ASSESSING A COUNTRY'S SECTOR-SPECIFIC LOGISTICS PERFORMANCE: THE CASE OF INDIA'S MARINE-PRODUCT SECTOR

Satyendra Nath  Chakrabartty; Deepankar  Sinha

doi:10.46754/jml.2022.12.004

Authors

Satyendra Nath Chakrabartty Indian Ports Association & Indian Maritime University
Deepankar Sinha Indian Institute of Foreign Trade, Kolkata Campus

DOI:

https://doi.org/10.46754/jml.2022.12.004

Keywords:

Logistics performance index, sector-specific logistics performance index, battery of scales, elasticity, marine sector

Abstract

Country-wise Logistics Performance Index (LPI) is insufficient to guide changing policies for different sectors with varied logistics requirements and perspectives. Each perspective has various measures, and hence a battery of scales is mandated to measure the performance for an individual sector like marine, agriculture, and similar. For the marine-product sector of India, scores are transformed and combined to follow normal distributions enabling parametric analysis. A method of sector-specific logistics performance index (LPI-S) is proposed addressing multi-dimensional, multi-scale response categories satisfying the desired properties of an index. An empirical illustration is given to assess LPI-S for the marine-product sector in India, combining responses of 141 Indian marine exporters in a battery with nine dimensions. The proposed method generates continuous, monotonic data, and distributions of dimension/battery scores are normal. The LPI-S scores have better arithmetic aggregation admissibility, even if lengths of dimensions are different. In addition, it identifies critical dimensions, detects changes by longitudinal data, and dimension-wise elasticity reflecting the sensitivity of the dimension from snap-shot data. Irrespective of dimensions and types of data, the proposed methodology uses the sensitivity of a dimension on LPI-S to help policy makings separately for individual categories to improve logistics efficiency. The study identified eight crucial dimensions associated with marine product logistics. The sensitivity of these dimensions in the descending order of importance were - Information system, Regulatory process, Safety & Security issues, Timeliness and Completeness efficiency, Sustainability in logistics, Operating conditions, Logistics facility pricing, Quality of Logistic services, Transportation Networks and Logistics infrastructure. Such ordering of dimensions help in deciding policy priorities.

References

Arvis, J. F., Saslavsky, D., Ojala, L., Shepherd, B., Busch, C., Raj, A., & Naula, T. (2016). Connecting to compete: Trade logistics in the global economy. Washington DC: World Bank DOI: https://doi.org/10.1596/24598

Bastien, C. H., Vallieres, A., & Morin, C. M. (2001). Validation of the insomnia severity index as an outcome measure for insomnia research. Sleep Medicine, 2(4), 297–307. DOI: https://doi.org/10.1016/S1389-9457(00)00065-4

Beysenbaev, R. (2018). The importance of country-level logistics efficiency assessment to the development of international trade. British Journal for Social and Economic Research, 3(6), 13–20. DOI: https://doi.org/10.22406/bjser-18-3.6-13-20

Bound, J., Brown, Ch., & Mathiowetz, N. (2000). Measurement Error in Survey Data (Report No. 00-450). Population Studies Center at the Institute for Social Research University of Michigan

Bradburn, N. M., Sudman, S., & Wansink, B. (2004). Asking questions: The definitive guide to questionnaire design—for market research, political polls, and social and health questionnaires. San Francisco: Jossey-Bass

Chakrabartty, S. N. (2020a). Logistics Performance Index: Methodological Issues. Foreign Trade Review, 55(4), 466-477. https://doi.org/10.1177/0015732520947860 DOI: https://doi.org/10.1177/0015732520947860

Chakrabartty, S. N. (2020b). Reliability of Test Battery. Methodological Innovation. 13(2), 1-8. DOI: https://doi.org/10.1177/2059799120918340

Chakrabartty, S. N. (2021). Optimum number of response categories. Current Psychology. https://doi.org/10.1007/s12144-021-01866-6 DOI: https://doi.org/10.1007/s12144-021-01866-6

Chakrabartty, S. N. (2022). Understanding national level logistics costs: Methodological approach. Journal of Asian Economic Integration, 195-207. https://doi.org/10.1177/26316846221107419 DOI: https://doi.org/10.1177/26316846221107419

Christopher, M. (2016). Logistics & supply chain management. UK: Pearson

Coto-Millán, P., Agüeros, M., Casares-Hontañón. P., & Pesquera, M. Á. (2013) Impact of logistics performance on world economic growth (2007–2012). World Review of Intermodal Transportation Research, 4(4), 300-310. DOI: https://doi.org/10.1504/WRITR.2013.059857

Dawes, J. G. (2002). Five-point vs eleven-point scales: Does it make a difference to data characteristics? Australasian Journal of Market Research, 10(1), 39–47.

