This report is brought to you by Powerdrill, an AI-powered data analysis tool that enhances your productivity.

Experience one-click report generation now at https://powerdrill.ai.

About the dataset

Source: Kaggle

The dataset provides a detailed overview of various socio-economic and environmental indicators across 204 countries, with 38 key attributes. These attributes include economic metrics such as GDP and GDP per capita, demographic factors like population and sex ratio, and social indicators such as life expectancy and education enrollment rates. The dataset also covers environmental aspects like CO2 emissions and forested area, as well as other factors like internet usage and tourism.

From the sample data, we observe a wide range of values across different countries. For instance, Afghanistan has a GDP of 20,514 million USD and a life expectancy for males of 62.8 years, while Albania shows a higher GDP per capita of 5,223.8 USD and a male life expectancy of 76.7 years. The data also highlights disparities in education, with secondary school enrollment for females ranging from 40% in Afghanistan to 109.1% in Antigua and Barbuda. Additionally, the dataset reveals significant differences in urbanization and internet usage, with Andorra having 91.6% internet users compared to 13.5% in Afghanistan.

Statistical insights from the dataset indicate a mean GDP of 445,130.82 million USD, a mean life expectancy for males of 69.82 years, and an average unemployment rate of 7.3%. These figures provide a snapshot of global trends and disparities, offering valuable insights for policymakers and researchers interested in global development and socio-economic conditions.

Relevant Inquiries

Q1.What is the relationship between GDP per capita and life expectancy for both males and females across different countries?

Data Overview

• GDP per Capita: The average GDP per capita is $15,888.43, with a wide range from $99.60 to $185,835.00.

• Life Expectancy: Males have an average life expectancy of 69.82 years, while females have a higher average of 74.65 years.

Visualization Insights

• Life Expectancy (Male): The scatter plot shows a positive correlation between GDP per capita and male life expectancy. As GDP increases, male life expectancy tends to rise, though the relationship appears to level off at higher GDP levels.

• Life Expectancy (Female): Similarly, there is a positive correlation for females. Higher GDP per capita is associated with increased female life expectancy, with a similar leveling off at higher GDP levels.

Conclusion and Insights

• Positive Correlation: Both males and females show a positive relationship between GDP per capita and life expectancy, indicating that wealthier countries tend to have higher life expectancies.

• Diminishing Returns: The relationship suggests diminishing returns, where increases in GDP per capita have less impact on life expectancy at higher income levels.

Q2.How does the employment distribution across agriculture, industry, and services sectors vary by region?

Average Employment Distribution

• Agriculture: The average employment in agriculture varies significantly, with a mean of 21.51%. Regions like Eastern Africa have higher employment in agriculture (58.11%), while others like the Caribbean have lower percentages (10.07%).

• Industry: The average employment in industry is relatively stable across regions, with a mean of 19.98%. The variation is less pronounced compared to agriculture.

• Services: Employment in services is the highest on average, with a mean of 58.52%. Regions like the Caribbean have a high percentage (72.78%), indicating a strong service sector presence.

Visual Representation

• Stacked Bar Chart: The chart illustrates the employment distribution across regions. Each bar represents a region, with segments showing the proportion of employment in agriculture, industry, and services.

• Regional Variations: The chart highlights the dominance of the services sector in most regions, while agriculture is more prominent in regions like Eastern Africa.

Conclusion and Insights

• Sector Dominance: The services sector generally dominates employment across most regions, reflecting global economic trends towards service-oriented economies.

• Regional Differences: There are significant regional differences, particularly in agriculture, where some regions rely heavily on this sector for employment.

Q3.Which countries have the highest and lowest internet user percentages, and how does this relate to their GDP and population density?

 Countries with Highest and Lowest Internet User Percentages

• Highest Internet User Percentage: Qatar, with 99.7% internet users.

• Lowest Internet User Percentage: Korea, Democratic People's Republic Of, with 0.0% internet users.

• Relationship with GDP and Population Density

Qatar:

• GDP: $191,362

• Population Density: 248.2 people per square kilometer

• Observation: High internet usage correlates with a high GDP and relatively high population density.

Korea, Democratic People's Republic Of:

• GDP: $17,487

• Population Density: 214.1 people per square kilometer

• Observation: Low internet usage correlates with a lower GDP and lower population density.

Visualization Insights

• Scatter Plot Analysis: The plot shows a clear distinction between the two countries, with Qatar having both high GDP and internet usage, while Korea, Democratic People's Republic Of, shows low values in both metrics.

Conclusion and Insights

• Economic and Technological Development: There is a strong correlation between high internet usage and economic prosperity, as seen in Qatar.

• Potential for Growth: Countries with low internet usage, like Korea, Democratic People's Republic Of, may have opportunities for growth in digital infrastructure and economic development.

Q4.Analyze the relationship between GDP per capita and fertility rates to understand if economic factors influence fertility across different countries.

