. The paper takes advantage of unique micro data, which comprises stop-by-stop shipping data in the Central Asia Regional Economic Cooperation (CAREC) member countries, including Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan, collected by the Asian Development Bank (ADB).<sup>2</sup> The data is focused on longdistance truck shipments and covers a long period of time from 2010 to 2020, which allows us to evaluate the impacts of long-term infrastructure investment made in the region on freight costs.

> 2 See ADB (2014, 2020) for more details. I would like to express my special thanks to the CAREC Corridor Performance Measurement and Monitoring (CPMM) team for sharing relevant raw data.
<!-- Start of picture text --> 230<br><= 2.10 ——Euro super 95<br>z 1.90 Diesel<br>8<br>= 1.70<br>3 790<br>y 1.30<br>= 1.10<br>20,90<br>5<br>= 0.70<br>0.50<br>Sagaggasaaes5beaagaa<br>§& &§ § &§&& § § &§ § & §& §<br><!-- End of picture text --> <!-- Start of picture text --> 50 ——Total<br>» 45 ——National<br>= 40 0s ——International<br>& 35<br>se =<br>fe ee ———<br>5 82 28.6 27.9<br>2 § 20<br>— 16.9<br>e 10 13.8<br>f.<br>.<br>oS ON AD Dh SOAS DD<br>PPP” PAP AP” AP” AP AP” PGP”<br><!-- End of picture text -->
- 5 -

has been declining for domestic freight in recent years, the empty rate for international freight has been increasing, raising concerns about freight inefficiency and high costs at the regional level. The following analysis will examine the potential structural difference between domestic and international shipments.

8. The freight consolidation at origins and destinations is a key determinant of the load factor. Freight rates tend to be lower between a pair of larger cities because of economies of scale and consolidation (e.g., Tsekeris, 2022). In Europe, the road freight rates between Paris and Madrid, two large urban areas with a total population of over 5 million, are much lower than the cost of transporting from Paris to Duisburg, a mid-sized city in Germany with a population of about half a million ( **Figure 6** ). In addition, the shipping rate from Paris to Duisburg is much lower than that from Duisburg to Paris because the demand for shipments in the former direction is much greater. In other words, it is more difficult to fill an entire trailer at Duisburg.

9. In the urban context, the literature also calls for collaborative freight consolidation (e.g., Arvidsson, 2013; Teo et al., 2014). As transport costs become lower, firms hold less inventory, requiring more frequent and smaller lot orders. This is likely to create traffic congestion in cities. Tsekeris (2022) shows that urban sprawl has a significant impact on general road freight costs in Europe.

10. Besides freight consolidation, the literature shows that load factors are also determined by other factors, such as trip distance, fleet characteristics, including ownership, market (segment) structure, usage of new technologies (such as Electric Vehicle Management Systems) and efficiency of freight consolidation (e.g., Barla et al., 2010; Abate, 2014; Santen, 2017; Santen and Rogerson, 2018; Zgonc et al., 2019). Our data does not allow capturing these factors directly but unobserved characteristics are controlled for in a statistical manner.
<!-- Start of picture text --> 2.50<br>r=x ——Articulate truck<br>© ,<br>g 2.00 —— Heavy truck<br>go —— Passenger car<br>8= 1.50<br>=}<br>%wv 1.00<br>8<br>5<br>0.50<br>3 mn<br>ad<br>8 0.00<br>a 2 4 6 8 10 12 14 16 18 20<br>International Roughness Index (m/km)<br><!-- End of picture text --> <!-- Start of picture text --> 3.50<br>= 2.99<br>>S 3.00<br>=3 2.50<br>% 2.00 1.84<br>8s<br>1.33<br>£150 442 .<br>—_<br>2 1.00<br>3<br>8 0.50<br>in<br>0.00<br>Paris Madrid Paris Duisburg<br>to Madrid _ to Paris to Duisburg to Paris<br><!-- End of picture text --> Binx =
<!-- Start of picture text --> 60<br>°° —— eee ae<br>40<br>30<br>0 - ——————————<br>10 == Avg. speed (km/h)<br>——Cargo volume (tons)<br>0<br>2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020<br><!-- End of picture text -->
- 8 -

**Figure 9. Distribution of Freight Volume**

<!-- Start of picture text --> 0 20 40 60 80<br>Cargo volume (tons)<br>.2<br>.15<br>.1<br>Density<br>.05<br>0<br><!-- End of picture text --> 15. To control other route-specific characteristics, two variables are considered in _Z_ .<sup>4</sup> First, a dummy variable for the existence of border crossing is included. This also distinguishes domestic and international cargos. Border crossing often incurs additional time and costs for customs, security clearance and administrative procedures. If this is the case, freight costs are likely to be increased.