Devlin, J., & Yee, P. (2005). Trade logistics in developing countries: The case of the Middle East and North Africa. The World Economy, 28, 435–456. DOI: https://doi.org/10.1111/j.1467-9701.2005.00620.x

Dua, A., & Sinha, D. (2019). Quality of multimodal freight transportation: A systematic literature review. World Review of Intermodal Transportation Research, 8(2), 167-194. DOI: https://doi.org/10.1504/WRITR.2019.10020315

Duˇsko, P., & Boˇzica, R. (2016) The impact of transport on international trade development. Acta Economica Et Turistica, 2(1), 13–28 DOI: https://doi.org/10.1515/aet-2016-0002

Ferrando, P. J. (2003) A kernel density analysis of continuous typical-response scales. Educational and Psychological Measurement, 63, 809-824 DOI: https://doi.org/10.1177/0013164403251323

Flora, D. B., & Curran , P. J. (2004) An empirical evaluation of alternative methods of estimation for confirmatory factor analysis with ordinal data, Psychological Methods, 9(4), 466– 491. https://doi.org/10.1037/1082-989X.9.4.466 DOI: https://doi.org/10.1037/1082-989X.9.4.466

Gani, A. (2017) The logistics performance effect in international trade. The Asian Journal of Shipping and Logistics, 33(4), 279–288. DOI: https://doi.org/10.1016/j.ajsl.2017.12.012

Göçer, A., Özpeynirci, Ö., & Semiz, M. (2021) Logistics performance index-driven policy development: An application to Turkey. Transport Policy. DOI: https://doi.org/10.1016/j.tranpol.2021.03.007

Gu, Peter & Wen, Q. & Wu, D. (1995) How often is often? Reference ambiguities of the Likert-scale in language learning strategy research. Occasional Papers in English Language Teaching, 5, 19-35.

Hand, D. J. (1996) Statistics and the theory of measurement, Journal of the Royal Statistical Society Series A, 159(3), 445-492. https://doi.org/10.2307/2983326 DOI: https://doi.org/10.2307/2983326

Harwell, M. R. & Gatti, G, G. (2001) Rescaling ordinal data to interval data in educational research. Review of Educational Research, 71(1), 105–131. https://doi.org/10.3102/00346543071001105 DOI: https://doi.org/10.3102/00346543071001105

Hausman, W., Lee, H. L. & Subramanian, U. (2005) Global logistic indicators, supply chain metrics and bilateral trade patterns. Washington D.C: World Bank Policy Research Working Paper 3773, DOI: https://doi.org/10.1596/1813-9450-3773

Jhawar, A., Garg, S. K., & Khera, S. N. (2017) Improving logistics performance through investments and policy intervention: A causal loop model. International Journal of Productivity and Quality Management, 20(3), 363–391. DOI: https://doi.org/10.1504/IJPQM.2017.10003289

Ji, Y., Yang, H., & Chen, M. (2017) Logistics network configuration for fresh agricultural products. In 2017 29th Chinese Control and Decision Conference (CCDC) (pp. 5724-5727). IEEE. DOI: https://doi.org/10.1109/CCDC.2017.7978187

Johnston, B. T. & Sheehy, T. P., (1995) The Index of Economic Freedom. Washington: Heritage Foundation, pp. ix-21

Lee, J. A. & Soutar, G. N. (2010). Is Schwartz's value survey an interval scale, and does it really matter? Journal of Cross-Cultural Psychology, 41, 76-86 DOI: https://doi.org/10.1177/0022022109348920

Lim, Hock-Eam (2008) The use of different happiness rating scales: Bias and comparison problem? Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 87, 259–267 DOI: https://doi.org/10.1007/s11205-007-9171-x

Livingston, S. A. (2004) Equating test scores (without IRT). Princeton, NJ: ETS.

