PP434 - Final Assignment

Data Visualisation
Project

Candidate number: 64689

This page presents the final project for the PP434 Automated Data Visualisation for Policymaking course.

Financial Empowerment and Economic Stability

Introduction

This project investigates the current state of financial literacy and financial inclusion, exploring how these factors translate into real-world habits and macroeconomic stability.

Technical Notes

Where are the gaps in financial literacy across the world?

1. Global Financial Literacy Benchmark

This map provides the basis for this research by identifying regions with low rates of financial literacy.

It highlights that financial knowledge is not evenly distributed, which is the first step in identifying which economies are more vulnerable than others.

Does greater financial inclusion mean saving more at the national level?

2. National Savings Capacity vs. Financial Inclusion

This chart explores the relationship between financial access and national saving rates. While countries with high GDP per capita generally show higher savings, the data reveals interesting exceptions.

This suggests that while inclusion is a tool, others factor as financial literacy, cultural factors or economic policy may also play a critical role in how much a nation actually saves.

Which specific financial concepts are the hardest for people to understand?

3. Financial Knowledge by Country

This heatmap breaks down literacy into specific concepts. It shows that even the most of the countries, people struggle with understand concepts like "Compound Interest". This helps to identify exactly what should be taught in schools to improve economic knowledge and empower people.

Are people receiving the same level of financial education?

4. Financial Knowledge by Socio-demographic Subgroups

This chart reveals that social inequality is a barrier to education. It can be observed a persistent gender gap in almost every country. As well, the data shows a stark divide between income groups.

This suggests that financial literacy policies should be focus on both empowering women and bridging the wealth-education gap to achieve true economic stability for everyone.

Do people actually practice what they know about money?

5. Financial Habits by Country

It is important to keep in mind that having financial knowledge is not the same as putting it into practice. This map shows that "Comparing financial products" and "Sets Long-term financial goals" are not common habits. It proves that education must also have a a behavioural perspective to transform theoretical knowledge into real-world financial security.

Why do people in some regions still save "under the mattress"

6. Formal vs. Informal Savings by Demographic Groups

This comparison shows that in many emerging markets, informal saving remains more common than using banks. This highlights a lack of trust or physical access, which prevents household money from being protected by the safety and interest-earning potential of the formal financial system.

Does increasing financial inclusion actually help control inflation?

7. Regression Analysis: Financial Inclusion and Inflation

In this final analysis, a negative correlation was identifyed: as financial inclusion increases, inflation stays lower.

This support the argue that higher financial literacy could strengthen the effect on the financial inclusion and inflation control. Thus, by promoting saving and efficient money management, literacy improves the transmission of monetary policy, making inflation control even more effective.

Conclusions

This data visualization project shows how financial literacy and access vary around the world. By transforming complex data into simple charts, patterns in money management can be identified across different groups. These visualizations serve as a starting point for discussing where financial authorities should focus their efforts to improve people economic security.

While these charts reveal interesting links between financial literacy, financial inclusion, savings, and inflation, their aim is to spark curiosity rather than prove ultimate causes. This project encourages further research to explore the deeper reasons behind these trends. These visualizations can guide future studies to better understand the cultural and social factors that shape global economic stability.

Technical Notes

Aims

The main objective of this project is to examine the relationship between financial literacy, formal financial inclusion, and macroeconomic stability. Through data visualization, I aim to identify patterns in how financial knowledge and access shape financial behavior and how these factors may be associated with a country’s ability to control inflation.

Data

Following an open-data approach, I relied on the following core sources:

  • S&P Global Financial Literacy Survey 2014: Provides baseline measures of financial knowledge across countries. Last publication available in 2014.
  • OECD/INFE 2023 International Survey: Used to capture detailed financial habits and concept-specific literacy scores.
  • World Bank Global Findex Database 2025: The main source for data on account ownership and saving behaviour.

In addition, I used the World Bank API to automate data collection using the following indicators, ensuring accuracy and easy replication:

  • National savings: NY.GNS.ICTR.ZS (Gross national savings, % of GDP)
  • GDP per capita: NY.GDP.PCAP.CD (Current US$)
  • Inflation: FP.CPI.TOTL.ZG (Inflation annual %)
  • Account ownership: FX.OWN.TOTL.ZS (Account ownership at a financial institution, % age 15+)

Automation and replication: By linking directly to these indicator codes, the project is structured to allow straightforward updates in the future. All processed datasets are hosted in my public GitHub repository, with the Vega-Lite visualizations remain reproducible for other researchers.

Tools and technical challenges

The technical workflow focused on turning raw data into clear and interactive visual narratives:

  • Data Cleaning (Python): I used Excel and Python (Pandas and NumPy) to clean the datasets. This include removing metadata headers from raw files, handling missing values, and renaming variables. As well, all geographic identifiers were standardized using ISO3 country codes to ensure consistency across datasets.
  • Data Shaping: The data was transformed into a long format using Python, which was necessary for Vega-Lite to run advanced interactivity, filtering, and layered visualizations such as dumbbell charts and heatmaps.
  • Visualization (Vega-Lite): I implemented interactive features including selection parameters (dropdown menus), layered regression lines, dynamic scaling (zoom and pan), and tooltips.
  • Challenges: The main limitation was the lack of annual time-series data for financial literacy. To address this, I focused on high-quality 2023–2024 cross-sectional data, which provide the most reliable representation of current conditions.

Conclusions

This project shows how effective data design can make complex economic and social relationships more accessible and understandable for policy makers. Although the analysis is cross-sectional, the visualizations helps to support the research study, in this case, it helps to identify that financial literacy and inclusion play an important role in economic resilience.

By making the code, API queries, and data sources publicly available, this project lays the groundwork for future research as more longitudinal data becomes available.

Technical Links

To review how the data was imported and the specific indicators used, see the Google Colab notebook here. For full documentation of the visualization process and access to the raw data files, visit the GitHub repository here.

Gen AI Disclosure

I used Claude and Gemini as a coding assistant for data cleaning in Python, creating charts in VegaLite, and for website style design.