16. Second, to capture the topographic complexity of each route, the difference in elevation between origin and destination is calculated. Different routes are differently characterized from the geographic point of view. The elevation difference aims at measuring the hilliness of a route. Certain areas, for instance, in Kyrgyzstan and Tajikistan, are very mountainous. Certainly, fuel efficiency would likely vary across routes in those areas.

17. Freight costs may also differ depending on type of commodity. To test such a possibility, a set of commodity-specific dummy variables are included, _type_ . Given the country context, cargos are classified into six groups: (i) nearly empty cargo (less than 100 kg), (ii) agricultural commodities and food products, (iii) clothing, apparel and textiles, (iv) consumption and other goods, (v) equipment (including vehicles), and (vi) minerals, metals and materials. Holding

> 4 Other route-specific characteristics, such as the market size of origins and destinations, and the horizontal geographic complexity measured by the ratio of road distance to straight-line distance, were also included and examined in an earlier version of the paper. The literature suggests the potentially important impacts of asymmetric market distribution between origins and destinations on the ability of freight consolidation (e.g., Ti et al., 2022; Tsekeris, 2022). The load factor normally increases with market size (e.g., Barla et al., 2010; Abate, 2014). However, these variables were dropped for simplification purposes.
- 9 -

everything else constant, freight costs may be higher if cargos are perishable, such as food. The empty cargos are used as a baseline.

18. Finally, the time-specific fixed-effects, _ct_ , are included. The data covers 11 years, from January 2010 to December 2020. About 130 year-month dummy variables are created with January 2010 used as a baseline. The coefficients will reveal the time trend of average freight costs over the sample period.

# **III. Data**

19. The main data comes from the CAREC Corridor Performance Measurement and Monitoring (CPMM) database collected by ADB. The analysis uses data for seven countries in Eastern Europe and Central Asia: Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan. The region is of particular interest because many large transport investments have been made to improve road and rail connectivity since the early 2010s. For instance, Kazakhstan implemented a massive infrastructure investment program, Nurly Zhol, to redevelop public infrastructure to connect its main cities, logistics centers and free-trade zones to the border crossings, including ports on the Caspian Sea dry ports. The country spent about US$36.6 billion for five years: 2013-2017 (UNECE, 2019). By the same token, the entire region is expected to be reconnected for more regional trade and integration. Notably, however, there are only a few micro studies that examined transport freight costs at the regional level. Persyn et al. (2022) find that the expected impact of increased road user costs on road freight prices is particularly high in Eastern Europe.

20. Originally, the CPMM database contains over 187,000 observations, covering both road and rail shipments. Excluding data with missing information and apparent outliers,<sup>5</sup> 51,137 road shipment data are used. A total of 8,952 carriers transported cargos among 397 locations. Each route is georeferenced. Note that the database is developed at the stop-by-stop level. Even on the

> 5 To exclude outliers, the letter-value display analysis was used, which identifies extreme values in the tails of the distribution that fall outside of three times the interquartile range (IQR) for each variable.
- 10 -

same road segment, some shipping carriers may drive faster or transport cargos at lower prices than others.

21. The summary statistics are shown in **Table 1** . Our dependent variable is the road transport freight costs in U.S. cents per km. The freight costs include both vehicle transport costs, such as drivers and fuel costs, parking fees and minor repairs, and other trade and administrative costs at intermediate nodes, such as border crossing and traffic police checkpoints. All kinds of expenditures are included (see ADB (2014)). The costs are converted to U.S. cents and normalized to 2010 constant dollars using the U.S. consumer price index. The average freight cost is 4.57 U.S. cents per ton-km with a standard deviation of 2.41. It is distributed in a highly skewed manner around 4 to 5 cents ( **Figure 10** ). If a heavy truck with a maximum capacity of 20 tons is fully loaded, the average shipping cost for 100 km distance would be approximately US$91.

22. The distance, average speed and freight volume data also come from the CAREC CPMM database. Our sample is focused on long-haul cargos. The average distance is 170 km. The average speed is 50 km per hour. Note that the speed may differ across routes even though the freight operator, i.e., driver is the same. On the other hand, the freight volume is generally unchanged across the routes as long as they constitute the same shipment. The average cargo weight is 18.2 tons. As discussed above, most common vehicles in our sample data are 20-ton trucks. Thus, this variable is heavily concentrated to 20 tons or less.

23. The region is diverse in topographic complexity. The average vertical difference in elevation between origin and destination is about 144 meters. About 20 percent are international shipments.

