The presented study makes analytical use of secondary data covering the period from 1975 to 2022. World Development Indicators (WDI) was the primary source for GDP, secondary enrollment rate, and gross fixed capital formation (GFCF) data. The World Bank was a supplementary source for poverty data which was interpolated due to its availability on a 5 yearly basis, specifically the rate of population below the poverty line at 2.15 dollars.
Research Design:
While numerous studies have explored the relationship between GDP and various economic factors, limited attention has been given to analyzing the specific dynamics between GDP, poverty, education, and physical capital, particularly within developing countries. Although some studies have examined these relationships in European contexts, there exists a research gap concerning their prominence within developing nations.
This study aims to investigate the impact of poverty, education, and physical capital on GDP, focusing primarily on developing countries that underwent significant socioeconomic transformations. We adopt a methodological approach that incorporates various models to elucidate these relationships. Initially, we employ a standard linear regression model to examine the association between GDP and poverty, education, and physical capital individually. Subsequently, we enhance the model's explanatory power by incorporating all variables simultaneously in a multivariate regression analysis.
By utilizing these models, we seek to uncover the nuanced relationships between GDP, poverty, education, and physical capital in developing countries, considering the influence of various socioeconomic factors. Our analysis aims to contribute to a deeper understanding of the mechanisms driving economic growth and development in these contexts, offering insights for policymakers and practitioners striving to alleviate poverty, improve education, and enhance physical capital investments for sustainable economic progress.
Data Collection:
GDP Data: Gathered from the World Development Indicators (WDI) database for the desired countries and years. GDP figures are expressed in constant 2011 US dollars to adjust for inflation.
Education Data (Secondary Enrollment Rate): Retrieved from the WDI database for the corresponding countries and years. Secondary enrollment rate is expressed as a percentage of the secondary school-aged population enrolled in secondary education.
Physical Capital Data (GFCF): Obtained from the WDI database for India and the required time periods. GFCF figures are expressed as a percentage of GDP.
Poverty Data: Sourced from government agencies responsible for collecting poverty-related statistics. Poverty rate data is represented as the percentage of the population living below the poverty line, specifically at 2.15 US dollars per day. Due to its availability on a 5 yearly basis, data was interpolated to get rough estimates between 1975 and 2022.
Model Specification and Statistical Analysis:
Model A:
The model specification is as follows:
GDP = β₀ + β₁(Education) + β₂(Physical Capital) + β₃(Poverty) + ε
GDP: Gross Domestic Product, representing the economic output of a country.
Education: Secondary enrollment rate or another proxy for educational attainment.
Physical Capital: Gross Fixed Capital Formation (GFCF) or another proxy for investment in physical infrastructure.
Poverty: Percentage of the population living below the poverty line.
ε: Error term capturing unexplained variation in GDP.
Model B:
Ln GDP = β₀ + β₁(Ln Education) + β₂(Ln Physical Capital) + β₃(Ln Poverty) + ε
Rationale for Using Natural Logarithm in Model B:
The decision to take the natural logarithm of the variables is rooted in addressing potential issues related to non-stationarity, specifically the presence of unit roots. The natural logarithm transformation is commonly used in econometrics to address issues of non-stationarity and ensure the validity of regression results.
ADF Test for Stationarity:
We conducted the Augmented Dickey-Fuller (ADF) test for each variable in both Model A and Model B to assess stationarity. The ADF test evaluates whether a unit root is present in the time series data. A rejection of the null hypothesis suggests that the variable is stationary. By testing for stationarity, we ensure the validity of the regression analysis and the reliability of the estimated coefficients.
Granger Causality Test:
To examine the causal relationship between poverty (Poverty) and GDP, we conducted the Granger causality test. The Granger causality test assesses whether past values of one variable help predict the current values of another variable. In our analysis, we specifically tested whether past values of poverty (Poverty) Granger-cause changes in GDP. A significant result would provide evidence of a causal relationship between poverty and GDP, offering insights into the economic mechanisms at play.