Economy in the Shadows: Investigating Informal Employment

A story looking at the vicious cycle of informal employment.

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Inspiration

We decided to go in the direction of SDG 8, as we will soon be a part of the workforce and we are curious how the workforce is around the world. In doing research, we learned a lot more about the informal work industry and the often invisible struggles that exist within developing countries. We felt that more awareness needed to be brought to this topic as it is something that we often don’t see or hear about often.

Story

Developing countries struggle economically and this problem stems from many factors with one of them being informal employment. Informal work is work that is not officially recognized by the government and thus, outside the national labour regulations, creating a vicious cycle that can not be broken without intervention. These countries struggle with providing people secure jobs and providing the benefits that come with formal jobs. Moreover, workers also face a challenge with financial security as they have no access to credible and dependable bank systems, which forces them into the informal workforce. Since this work isn’t recognized by governments, they receive tax revenue and thus have less money to reinvest into the community. So as more people become invested in informal employment, youth forcibly become attracted to this type of work perpetuating a cycle of economic struggle. Every step in the cycle is an action that a party seems forced to make and unable to escape from. And all of that just in order to survive. But to survive in this cycle, they must make the cycle continue forever. However, not all hope is lost, a possible solution would be to increase the implementation of labour rights in these countries with high informal employment rates. These labour rights help move informal jobs to formal ones with the help of various policies.

Behind the Story: Our Data Analysis

With the data, we cleaned some values that were NaN. An example would be with the Compliance to Labour rights and Informal Employment Rate data frame. For some data frames, grouping was done to combine different forms We then turned them into graphs, with the use of ChatGPT 4, that compared different sets of data and produced trendlines and correlations on them. Graphs used were scatter plots and bar plots

What are your data sources?

All of our data sources comes from the UN databanks.

Rate of informal employment of every country: link

GDP per capita of every country: link

Adults with an account at a financial institution: link

Proportion of children engaged in economic activity: link

Proportion of NEET youth: link

How did you use ChatGPT?

We used ChatGPT to brainstorm main ideas and help us code python graphs. In fact, we had difficulty getting the exact result we wanted, so we used ChatGPT to facilitate the debugging process instead of searching the web for solutions, which could potentially waste time. We found their coding suggestions accurate to some extent, as it often gave us the desired graphs.

What we learned.

While we were processing and analyzing our data, we faced a challenge where the names of the countries in the different CSV files were different. For example, the US was labelled as “United States of America” in one document and just “USA” in another document. At first, we thought that we should create a list in python with all the different names of a country, and use it to compare the documents. However, we realized that this situation can be easily overcome simply by asking ChatGPT, to organize the data and unite the country names. This is when we realized the power of LLMs, which allow us to facilitate tasks which would have required manual labor before. Furthermore, ChatGPT allowed us to generate a basis of python codes which we can start working with, and also allowed us to easily debug the code when we faced problems. By using this technology, we were able to learn how to handle big data and generate visual aids with Python.

Links to: PowerPoint: link CoLab NoteBook: link Github Repos for raw CSV: link link ChatGPT Log Link: link

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