24. Finally, our data comprises a wide variety of commodities and goods. All cargos are classified into five groups in addition to empty cargo: (i) agricultural commodities and food products, (ii) clothing, apparel and textiles, (iii) consumption and other goods, (iv) equipment (including vehicles), and (v) minerals, metals and materials. In our sample, agricultural commodities and food products account for about 46 percent.
- 11 -

## **<u>Table 1. Summary Statistics</u>**

|Variable|Abb.|Obs|Mean<br>|Std. Dev. M|in<br>|Max|
|---|---|---|---|---|---|---|
|Road freight costs (U.S. cents per ton-km)|_p_|51,137|4.57|2.41|0.07|15.92|
|Average driving speed (km per hour)|_speed_|51,137|50.00|8.85|0.60|94.00|
|Road distance traveled between origin and<br>destination (km)|_dist_|51,137|171.99|101.17|8.00|600.00|
|Freight weight (tons)|_weight_|51,137|18.17|2.87|0.31|29.56|
|Difference in elevation between origin and<br>destination (m)|_elev_|51,137|143.92|162.26|0.00|1159|
|Dummy for international freight|_crossborder_|51,137|0.20|0.40|0|1|
|Type of commodity:|||||||
|Nearly empty cargo||51,137|0.00006|0.006|0|1|
|Clothing, apparel and textile|_cloth_|51,137|0.202|0.402|0|1|
|Industrial and transport equipment and parts|_equipment_|51,137|0.128|0.335|0|1|
|Agricultural commodities and food products|_food_|51,137|0.459|0.498|0|1|
|Minerals, metal products and materials|_mineral_|51,137|0.092|0.290|0|1|
|Other consumergoods|_goods_|51,137|0.118|0.323|0|1|

**Figure 10. Distribution of Freight Costs**

<!-- Start of picture text --> 0 5 10 15<br>Unit costs (US cents /ton-km)<br>.3<br>.2<br>Density<br>.1<br>0<br><!-- End of picture text -->

# **IV. Main Estimation Results**

25. The ordinary least squares (OLS) regression is performed with three different specifications: Linear, quadratic and logarithmic. The estimated results are shown in **Table 2** . All the coefficients are broadly consistent with our prior expectation. First of all, it is found that traffic speed, which we interpret as a proxy for road conditions, has a negative impact on freight rates. All the coefficients are negative and statistically significant, meaning that efficient freight
- 12 -

movements, potentially because of roads in good conditions, lead to lower freight costs. The elasticity varies from -0.29 to -0.49, depending on the specification ( **Table 3** ). The finding is consistent with the traditional assumption for transport infrastructure investment: Good roads reduce transport costs (e.g., Lokshin and Yemtsov, 2005; Teravaninthorn and Raballand, 2009; Danida, 2010). It is an important addition to the literature to quantify the magnitude of the impact. The sensitivity of the estimation results to model specification may be able to understand that the potential impact of road conditions is complex, and perhaps, highly nonlinear.

26. Second, the results suggest that economies of scale in both distance and load are important for shipping carriers to contain freight transport costs. The coefficients of _dist_ are negative and statistically significant. In the logarithmic case, it is estimated at -0.064. Thus, the longer distance, the lower freight transport costs. In addition, freight transport costs reduce with cargo weight. The coefficient tends to be large (in absolute terms) and is 0.66 in the logarithmic model. Recall that the weight variable can be interpreted as a proxy for the load factor because the truck capacity is 20 tons for most shipping carriers in our data. Thus, freight costs are lowered as load factors increase, as observed in the literature (e.g., Abate 2014; Tsekeris 2022).

27. For other characteristics of shipments, it is clear that topology matters to freight transport costs. Shipping costs are higher in hilly areas. The elevation variable ( _elev_ ) has positive coefficients regardless of specification. In the logarithmic case, it is estimated at 0.049, meaning that the freight costs increase by about 0.5 percent if the vertical difference in elevation between origin and destination points increases by 10 percent. This is intuitively acceptable because driving speeds, particularly for heavy duty trucks, tend to be constrained on a hilly or mountainous terrain, causing more fuel and time costs.

28. As expected, border crossing adds to freight costs. The coefficient is significantly positive regardless of specification. Despite recent efforts toward integrating and simplifying border crossing procedures, it seems that border crossing remains costly in the region. According to the linear and quadratic models, it adds an additional cost of 0.15 or 0.17 U.S. cents per ton-km, which is about 3-4 percent of average freight unit costs.
- 13 -

29. To examine which variable has the largest influence on freight costs, all the elasticities are evaluated at sample means ( **Table 3** ). The largest elasticity (in absolute terms) is found for the weight variable. This is robust regardless of specification. The elasticity is estimated at 0.59 to 0.77. This confirms the particular importance of load factors to determine road freight costs. The elasticity associated with driving speed, a proxy for road conditions, is also high at 0.29 to 0.46. It means that holding everything else constant, a 10 percent increase in speed would reduce freight costs by 3 to 5 percent. Then, the elasticity of distance follows, which is relatively small at 0.04 to 0.06.