Martí, L., Martín, J. C., & Puertas, R. (2017) A DEA-logistics performance index. Journal of applied economics, 20(1), 169-192. DOI: https://doi.org/10.1016/S1514-0326(17)30008-9

Martí, L., Puertas, R., & García, L. (2014) The importance of the Logistics Performance Index in international trade. Applied economics, 46(24), 2982-2992. DOI: https://doi.org/10.1080/00036846.2014.916394

Mellat-Parast, M. & Spillan, J. E. (2014) Logistics and supply chain process integration as a source of competitive advantage, International Journal of Logistics Management, 25(2), 289-314. DOI: https://doi.org/10.1108/IJLM-07-2012-0066

Ministry of Commerce and Industry Government of India. (2019). Logistics Ease Across Different States (LEADS). https://commerce.gov.in/wp-content/uploads/2020/08/MOC_637051086790146385_LEAD_Report-2.pdf

OECD. (2002). An Update of the OECD Composite Leading Indicators. Short-term economic Statistics division, Statistics Directorate/OECD, https://www.oecd.org/sdd/leading-indicators/2410332.pdf

OECD. (2020). OECD Services Trade Restrictiveness Index: Policy trends up to 2020. https://www.oecd.org/trade/topics/services-trade/documents/oecd-stri-policy-trends-up-to-2020.pdf

Pohit S., Gupta. D. B., Pratap. D., Alawadhi. A., Sayal. L., Malik. S. (2019). Analysis of India’s Logistics Costs. National Council of Applied Economic Research (NCAER). https://www.ncaer.org/publication/analysis-of-india039s-logistics-costs, accessed on 10.11.2022.

Preston, Carolyn C., & Colman, A. M. (2000). Optimal number of response categories in rating scales: Reliability, validity, discriminating power, and respondent preferences, Acta Psychologica, 104, 1-15 DOI: https://doi.org/10.1016/S0001-6918(99)00050-5

Ray, A. K. (2008). Measurement of social development: An international comparison. Social Indicators Research, 86(1), 1–46. DOI: https://doi.org/10.1007/s11205-007-9097-3

Rezaei, J., Van R., W. S., & Tavasszy, L. (2018) Measuring the relative importance of the logistics performance index indicators using Best Worst Method. Transport Policy, 68, 158-169. DOI: https://doi.org/10.1016/j.tranpol.2018.05.007

Rievaj, V., Vrábel, J., Synák, F., & Bartuška, L. (2018). The effects of vehicle load on driving characteristics. Advances in Science and Technology. Research Journal, 12(1), 142-149. https://doi.org/10.12913/22998624/80896 DOI: https://doi.org/10.12913/22998624/80896

Rüschendorf, L. (2013). Mathematical Risk Analysis.Dependence, Risk Bounds, Optimal Allocations and Portfolios. Heidelberg: Springer. MR-3051756 DOI: https://doi.org/10.1007/978-3-642-33590-7_10

Schönsleben, P. (2018). Integral logistics management: Operations and supply chain management within and across companies (4th ed.). Auerbach–Taylor & Francis. DOI: https://doi.org/10.4324/9781315368320

The World Bank. (2018). International LPI. https://lpi.worldbank.org/international/global

Van Der Eijk, C., & Rose, J. (2015) Risky business: Factor analysis of survey data - assessing the probability of incorrect dimensionalisation. PloS one, 10(3), e0118900. https://doi.org/10.1371/journal.pone.0118900 DOI: https://doi.org/10.1371/journal.pone.0118900

Wang, R. (2014). Sum of arbitrarily dependent random variables. Electronic Journal of Probability, 19(84), 1 - 18. DOI: https://doi.org/10.1214/EJP.v19-3373

World Economic Forum. (2016) The global enabling trade report 2016. https://www.weforum.org/reports/the-global-enabling-trade-report-2016/

Wu, C. H. (2007). An empirical study on the transformation of likert scale data to numerical scores, Applied Mathematical Sciences, 1(58), 2851 – 2862.

Zahedian, A. & Saba, R. A. (2016) Measurement Error Estimation Methods in Survey Methodology. Applications and Applied Mathematics. 11(1), 97-114.