Group08 Report
Econometric Modeling with Gender Equality and Women Empowerment
Contents
Introduction
As civilisation advances across the world and nations’ priorities shift from economic survival to economic growth and creation of wealth, people are awakened to consequences of marginalisation and inequalities left behind in the wake of economic progress. Increasingly, questions shift towards how to address or mitigate these risks, balancing these trade-offs, and even whether tackling them upfront may even be beneficial and facilitate economic growth. One of the biggest inequalities of our time is in gender inequality. Gender equality has been touted as a strong way to unlock greater economic potential for countries, by various leading organisations – the World Economic Forum made a case for gender equality, the Economist highlights how inequality affects growth , and the OECD illustrates how growth gaps can be attributed to gender inequality . Intuitively this makes sense since labour inputs is one of the primary drivers of economic growth, and women effectively represented half the population’s potential for labour input.
Economic growth aside, gender inequality also has severe social repercussions as we have observed in the recent rise of the #MeToo movement. All these underscores the importance of better understanding gender inequality and how to address it.
The purpose of this project thus aims to look at existing economic and development data and lend new or reinforce existing perspectives through a data visualisation application which encompasses econometric models.
Data Review
The World Bank stores an enormous amount of data across more than 200 countries and over 1,300 indicators under its ‘World Development Indicators’ database.
Much of this information covers mainly economic related data for each country, over a long period time from 1972 to the latest 2016 data. The gamut of topics covers an extremely wide range, including – GDP, income, inequality, population indicators such as age, health, labour statistics, energy use, financial industry information, even environmental impact and other factors.
Challenges
Given the vast breadth and depth of coverage, undertaking any project that leverages on this data can require a potentially large menial effort in sieving and gathering the data. For the purposes of the topic, we needed to first narrow down the indicators to those that may be relevant to women’s empowerment and gender equality to create a base dataset for analysis. The selection of the variables was done qualitatively and reduced to 47 identified potential indicators.
Next, a preview of the data reveals sparsity issues on two fronts – on time-period and frequency, as well as available countries. Several of these indicators had data that was only collected from a later stage rather than since the start of data collection by the World Bank. And even after data collection had commenced, it was not necessarily done on a sufficiently regular basis (typically yearly, as with all the indicators). This was understandable as the awareness and attention to this area had only seen some impact on data collection activities in the past couple decades. At the same time, the regimen of consistent collection of such data may not have been consistently enforced across all participating countries, which resulted in a glaring gap in yearly data for a large majority of countries.
Data Reduction
For consistency, only countries with enough yearly data points were picked out, and indicators with the same set of data sparsity (or rather, lack thereof) were used. Eventually this was whittled down to 21 indicators for 31 countries, and the time horizon reduced to the period from 2000-2016.
Final dataset structure
After data selection process, some basic transformation was performed to enable greater ease of usage in the subsequent analysis. Some additional factors relating to the data were also added to present a dimension to the countries for use in data clustering.
Designing the Application
The primary aim of the visual application is to provide a structured method to visualising and understanding data, so it can help to enable discovery of new perspectives or reinforce existing theories on gender equality. In designing this piece, therefore, an important aspect was high flexibility. Users need to be able to go through data exploration and discovery, run some basic techniques to better help them decide on features that will be useful, before eventually building the model and gathering results – all within the same application.
To enable this, we first constructed a basic hierarchical map of the functions we wished to include. The structure would include elements of exploratory data analysis as well as econometric modelling, as follows:
Dashboard Design
Next, we embarked on design of the dashboard, using a basic storyboard to layout the intended functions in a logical, user-centric manner to aid navigation. While the eventual application went through multiple iterations to look different from the original design, the initial process helped to better structure and crystallise our thoughts in a visual format. It also acted as a useful reference point to keep our intentions focused.