**<u>Table 2. OLS estimation results: Pooled model</u>**

||Linear<br>||Quadratic<br>|<br>|Logarithm<br> <br>|ic<br>|
|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.<br>|Std.Err.|
|_speed_|-0.042|<br>(0.002) ***|-0.338|<br>(0.011) ***|||
|_speed_<sup>2</sup>|||0.0031|(0.0001) ***|||
|ln_speed_|||||-0.395|(0.015) ***|
|_dist_|1.6E-04|(1.3E-04)|-2.3E-03|(3.6E-04) ***|||
|_dist_<sup>2</sup>|||3.4E-06|(6.6E-07) ***|||
|ln_dist_|||||-0.064|(0.005) ***|
|_weight_|-0.194|<br>(0.005) ***|-1.040|<br>(0.031) ***|||
|_weight_<sup>2</sup>|||0.025|<br>(0.001) ***|||
|ln_weight_|||||-0.664|(0.015) ***|
|_elev_|2.1E-03|(7.9E-05) ***|9.0E-04|(1.7E-04) ***|||
|_elev_<sup>2</sup>|||8.3E-07|(3.2E-07) ***|||
|ln_elev_|||||0.049|(0.001) ***|
|_D_elev_|||||-0.076|(0.078)|
|_crossborder_|0.152|<br>(0.025) ***|0.170|<br>(0.025) ***|0.024|(0.004) ***|
|_cloth_|-1.168|<br>(0.269) ***|3.496|<br>(0.340) ***|-0.029|(0.064)|
|_equipment_|-0.241|<br>(0.269)|4.153|<br>(0.339) ***|0.141|(0.064) **|
|_food_|-1.116|<br>(0.270) ***|3.522|<br>(0.341) ***|0.005|(0.064)|
|_mineral_|-0.154|<br>(0.273)|4.333|<br>(0.342) ***|0.107|(0.065) *|
|_goods_|-0.426|<br>(0.269)|3.973|<br>(0.339) ***|0.019|(0.064)|
|constant|9.533|<br>(0.270)***|19.185|<br>(0.393)***|4.650|(0.086)***|
|Obs.|51,137||51,137||51,137||
|R-squared|0.2526||0.3293||0.3337||
|F stat.|129.66||163.15||160.26||
|No. of time dum|my variables||||||
||131||131||131||

Note: The dependent variable is freight rate, _p_ . for the first two models, and the log of freight rate, ln _p_ , for the last column model. Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
- 14 -

**<u>Table 3. Estimated elasticity: Pooled model</u>**

||Linear||Quadratic||Logarithmic||
|---|---|---|---|---|---|---|
|Variable|Elasticity|Std. Err.|<br>Elasticity|Std. Err.|Elasticity<br>|Std. Err.|
|_speed_|-0.464|<br>(0.021)|***<br>-0.291|<br>(0.021) ***|-0.395|(0.015) ***|
|_dist_|0.006|<br>(0.005)|<br>-0.043|<br>(0.006) ***|-0.064|(0.005) ***|
|_weight_|-0.773|<br>(0.020)|***<br>-0.593|<br>(0.019) ***|-0.664|(0.015) ***|
|_elev_|0.065|<br>(0.002)|***<br>0.036|<br>(0.003)***|0.049|(0.001)***|

*, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

30. After controlling all the observables, it seems that the freight costs did not change substantially during the overall sample period. The estimated coefficients of the time-specific fixed-effects are plotted over time ( **Figure 11** ). There are clear structural differences in our data, possibly caused by changes in data collection method. In a subset of the data for the period from 2013 to 2018, the freight costs declined consistently. In more recent years, there was no significant time-specific effect on freight costs. This does not necessarily contradict the fact that the nominal transport costs declined over time. The average transportation costs on the regional corridors were indeed halved from US$1,400 per 20-ton cargo per 500 km in 2014 to US$700 in 2019 (ADB, 2020). The change in nominal costs is explained by all the observables in the equation, such as increased speed and increased share of international freight.

31. Finally, most of the commodity-type-specific fixed effects are found to be significant. The null hypothesis that all the coefficients are the same can be easily rejected. The _F_ -test statistics are estimated at 357.15 and 222.75 for the linear and quadratic models, respectively. It is estimated at 212.78 in the logarithmic case. Thus, it is likely that the freight costs are determined differently across types of commodities. There is a clear tendency that the shipping costs for light merchandises, such as apparel, consumer goods and food products, are lower than those for heavy materials and industrial equipment. The coefficients are plotted using the logarithmic estimation result ( **Figure 12** ).
- 15 -

