Rene12-3/kenya-tanzania-wdi-analysis

Python analysis of Kenya and Tanzania using World Bank Development Indicators # Kenya vs Tanzania: World Development Indicators Analysis A Python-based comparison of development trends in Kenya and Tanzania using selected indicators from the World Bank's World Development Indicators (WDI). The project asks: > Does faster expansion of infrastructure access translate into stronger > development outcomes? ## Project outputs - PowerPoint presentation - Full research report - Python analysis - Clean analysis data - Latest indicator values - Model results ## Indicators | Indicator | WDI code | Purpose | |---|---|---| | Access to electricity (% of population) | `EG.ELC.ACCS.ZS` | Infrastructure access | | Individuals using the Internet (% of population) | `IT.NET.USER.ZS` | Digital inclusion | | Life expectancy at birth (years) | `SP.DYN.LE00.IN` | Health outcome | | GDP growth (annual %) | `NY.GDP.MKTP.KD.ZG` | Economic performance | ## Key findings - Kenya's electricity access increased from **4.5% in 1995** to **76.2% in 2023**. - Tanzania's electricity access increased from **7.2% in 1995** to **42.7% in 2021**, the latest observation in the analysis dataset. - Internet use in 2024 was **40.8% in Kenya** and **31.2% in Tanzania**. - Tanzania recorded higher life expectancy in 2024: **67.21 years**, compared with **63.83 years** in Kenya. - Tanzania also recorded higher GDP growth in 2024: **5.5%**, compared with **4.7%** in Kenya. The hypothesis received partial support. Kenya leads on infrastructure and digital access, but Tanzania leads on the selected recent health and growth measures. ## Electricity-access model A separate ordinary least-squares trend was fitted for each country: ```text electricity access = intercept + slope x year ``` | Country | Estimated annual change | R-squared | 2030 fitted value | |---|---:|---:|---:| | Kenya | 2.54 percentage points | 0.88 | 87.9% | | Tanzania | 1.39 percentage points | 0.89 | 50.4% | The model summarizes historical direction. It is not a causal model or a policy forecast. ## Too …