PPTX Intro

Logistics Performance of Countries and Its Relationship with Economic Growth:

A Panel Data Analysis

PAMELA F. RESURRECCION, PHD

Mindanao State University – Iligan Institute of Technology

Philippines

The Link between Logistics and Economic Growth

Continuous world merchandise trade volume expansion of 3.9%

Global gross domestic product (GDP) growth of 3.1% at market

exchange rates

(World Trade Organization, 2018)

Logistics: Macro-level Perspective

L O G IS T IC S Economic Growth Competitiveness

Productivity Exports

(Chu, 2012; Sánchez, Tomassian, &

Perrotti, 2014; D’Aleo, 2015; D’Aleo &

Sergi, 2017)

(Serhat & Harun, 2011; D’Aleo &

Sergi, 2017)

(Coto-Millán, Fernández, Pesquera, &

Agüeros, 2016)

(Puertas, Marti, & Garcia, 2014)

Studies on Logistics Performance

 Some studies on logistics performance and economic growth involves the use of the aggregate scores of the Logistics Performance Index (LPI) developed by the World Bank in 2007 (D’Aleo, 2015; D’Aleo &

Sergi, 2017)

 There are studies that used the scores of each dimension of LPI pertaining to its relationship with exports (Puertas, Marti, & Garcia, 2014) and competitiveness (Serhat & Harun, 2011).

 Other studies on logistics and economic growth utilized relevant

indicators in the Global Competitiveness Index (GCI) to measure

logistics performance (Sánchez, Tomassian, & Perrotti, 2014) and

logistics investment (Chu, 2012)

How can countries improve economic growth through purposive policies and

interventions in logistics?

Research Objectives

 To investigate which among the dimensions of the LPI are significant predictors of economic growth; and

 To determine if the LPI dimensions, together with

manufacturing value added and inflation as control variables,

are good predictors of economic growth.

Logistics Performance Index (LPI)

Efficiency of customs and

border management

clearance

Quality of trade and transport infrastructur

e

Ease of arranging competitively

priced shipments

Competence and quality of logistics

services

Ability to track and

trace consignment

s

Frequency of shipments

reaching consignees

on time

H

₁

H

₂

H

₃

H

₄

H

₅

H

₆

Model Specification

����= � ( ���� , ����� , ����� , ���� , ���� , ���� , ����� , ��� )

(1)

(2)

Model Specification

where,

PROD = Log of Annual Gross Domestic Product (GDP) at constant 2010 US$

CUST = The efficiency of customs and border management clearance INFRA = The quality of trade and transport infrastructure

PRICE = The ease of arranging competitively priced shipments

QUAL = The competence and quality of logistics services such as trucking, forwarding, and customs brokerage TRTR = The ability to track and trace consignments

TIME = The frequency with which shipments reach consignees within schedule of expected delivery times MFGVA= Log of Manufacturing Value Added at constant 2010 US$

INF = Log of inflation

Data Description and Sources

 A total of 491

observations representing 146 cross-sectional units with four years data each covering the years 2010, 2012, 2014, and 2016

were used in the analysis.