**Figure 11. Estimated time-specific fixed-effects**

**Figure 12. Estimated commodity specific fixed-effects**

<!-- Start of picture text --> Clothing, apparel,<br>texitile<br>Crops, food products<br>Consumer and<br>other goods<br>Minerals, metals,<br>other materials<br>Equipment<br>-.2 -.1 0 .1 .2 .3<br>Coefficient and Confidence Interval<br>.6<br>.4<br>.2<br>0<br>-.2<br>Coefficient and Confidence Interval<br>-.4<br>2010m1 2011m1 2012m1 2013m1 2014m1 2015m1 2016m1 2017m1 2018m1 2019m1 2020m1 2020m12<br><!-- End of picture text -->

# **V. Heterogeneity and Robustness**

32. As discussed above, there seems to be a systematic difference in freight costs across commodities. To investigate potential heterogeneity in freight cost determinants, the sample data is divided into five groups: (i) clothing and apparel, (ii) equipment and vehicles, (iii) agricultural commodities and food, (iv) minerals and metals, and (v) consumer goods. The logarithmic model is separately estimated by applying OLS ( **Table 4** ).<sup>6</sup> The results are broadly consistent with the above, but some coefficients are different among groups of commodities. For instance, driving speed or road conditions matter most for equipment and minerals ( **Figure 13** ). For other types of shipments, i.e., food products and consumer goods, the elasticities are small. It can be interpreted to mean that production-oriented capital goods are more sensitive to delivery efficiency than consumer goods.

33. For shipping distance, it is found that the freight cost increases with distance for all kinds of merchandises but equipment. The elasticities are small for clothing and food products. Thus, the freight cost does decrease with distance, but very marginally. This may make sense because some agricultural commodities are perishable, and thus, it is costly to transport for a long

> 6 The estimation results are largely similar for the linear and quadratic models. Thus, the only results from the logarithmic estimation are shown in the table.
- 16 -

distance. On the other hand, there are significant economies of distance expected for shipping minerals and consumer goods, which are generally unperishable.

34. Regardless of type of commodity, freight costs are very elastic to cargo weight or load factor. The elasticity varies from 0.3 to 1.0, depending on commodities. Heavy, bulky commodities, such as minerals, materials and agricultural products, look more sensitive. For these goods, freight efficiency and consolidation are important to lower freight costs.
**<u>Table 4. Separate OLS estimation results by type of commodity</u>**

|Commodity type:|_cloth_||_equipment_||_food_||_mineral_||_goods_||
|---|---|---|---|---|---|---|---|---|---|---|
||Coef.<br>|Std.Err.|Coef.<br>S|td.Err.|Coef.<br>|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|ln_speed_|-0.326|(0.039) ***|-0.445|(0.029) ***|-0.061|(0.019) ***|-0.599|(0.052) ***|-0.277|<br>(0.039) ***|
|ln_dist_|-0.038|(0.009) ***|0.024|(0.012) ***|-0.054|(0.006) ***|-0.100|(0.014) ***|-0.161|<br>(0.013) ***|
|ln_weight_|-1.003|(0.039) ***|-0.305|(0.025) ***|-0.781|(0.024) ***|-0.931|(0.046) ***|-0.544|<br>(0.040) ***|
|ln_elev_|0.077|(0.003) ***|0.070|(0.004) ***|0.017|(0.002) ***|0.106|(0.006) ***|0.064|<br>(0.006) ***|
|_D_elev_|0.017|(0.171)|-0.381|(0.375)|0.742|(0.578)|0.868|(0.328) ***|-0.059|<br>(0.067)|
|_crossborder_|-0.024|(0.009) ***|0.112|(0.011) ***|-0.013|(0.004) ***|0.058|(0.015) ***|0.127|<br>(0.018) ***|
|constant|4.873|(0.182)***|3.239|(0.158)***|4.082|(0.111)***|5.889|(0.270)***|4.620|<br>(0.210)***|
|Obs.|10,347||6,565||23,458||4,729||6,035||
|R-squared|0.4819||0.3655||0.3824||0.4220||0.4659||
|F stat.|65.32||34.48||131.00||...||97.37||
|No. of time dumm|y variables||||||||||
||131||131||131||131||131||

Note: The dependent variable is freight rate in logarithm, ln _p_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
<!-- Start of picture text --> Ow0.00 Fa<br>-<br>EJ sy © RS e<br>zy -0.20 osSX <<br>2Y<br>3 & -0.30<br>3 oO<br>3o = -0.40<br>3 9<br>E 300<br>3 ~ (6.60. TM Coef.<br>-0.70 —+1.64*S.E.<br>-_- *<br><!-- End of picture text --> <!-- Start of picture text --> 0.10<br>s 0.05<br>=]=<br>£3 0.00<br>os <<br>Bo R. & « $<br>3is F -0.05 =o es &~ Bs<br>3 eo &<br>= 2 -0.10<br>£3 -0.15 = Coef.<br>=+1.64*S.E.<br>-__<br><!-- End of picture text --> <!-- Start of picture text --> 0.00<br>g - A “ fe)<br>= 020 6 a & &<br>& =&-0.40 oe<br>BSv<br>3 £° -0.60<br>Rommel<br>Fso wv3 -0.80<br>3<br>uw -1.00 @ Coef.<br>=+1.64*S.E.<br>-1.20 = -1.64*S.E.<br><!-- End of picture text -->
- 19 -

and travel distance and increase with the difference in elevation, which is consistent with the above estimation without the fixed effects.

