Exploring the Relationship Between Economic Prosperity, Health, and Internet Adoption Across Countries

 

1. Project Title:

Exploring the Relationship Between Economic Prosperity, Health, and Internet Adoption Across Countries

2. Research Question

How are a country’s income per person and life expectancy associated with its internet use rate? Specifically, does higher income and longer life expectancy predict greater internet adoption across nations?

3. Motivation / Rationale

Access to the internet has become a key indicator of technological advancement, educational access, and economic opportunity in the modern world. However, large disparities still exist globally. Understanding what drives these differences—especially whether economic well-being and population health influence digital inclusion—can reveal important insights into global inequality and development.
By using publicly available Gapminder data, I aim to identify the strongest predictors of internet use and examine how economic and health indicators interact to shape technological access across countries.

4. Potential Implications

If a strong relationship exists between income, health, and internet use, this could have several implications:

  • Policy Insight: Governments and international organizations could design policies that jointly target health and economic growth to bridge the digital divide.

  • Development Planning: Donor agencies and NGOs could allocate resources more strategically to promote connectivity in underserved regions.

  • Societal Impact: Improved internet access can enhance education, healthcare, and communication—contributing to more equitable development outcomes worldwide.

Understanding these links could therefore help shape interventions that foster both digital inclusion and human development.

Comments

Post a Comment

Popular posts from this blog

Python project 2

  1. The Script import pandas as pd # Load dataset df = pd.read_csv("gapminder.csv") # Select variables vars_of_interest = ["incomeperperson", "internetuserate", "lifeexpectancy"] # Convert to numeric, handle missing/invalid df[vars_of_interest] = df[vars_of_interest].apply(pd.to_numeric, errors="coerce") # --- Data Management Decisions --- # 1. Drop rows where all three variables are missing df = df.dropna(subset=vars_of_interest, how="all") # 2. Create categorical bins df["income_group"] = pd.cut(df["incomeperperson"],                             bins=[0, 5000, 20000, 100000],                             labels=["Low Income", "Middle Income", "High Income"]) df["internet_group"] = pd.cut(df["internetuserate"],                               bins=[0, 30, 70, 100],  ...
Read more

Simple Linear Regression

 1. Program/Script import pandas as pd import statsmodels.api as sm # Load dataset df = pd.read_csv("gapminder.csv") # Convert variables to numeric df["incomeperperson"] = pd.to_numeric(df["incomeperperson"], errors="coerce") df["internetuserate"] = pd.to_numeric(df["internetuserate"], errors="coerce") # Drop missing values df_clean = df.dropna(subset=["incomeperperson", "internetuserate"]) # Center the explanatory variable (income per person) df_clean["income_centered"] = df_clean["incomeperperson"] - df_clean["incomeperperson"].mean() # Check the mean after centering print("Mean of centered income:", df_clean["income_centered"].mean()) # Add constant and run regression X = sm.add_constant(df_clean["income_centered"]) y = df_clean["internetuserate"] model = sm.OLS(y, X).fit() print(model.summary()) 2. Output Mean of centered in...
Read more