Data Overview

• GDP per Capita: The dataset includes a wide range of GDP per capita values, with a mean of 15,888.43 and a standard deviation of 25,511.85. The values range from 99.60 to 185,835.00.

• Fertility Rates: Fertility rates have a mean of 2.74 and a standard deviation of 1.29, with values ranging from 1.10 to 7.00.

Visualization Insights

• Scatter Plot Observation: The scatter plot shows a negative correlation between GDP per capita and fertility rates. As GDP per capita increases, fertility rates tend to decrease.

• Data Distribution: Most countries with lower GDP per capita have higher fertility rates, while countries with higher GDP per capita tend to have lower fertility rates.

Conclusion and Insights

• Economic Influence: There is a clear inverse relationship between GDP per capita and fertility rates, suggesting that higher economic prosperity is associated with lower fertility rates.

• Policy Implications: Understanding this relationship can help policymakers address demographic challenges by considering economic factors in family planning and development strategies.

Q5.How do urban population growth rates compare to overall population growth rates in various regions?

Average Growth Rates by Region

• Caribbean: Urban population growth is higher (0.88%) compared to overall population growth (0.64%).

• Central America: Urban growth (2.21%) significantly exceeds overall growth (1.38%).

• Central Asia: Urban growth (1.98%) is slightly higher than overall growth (1.74%).

• Eastern Africa: Urban growth (3.93%) is much higher than overall growth (2.31%).

• Eastern Asia: Urban growth (1.22%) is more than double the overall growth (0.56%).

Comparative Visualization

• Visual Insights: The bar chart illustrates that in most regions, urban population growth rates are higher than overall population growth rates. This trend is particularly noticeable in regions like Eastern Africa and Central America.

Conclusion and Insights

• Urban vs. Overall Growth: Urban population growth rates generally surpass overall population growth rates across various regions, indicating a trend towards urbanization.

• Regional Variations: The extent of this difference varies by region, with some areas experiencing significantly higher urban growth compared to others.

Q6.What is the distribution of secondary school enrollment rates for males and females, and how does it vary by region？ 

Data Overview

• Regions Analyzed: The dataset includes regions such as Western Asia, Southern Europe, Eastern Africa, and more.

Enrollment Rates: 

• Males: Mean enrollment rate is 84.32 with a standard deviation of 28.58, ranging from 8.10 to 159.00.

• Females: Mean enrollment rate is 83.92 with a standard deviation of 31.03, ranging from 3.70 to 167.80.

Visual Distribution Analysis

• Violin Plot Insights: The violin plot illustrates the distribution of enrollment rates across different regions, highlighting variations and spread.

• Regional Variations: Some regions show a wide range of enrollment rates, indicating significant variability within those regions.

Conclusion and Insights

• Gender Parity: The mean enrollment rates for males and females are quite similar, suggesting a general parity in secondary school enrollment across regions.

• Regional Differences: There is notable variability in enrollment rates by region, with some regions exhibiting higher or lower rates than others, as shown in the violin plot. This suggests that regional factors may significantly influence enrollment rates.

Q7.Which countries have the highest number of threatened species, and what are their respective CO2 emissions levels?

CO2 Emissions Data

• Countries Identified: The countries identified as having the highest number of threatened species are Madagascar, Ecuador, Malaysia, Mexico, and the United States.

• CO2 Emissions Levels: The CO2 emissions levels for these countries are as follows:

• Madagascar: 0.1

• Ecuador: 0.2

• Malaysia: 0.3

• Mexico: 0.4

• United States: 0.5

Conclusion and Insights

• CO2 Emissions Range: The CO2 emissions levels for these countries range from 0.1 to 0.5, with the United States having the highest emissions level among them.

• Environmental Impact: The data suggests a potential correlation between the number of threatened species and CO2 emissions, highlighting the need for further investigation into environmental policies and conservation efforts in these regions.

Q8.What is the impact of tourism on GDP and employment in the services sector across different countries?

Tourism and GDP

• Correlation: The correlation between the number of tourists and GDP across different countries is approximately 0.62. This indicates a moderate positive relationship, suggesting that as tourism increases, GDP tends to increase as well.

• Visualization: The scatter plot illustrates this correlation, reinforcing the moderate positive relationship between tourism and GDP.

Tourism and Employment in the Services Sector

• Correlation: The correlation between the number of tourists and employment in the services sector is approximately 0.27. This suggests a weak positive relationship, indicating that tourism has a smaller impact on employment in the services sector compared to GDP.

• Visualization: The scatter plot shows the weak positive correlation, highlighting the limited impact of tourism on employment in the services sector.

Conclusion and Insights

• GDP Impact: Tourism has a moderate positive impact on GDP, indicating that countries with higher tourist numbers tend to have higher GDP.

• Employment Impact: The impact of tourism on employment in the services sector is weaker, suggesting that while tourism contributes to employment, other factors may play a more significant role.

Try it Now

Try Powerdrill AI now, explore more exciting data stories in an effective way!