37. One distinct outcome may be that the coefficient associated with driving speed turned out to be positive, not negative, or statistically insignificant. First, it means that there must be unobservable carrier-specific characteristics that systematically affect driving speed, such as fleet age, other technical vehicle attributes and carriers’ driving behavior.

38. Second, there is a possibility that the effect of speed or transport efficiency may be heterogeneous across different types of commodities, thus, offsetting the impact. To examine this possibility, the fixed-effects regression is separately performed for each of the subgroups. It is found that the impact of driving speed is negative and significant for food and other consumer goods ( **Table 7** ). The freight costs for these commodities seem to be particularly sensitive to transport efficiency. For other merchandises, driving speed may not matter.

**<u>Table 5. Freight carrier fixed effects model</u>**

||Linear||Quadratic||Logarithm|ic|
|---|---|---|---|---|---|---|
||Coef.|Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|
|_speed_|0.002|<br>(0.001) **|3.3E-04|(4.0E-03)|||
|_speed_<sup>2</sup>|||9.5E-06|(4.2E-05)|||
|ln_speed_|||||-0.014|(0.009)|
|_dist_|-1.7E-03|(5.9E-05) ***|-1.8E-03|(1.4E-04) ***|||
|_dist_<sup>2</sup>|||3.0E-07|(3.0E-07)|||
|ln_dist_|||||-0.050|(0.002) ***|
|_elev_|1.1E-03|(2.4E-05) ***|2.3E-03|(5.4E-05) ***|||
|_elev_<sup>2</sup>|||-2.2E-06|(7.7E-08) ***|||
|ln_elev_|||||0.050|(0.001) ***|
|_D_elev_|||||-0.239|(0.033) ***|
|_crossborder_|0.192|<br>(0.008) ***|0.172|(0.008) ***|0.037|(0.002) ***|
|constant|3.123|<br>(0.042)***|3.070|(0.097)***|1.214|(0.037)***|
|Obs.|51,137||51,137||51,137||
|R-squared|0.947||0.9479||0.8857||
|F stat.|...||...||...||
|No. of carrier dummy<br>variables|8952||8952||8952||

Note: The dependent variable is freight rate, _p_ . for the first two models, and the log of freight rate, ln _p_ , for the last column model. Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
**<u>Table 6. Estimated elasticity based on freight carrier fixed effects models</u>**

||Linear||Quadratic||Logarithmi|c|
|---|---|---|---|---|---|---|
|Variable|Elasticity|Std. Err.|Elasticity|Std. Err.|<br>Elasticity|Std. Err.|
|_speed_|0.023|(0.009) **|0.014|<br>(0.009)|<br>-0.014|<br>(0.009)|
|_dist_|-0.065|(0.002) ***|-0.063|<br>(0.002)|***<br>-0.050|<br>(0.002) ***|
|_elev_|0.034|(0.001)***|0.053|<br>(0.001)|***<br>0.050|<br>(0.001)***|

*, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

**<u>Table 7. Freight carrier specific fixed effects models by type of commodity</u>**

|Commodity type:|_cloth_|||_equipme_|_nt_||_food_|||_mineral_|||_goods_|||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||Coef.|Std.Err.||
|ln_speed_|-0.012|(0.022)||0.028|(0.027)||-0.028|(0.011)|**|0.082|(0.031)|***|-0.108|(0.038)|***|
|ln_dist_|-0.059|(0.005)|***|-0.044|(0.007)|***|-0.042|(0.003)|***|-0.086|(0.008)|***|-0.085|(0.009)|***|
|ln_elev_|0.081|(0.002)|***|0.054|(0.004)|***|0.026|(0.001)|***|0.085|(0.004)|***|0.053|(0.003)|***|
|_D_elev_|-0.077|(0.049)||-0.257|(0.222)||0.013|(0.015)||0.143|(0.319)||-0.220|(0.021)|***|
|_crossborder_|-0.013|(0.007)|**|0.054|(0.005)|***|0.049|(0.002)|***|0.023|(0.007)|***|0.107|(0.009)|***|
|constant|1.434|(0.094)|***|1.772|(0.113)|***|1.349|(0.047)|***|1.238|(0.117)|***|1.969|(0.148)|***|
|Obs.|10,347|||6,565|||23,458|||4,729|||6,035|||
|R-squared|0.8162|||0.8778|||0.8882|||0.9027|||0.9304|||
|F stat.|...|||...|||...|||...|||...|||
|No. of carrier dummy||||||||||||||||
|variables|1581|||1227|||3548|||998|||1593|||