World Bank Database

• Logistics Performance Index

• GDP

• Manufacturing, Value Added

• Inflation

Descriptive Statistics

Variable Year N Missing Median Mean Standard

deviation

Skewness Shapiro-Wilk p-value

CUST 2010 155 12 2.38 2.59 0.617 0.760 <0.001

2012 155 12 2.51 2.66 0.577 0.698 <0.001

2014 160 7 2.58 2.73 0.595 0.578 <0.001

2016 160 7 2.59 2.71 0.635 0.456 <0.001

INFRA 2010 155 12 2.44 2.64 0.732 0.737 <0.001

2012 155 12 2.60 2.77 0.670 0.585 <0.001

2014 160 7 2.57 2.77 0.662 0.693 <0.001

2016 160 7 2.58 2.75 0.720 0.590 <0.001

PRICE 2010 155 12 2.83 2.85 0.470 -0.125 0.266

2012 155 12 2.76 2.82 0.512 0.209 0.143

2014 160 7 2.81 2.86 0.492 0.00452 0.028

2016 160 7 2.76 2.87 0.574 0.275 0.001

QUAL 2010 155 12 2.59 2.76 0.636 0.646 <0.001

2012 155 12 2.73 2.82 0.591 0.558 <0.001

2014 160 7 2.74 2.85 0.583 0.515 <0.001

2016 160 7 2.67 2.82 0.645 0.508 <0.001

TRTR 2010 155 12 2.79 2.92 0.650 0.331 <0.001

2012 155 12 2.77 2.88 0.614 0.384 <0.001

2014 160 7 2.83 2.90 0.581 0.375 0.002

2016 160 7 2.71 2.86 0.700 0.366 <0.001

Descriptive Statistics

Variable Year N Missing Median Mean Standard

deviation

Skewness Shapiro-Wilk p-value

TIME 2010 155 12 3.39 3.41 0.575 -0.160 0.068

2012 155 12 3.19 3.26 0.555 0.125 0.011

2014 160 7 3.16 3.25 0.587 0.311 0.001

2016 160 7 3.23 3.27 0.620 0.195 0.018

GROWTH 2010 166 1 37,844 393,398 1.40e+6 7.88 <0.001

2012 165 2 40,739 418,313 1.47e+6 7.71 <0.001

2014 165 2 43,796 441,572 1.56e+6 7.60 <0.001

2016 165 2 46,592 460,908 1.65e+6 7.54 <0.001

MFGVA 2010 150 17 5.22e+9 6.92e+10 2.44e+11 6.22 <0.001

2012 147 20 5.42e+9 5.96e+10 1.95e+11 6.80 <0.001

2014 147 20 5.79e+9 6.21e+10 2.02e+11 6.78 <0.001

2016 143 24 6.36e+9 6.57e+10 2.10e+11 6.62 <0.001

INF 2010 158 9 3.71 4.59 3.99 1.86 <0.001

2012 158 9 3.80 5.90 7.08 4.17 <0.001

2014 155 12 2.92 4.38 6.87 5.03 <0.001

2016 151 16 1.73 5.16 21.0 11.3 <0.001

Estimation Technique

 Hausman Test: Fixed Effects

 Test of Panel Regression Assumptions

o The test for normality of residuals resulted to a Chi-square (2) = 874.305 (p-value

< 0.0001) hence, the null hypothesis that errors were normally distributed was rejected.

o The Wald test for heteroskedasticity yielded a Chi-square (139) = 1.02746

⁰²⁷

(p- value = 0) hence, the null hypothesis that the units have a common error variance was rejected.

o The Wooldridge test for autocorrelation in panel data was used to test the null hypothesis of no first-order autocorrelation. Results (F (1,107) = 55.584, p- value < 0.0001) indicate that the null hypothesis should be rejected.

o Panel-corrected standard errors (PCSE) estimation technique was used.

Panel Data Regression Results

Variables Coefficient Het-corrected Std. Error

z p-value

Const -3.2608440 0.1031973 -31.60 0.000 ***

CUST -0.0271595 0.0348441 -0.78 0.436

INFRA 0.0928585 0.0350921 2.65 0.008 **

PRICE -0.0079313 0.0222385 -0.36 0.721

QUAL -0.0096846 0.0360117 -0.27 0.788

TRTR 0.0189234 0.0318955 0.59 0.553

TIME 0.0146973 0.0210471 0.70 0.485

MFGVA 0.7943215 0.0118965 66.70 0.000 ***

INF 0.0311979 0.0143275 2.18 0.029 *

*Significant at α=0.05 **Significant at α=0.01 ***Significant at α=0.001

R

²

= 0.9942; Wald chi

²

(8) = 9034.47 (p>chi

²

= 0.0000)

Quality of infrastructure as significant driver of growth

 A strong transport infrastructure that are well-connected are essentials to efficient transport systems.

 It enhances current interactions among economic entities as well as provides opportunities for new economic relations.

 It also contributes to the timeliness dimension of logistics performance given that there is ample capacity and

connectivity among the trade and transport infrastructure.

Quality of infrastructure as significant driver of growth

 In addition, optimum utilization of resources allows for an expansion of markets which consequently results to

economies of scale in production, distribution and consumption.

 Finally, due to increased economic activities in various

locations, the benefits of quality trade and transportation

infrastructure spills over to an increase in land valuation

(Rodrigue, 2017) as more enterprises and more economic

activities emerge.

Effects of Control Variables Confirmed

Manufacturing, Value Added

Inflation

Economic Growth

Sola, Obamuyi, Adekunjo, and Ogunleye (2013); Tsoku, Mosikari, Xaba, and Modise (2017).

Mallik and Chowdhury (2001);

Osuala, Osuala, and Onyeike (2013); and de Carvalho, Ribeiro, and Marques (2017)

(+)

(+)

Conclusion

 The goal of this study was to determine the relationship between logistics performance and economic growth.

 Specifically, the study aimed to determine which of the LPI components are significant predictors of economic growth.

 Results confirm the main thesis that logistics performance influence economic growth.

 Findings indicate that for the year 2010, 2012, 2014, and 2016, only

quality of trade and transportation infrastructure is a significant factor

positively affects economic growth.

Policy Recommendations

 Governments should continue to deliberately expand their infrastructures that facilitate trade.

 This does not only address the basic need of logistics but allows for other positive externalities such as increase in employment

opportunities hence promoting inclusive growth and development.

 Governments should also develop human capital to acquire the needed competencies to manage the complexities of logistics and trade. This is based on the premise that infrastructure, by themselves, do not move goods and services. Logistics has to be complemented with

management expertise (Rodrigue, 2017).

Future Research

 Investigate the relationship between LPI dimensions and

economic growth in each group of countries belonging to the same income classification.

 The mediating role of manufacturing productivity in the logistics performance and economic growth nexus could also be

examined.

 The interrelationships of the six LPI dimensions could be

explored.