It was found that logistics performance of country’s trading partners affecting to the country’s logistics. This is proven with statistically significant positive spatial correlation values. This research also reveals that logistics performance of a country is affected by its infrastructure such as liner shipping connectivity and road density. Political stability and total tax rate also affecting to the logistics performance. The findings has also shown that the international trade of electronics commodity have high and significant effect on the country’s logistics performance. It can be seen from the analysis where the spatial weighted matrix is constructed based on electronics trading value has higher LPI spatial correlation than the another models where the spatial weighted matrix is constructed based on total trading value, automotive trading value, and agricultural, forestry and fishing trading value.
Further studies should be conducted with considering other factors, such as social factor. The spatial panel model is suggested to be applied in future research to analyze the spatial effect on logistics performance.
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APPENDIX
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Appendix 1 : Indicators in the Logistics Performance Index Survey
The questions in the Logistics Performance Survey delved into the quality of infrastructure, the competence of private and public logistics service providers, the roles of customs and other border agencies, such governance issues as corruption and transparency, and the reliability of the trading system and supply chains. Reliability (measured by the predictability of the clearance process and the timely delivery of shipments) emerged as a key concern, with the difference in satisfaction between the high-and low-performing countries much larger than for any other question in the survey.
Quality of infrastructure
Telecommunications and information technology (IT) infrastructure are an essential component of modern trade processes. The physical movement of goods now entails the efficient and timely exchange of information. In countries in the LPI’s top two quintiles, logistics operators rarely have any issues with the quality of the telecommunications and IT infrastructure, but close to half of them express concerns in countries ranging from average to lowest performers.
The quality of transport infrastructure remains a concern in close to or more than half of the logistics operators in average, low, and lowest performers. That concerns also exist in even the highest and high per-forming countries reflects the challenge of maintaining physical infrastructure at a level able to satisfy rapidly growing demands.
Competence of private and public logistics service providers
The performance of the supply chain depends on the quality of services delivered by the private sector through customs brokers and road transport operators and on the competence and diligence of public agencies in charge of border procedures. In these areas, the three bottom quintiles generally fare much worse than the top quintile, and the differences in quality are as significant as those for infrastructure. For example, the satisfaction with customs brokers is fairly high for the upper-middle-income countries (around 50 percent), but it is only 8 percent for private providers in sub-Saharan Africa.
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For the lower performers, the dissatisfaction with the quality of trade logistics services applies to both the private and public sectors. In those countries where logistics performance is high, there is more satisfaction with private providers than with public providers. The negative view of private providers in the lower performers is an important insight. Too often in developing countries, and notably in Africa, inadequate regulations and the absence of competition lead to corruption or poor services such as those provided by “suitcase businessmen” at border posts. Often the mere presence of these operators disturbs the clearance process and hinders the emergence of competent local logistics operators who can work with international operators.
Customs and other border agencies
Clearance at the border is not only a matter of customs diligence. Law enforcement agencies and ministries of agriculture and industry also intervene in the process. Customs performance tends to be better than that of other border agencies; on average, customs clearance accounts for a third of import time. This underscores the importance of addressing the coordination of border agencies, especially in countries that already have attained good customs clearance.
Corruption and transparency
Logistics performance also depends on broader policy dimensions, including the overall business environment, the quality of regulation for logistics services, and, most important, overall governance. The way the local market for logistics services is regulated directly affects a country’s ability to use the physical internet to connect to global markets. The transparency of government procurement, the security of property from theft and looting, macroeconomic conditions, and the underlying strength of institutions are critical factors in determining logistics performance. Unsurprisingly, ratings of the domestic environment in such areas as corruption and the transparency of processes and regulation reflect these findings. The rating for transparency of border processes consistently declines along with LPI scores for the following groups of countries:
poor performers in the LPI were also poor performers on transparency of border processes. Solicitation of informal payments is rare among the top 30 countries but common among lower performers.
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Reliability of the trading system and supply chains
For traders at the origin or the destination of the supply chain, what matters most is the quality and reliability of logistics services, measured by the predictability of the clearance process and timely delivery of shipments to destination. The difference in satisfaction between the high-and low-performing countries on this question is much larger than for any other question in the survey.
Performance data derived from the survey on the time (in days) for delivery of goods confirms the same phenomenon.
Taken together, all these factors quality of infra-structure, the competence of private and public logistics service providers, the roles of customs and other border agencies, governance issues such as corruption and transparency, and the reliability of the trading system and supply chains confirm once again that logistics performance is about predictability. Predictability is central to the overall costs that companies incur in logistics and thus to their competitiveness in global supply chains.
Appendix 2 : The Moran index
The moran index is used as consideration in the selection of those three methods. In the spatial analysis moran index used as a tool that was first used to investigate whether there is spatial effect in the data. Moreover, in this case the moran index can be used to see how well these three methods in mapping the Indonesia’s trading partner countries.
The mapping performance comparison of three rank method
Rank method based Moran index
Trading value 0.050
Inverse trading 0.122
Inverse proportion of trading value 0.321
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Figure : The moran index comparison among three rank methods
The table and figure above show that the ranking method based on inverse proportion of trading value has the highest moran index. This also indicate that this method is better in mapping the Indonesia’s trading partner countries. Thus, the ranking method based on inverse proportion of trading value is chosen as a method in defining countries coordinates.