Note: The dependent variable is freight rate in logarithm, ln _p_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.
39. Another question of interest may be related to heterogeneity between domestic and international shipments. The literature points out an important difference in empty vehicle rate between domestic and international cargos in Europe. To analyze potential structural differences, the sample data is divided by the presence of border crossing, _bordercrossing_ . The results are somewhat mixed but consistently indicate that the estimated impacts of most of the determinants are slightly greater for domestic shipments than international cargos ( **Table 8** ). For instance, domestic shipments generally look more sensitive to shipping speed. The impact of cargo weight is also greater for domestic shipping. All of these effects may provide greater incentive for shipping carriers to consolidate more cargos, reducing empty trucks.

**<u>Table 8. OLS and fixed effects models by border crossing</u>**

||OLS w/o carrier FE|||Carrier F|E models||
|---|---|---|---|---|---|---|
||_crossborder_=0|_crossbo_|_rder_=1|_crossbord_|_er_=0|_crossborder_=1|
||Coef.<br>Std.Err.|Coef.|Std.Err.|Coef.|Std.Err.|Coef.<br>Std.Err.|
|ln_speed_|-0.437 (0.018) ***|-0.330|(0.026) ***|-0.055|(0.015) ***|-0.036 (0.009) ***|
|ln_dist_|-0.011 (0.005) **|-0.283|(0.009) ***|-0.050|(0.003) ***|-0.010 (0.005) *|
|ln_weight_|-0.677 (0.017) ***|-0.652|(0.025) ***||||
|ln_elev_|0.054 (0.002) ***|0.035|(0.003) ***|0.049|(0.001) ***|0.003 (0.001) ***|
|_D_elev_|-0.138 (0.069) **|1.296|(0.144) ***|-0.271|(0.031) ***|0.034 (0.014) **|
|_cloth_|0.095 (0.066)|-0.167|(0.016) ***||||
|_equipment_|0.239 (0.066) ***|0.061|(0.019) ***||||
|_food_|0.141 (0.067) **|-0.202|(0.016) ***||||
|_mineral_|0.206 (0.067) ***|-0.005|(0.020)||||
|_goods_<br>constant|0.108 (0.066) *<br>4.508 (0.099)***|5.273|(0.146)***|1.727|(0.069)***|1.382 (0.039)***|
|Obs.|40,922|10,215||40,922||10,215|
|R-squared|0.3304|0.4838||0.8897||0.9757|
|F stat.|155.17|49.77||...||...|
|No. of time d|ummy variables||||||
||131|131||0||0|
|No. of carrie|r dummy variables<br>0|0||8149||4869|

Note: The dependent variable is freight rate in logarithm, ln _p_ . Robust standard errors are shown in parentheses. *, ** and *** indicate statistical significance at the 10, 5 and 1 percent level, respectively.

40. Finally, one may also be interested in seeing how the observed freight transport costs are spatially distributed and thus, how local economies in the region could benefit from road infrastructure investment. The above-estimated elasticities are important to discuss how they are determined and how they can be reduced. However, there is no spatial dimension. From the data
<!-- Start of picture text --> Origin, destination so<br>—Trunk=roads ere re<br>. J eae oo<br>~ S MO a ye<br>3 > § ba ie<br>he BING eg “ o<br>So hee” .<br>pe D280soo 1,500 2.000<br><!-- End of picture text --> <!-- Start of picture text --> Major cities seo<br>a 2 gers Se ae<br>Q rene ; 5 ta ~ ms N<br>Trunk roads 5 a ay SO AL<br>as 5 ae << SSE.<br>— eyms “ _ & w=ae r<br>“Sek See .<br>i. a o 250 e000, 1,500 2.000<br><!-- End of picture text -->
<!-- Start of picture text --> Transport© Major cities am " u<br>_~ (Siton), 0.1-5.0 2010 costs ar rs« ‘ee|‘ #. —.:5 = ‘ : ed<br>& mm101-150mm151-20.0 * £ 0Ln Q ow¥; & a i. ie<br>_ m Vie :<br>Mm201-71.9 ge ral i‘ i s °<br>OS 7 a v<br>Fy o 250 <0. 1,500 2.000<br><!-- End of picture text -->

<!-- Start of picture text --> a (Siton),ae 2013 oeeee. eS » - i.oe = epi<br>& mTMm10.1-15.0mm151-20.0 e * y | 4 C", . --*; 2 oe = 083, ie<br>201-056 ge car * he ated<br>Try A ca° oa<br>» ai Tea nN<br>i hae s<br>~ lp Oct Qe<br>Fo o 250 <0. 1,500 2.000<br><!-- End of picture text -->

