Measuring the importance of the Global Supply Chain for economic development based on diachronic data of the Logistics Performance Index (LPI)
Description
Research Hypotheses Our research focuses on understanding the relationship between the Logistics Performance Index (LPI) and GDP per capita across 169 countries over a span of 16 years, from 2007 to 2023. The hypothesis driving this study is twofold: 1. Primary Hypothesis: There is a statistically significant and positive correlation between a country’s LPI and its GDP per capita. This hypothesis is grounded in the understanding that higher logistics performance, as measured by the LPI, reflects more efficient trade and economic activities, which in turn should contribute to higher economic outputs and, therefore, higher GDP per capita. 2. Secondary Hypothesis: Among various indicators related to supply chain efficiency, the LPI is the most accurate predictor of GDP per capita. To test these hypotheses, we compiled a dataset that includes annual LPI scores, other supply chain performance variables and GDP per capita values for 169 countries from 2007 to 2023 Data Analysis The data was analyzed using SPSS, a statistical software package widely used in academic research. The analysis involved several key steps: • Correlation Analysis: Pearson correlation coefficients were calculated to assess the strength and direction of the relationship between LPI and GDP per capita across different countries and years. • Multivariate Linear Regression: A series of regression models were developed to determine the extent to which LPI and other logistics-related variables predict GDP per capita. • Proximity to Mean Analysis: This analysis was conducted to evaluate the consistency of the relationship between LPI and GDP per capita across different contexts, identifying variables that showed a stable and significant impact on economic outcomes. How to Interpret the Data The dataset provides a comprehensive view of the relationship between logistics performance and economic output. When analyzing the data, the following points should be considered: 1. Correlation Between LPI and GDP per Capita: o The data reveals a strong positive correlation between the LPI and GDP per capita, indicating that countries with higher logistics performance tend to have higher economic output per person. This suggests that improvements in logistics efficiency, such as better customs procedures and infrastructure, can significantly contribute to economic growth. 2. Significance of LPI Components: o Each component of the LPI plays a role in the overall score, and understanding these components can provide deeper insights into specific areas of strength or weakness in a country’s logistics performance. 3. Impact of External Factors: o The data spans a period that includes significant global events, such as the 2008 financial crisis and the COVID-19 pandemic. These events may cause noticeable fluctuations in both LPI scores and GDP per capita.
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Data Source The data for this research was sourced from the World Bank's World Development Indicators (WDI) database . This database provides a wide array of global development data, including economic indicators, logistics performance metrics, and other relevant variables for analysis. Data Collection Process 1. Selection of Variables: o Logistics Performance Index (LPI): The LPI was selected as the primary variable representing logistics efficiency. The LPI scores were obtained from the World Bank's LPI reports, which are available biennially. o GDP per Capita: This economic indicator was used as a measure of a country's economic well-being and was sourced directly from the World Development Indicators database. o Other Supply Chain Variables: Additional variables related to supply chain performance, such as customs efficiency, infrastructure quality, and shipment timelines, were also gathered from the same database. 2. Time Frame: o The data covers a period from 2007 to 2023, enabling a longitudinal analysis of the relationship between LPI and GDP per capita. 3. Geographical Scope: o The dataset includes data from 169 countries, providing a broad and diverse sample to analyze global trends and correlations. Methods and Protocols 1. Data Extraction: Software: The data was initially extracted using Excel, where it was organized and cleaned before importing into SPSS for further analysis. 2. Data Analysis Using SPSS: Importing Data: The cleaned dataset was imported into SPSS for statistical analysis. Correlation Analysis: Pearson correlation coefficients were calculated to assess the strength and direction of the relationship between LPI and GDP per capita. Multivariate Linear Regression: SPSS was used to perform multivariate linear regression analyses. This involved setting LPI as the independent variable and GDP per capita as the dependent variable, with additional supply chain variables included as covariates. Proximity to Mean Analysis: This analysis was conducted using EXCEL to determine how closely various predictors align with the mean effect, confirming the robustness of LPI as a key variable. Reproducibility 1. Data Accessibility: o All data used in this research is publicly available through the World Bank's online databases. The dataset can be reproduced by querying the same indicators and time periods from the World Bank's database and importing them into SPSS. Instruments and Software: Excel: Used for initial data cleaning and organization before import into SPSS. SPSS: The primary software used for statistical analysis, including descriptive statistics, correlation analysis, multivariate regression, and proximity to mean analysis.