Appendix 3 : Countries included in the analysis
Australia, Belgium, Brazil, Bulgaria, Canada, Chile, China, Czech Republic, Denmark, Finland, France, Germany, Greece, Hong Kong, India, Indonesia, Italy, Japan, Korea, Latvia, Malaysia, Mexico, Netherland, New Zealand, Norway, Philippines, Poland, Portugal, Romania, Russian, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, United Kingdom, United States, Vietnam.
Appendix 4 : The countries coordinates
The table below presents the countries coordinates based on inverse proportion of trading value. The export data was used as x-axis coordinate and the import data used as y-axis coordinate. Indonesia, used as a reference country, is given coordinate of (0,0).
Then calculate either the inverse of export and import proportion for other 41 countries to get the countries coordinates.
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The countries coordinates
Denmark 444.36 525.83 Netherlands 163.4 41.1 Switzerland 253.5 406.5 Finland 319.10 936.42 New Zealand 213.0 353.2 Thailand 15.7 27.0 France 67.87 117.14 Norway 607.8 1976.0 Turkey 316.8 117.1
Germany 37.17 46.37 Philippines 205.1 50.7 UK 113.8 82.6
Greece 794.06 760.21 Poland 787.2 441.8 USA 13.3 10.3
Hong Kong 94.17 60.18 Portugal 1105.0 1109.7 Viet Nam 73.7 77.4
Note: x : inverse of Indonesia’s export proportion to that country y : inverse of Indonesia’s import proportion from that country
Appendix 5 : The statistics summary of variables included in the model
Variable Minimum Maximum Mean Standard
Deviation
LPI 2.370 4.190 3.502 0.421
LPI Customs 1.940 4.208 3.274 0.517
LPI Infrastructure 2.230 4.336 3.459 0.574
LPI International shipments 2.479 4.049 3.349 0.329 LPI Logistics competence 2.457 4.316 3.486 0.461
LPI Tracking 2.174 4.273 3.562 0.433
LPI Timeliness 2.943 4.529 3.895 0.372
Liner shipping connectivity 0.684 2.194 1.536 0.356
Road density 0.751 2.703 1.723 0.516
Quality of port 2.600 6.831 4.850 1.162
quality of road 2.059 6.663 4.725 1.378
Time to Export 6.000 25.000 13.119 5.160
Total tax rate 14.500 81.200 42.886 13.666
Political Stability 0.405 0.920 0.711 0.094
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Appendix 6 : The predictors that have no spatial correlation to LPI
The independent variable which does not have spatial correlation to LPI Variable Cofficient
The table above shows that when the spatial regression model did not include any independent variable in the model, the spatial coefficient is 0.1076, and it value is significant with p.value 0.000. The time to export, quality of port, and quality of road variable evidently make the spatial correlation decrease and become not significant.
When the time to export variable included to the model, the spatial correlation is 0.0577 with the p.value 0.092 (not significant). Similarly for for both the quality of port and quality of road variable, the spatial correlation value drops to 0.0332 and 0,030 respectively. As for this results, those three variables will not be included into the model due to the fact that those variables make the spatial correlation of LPI become not significant.
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Appendix 7 : The result of spatial regression model where spatial weighted matrix constructed based on electronic trading value
variable
CUS: custom, INF: infrastructure, INT: international shipment, LOG: logistics competence, TRA:
tracking and tracing, TIM: timeliness
*) significant at =10%, **) significant at =5%, ***) significant at =1%
Appendix 8 : The result of spatial regression model where spatial weighted matrix constructed based on automotive trading value
variable
CUS: custom, INF: infrastructure, INT: international shipment, LOG: logistics competence, TRA:
tracking and tracing, TIM: timeliness
*) significant at =10%, **) significant at =5%, ***) significant at =1%
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Appendix 9 : The result of spatial regression model where spatial weighted matrix constructed based on agriculture, forestry, and fishing trading value
variable
dependent variable
LPI LPI
CUS
LPI INF
LPI INT
LPI LOG
LPI TRA
LPI TIM coeff. coeff. coeff. coeff. coeff. coeff. coeff.
constant 2.988*** 2.690*** 2.529*** 3.183*** 2.946*** 2.840*** 3.689***
Liner Shipping Connect
0.054** 0.057** 0.136*** 0.201** 0.039** 0.108** 0.028*
Political Stability
0.364*** 0.539*** 0.653*** 0.194** 0.322** 0.501** 0.024*
Total tax rate -0.005*** -0.009*** -0.006** -0.003** -0.002** -0.002** -0.003***
Road Density 0.202*** 0.268*** 0.293*** 0.155*** 0.198*** 0.162** 0.164**
Spatial correlation ()
0.0030*** 0.0031*** 0.0035*** 0.0033*** 0.0032*** 0.0031*** 0.002***
CUS: custom, INF: infrastructure, INT: international shipment, LOG: logistics competence, TRA:
tracking and tracing, TIM: timeliness
*) significant at =10%, **) significant at =5%, ***) significant at =1%
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