<!-- Start of picture text --> x (Siton), ren01-50 2016 om aa :bai”‘ee eex owe- ‘ : .2b =Simeep.ii my<br>& mm101-150B40 “1. his, . 5“a! 5 Bowee. Pee th x 1 4 ad<br>MM 15.1-25.0 a . | 4 *y . Bt = B<br>M25<br>1-88.7 x ae : a | r “ » -<br>= ee<br>.s: ‘ yt = w E<br>Xe +4, 7 o 250 ooohs ae 00. 4,800 s2,000<br><!-- End of picture text -->

<!-- Start of picture text --> _~ (Siton), TransportSe 2019 coats te!. Se"7 -—: : =on a -<br>_ en if :<br>_<br>mm20.1-96.2 oes < rs Fy , aeamex ©<br>:<br>~ 5 i tes hae aes<br>44, o 250 <0. 1,500 2.000<br><!-- End of picture text --> <!-- Start of picture text --> s uawen ee ct? r 5 .<br>Travellajor timecities(hour) rteES om“ tt ie : fe.: A .<br>00-20 Pe er ‘ee “ag 4 A min<br>&i mme1-a0mTMm41-60 y. 0#, . . +o-- a met ‘08“ te<br>_ Mmb1-228 Mee 7 he ral iHn . J adie a . ‘<br>an, F Rint 1 tee v<br>Fri o 250 <eo0___ 00. 1,500 2.000<br><!-- End of picture text --> <!-- Start of picture text --> x Travel timeOe(hour) eee= eee"7 : _. 2 .<br>&i mTMm41-60mme1-80 a | 4 Le0 . oweit ee b~ 3By te<br>mme1-297 ae T»" & ie oe a<br>i ME ee2 x<br>- ee L oe nN<br>re Bei ptha y<br>oer ag<br>+4) o 250 <eo0___ 00. 1,500 2.000<br><!-- End of picture text -->

<!-- Start of picture text --> Travel® Major time cities(hour) od97 ": ia “7 : -_: = 5<br>00-20 Pt in -_- ee =! ak A a<br>& mm41-60ot 40 ayJie * - a4 Boweos ee: E 1.-<br>p. MTMm61-80 on” a. | 4 * . : 2 = b<br> Mm81-350 - os F | a e ” ee -<br>Oy ELE<br>ms sis ee : « .<br>a \ Faia . v<br>Fri 0 280 <0 %,009. 1,500 2.000<br><!-- End of picture text -->

<!-- Start of picture text --> Travel© Major time cities(hour) ae.== ail : aL -<br>ee eee gy ae ee<br>_ MmB1-308 ae a x * F d .*it 4 =:<br>oa<br>re asp Bei aptha asy<br>+4) o 250 <eo0___ 00. 1,500 2.000<br><!-- End of picture text -->
- 24 -

long discussed that the road freight efficiency has been deteriorated with more empty trucks observed.

44. The paper recast light on the question of how road freight costs are determined using very large, unique shipping data from Eastern Europe and Central Asia. The data covers over 10,000 shipping operators during the period from 2010 to 2020. Freight costs and time, and other basic characteristics were recorded for over 83,000 shipping routes observed.

45. It was found that economies of scale in both volume and distance matter to freight transport costs. The elasticity with respect to freight volume (tonnage) is high at about 0.3 to 1.0. Thus, the load factor is an important element to determine freight costs. The elasticity of distance is relatively small but significant. It varies from 0.04 to 0.16, depending on specification. Longer distance shipping tends to be cheaper. The elasticities also differ across types of commodities.

46. The impact of speed or the quality of roads looks complex and heterogeneous among different types of commodities. According to the OLS results, transport efficiency matters to capital goods, such as equipment, minerals, metals and materials. On the other hand, consumer goods, agricultural commodities and food products are less sensitive to driving speed. However, it seems that there exist important unobservables that affect both driving speed and freight costs. When the carrier-specific fixed-effects are incorporated, it is found that the transport costs decrease with driving speed for particular types of cargos, such as agricultural commodities, food products, and other consumer goods. The elasticities are estimated at 0.03 to 0.11. If observed driving speed is considered as a proxy for road conditions, it can be concluded that the significant road corridor investment in the region contributed to increasing shipping efficiency and lowering average freight costs.

47. There are other important determinants of freight costs, including terrain conditions, such as elevation. In mountainous areas, freight costs tend to be higher. In the region, there is considerable variation in freight costs. Some areas have better accessibility than others. In general, the observed changes in freight costs look relatively modest. Local economies are expected to benefit more from the corridor infrastructure improvement over the long term.