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Modeling the Well-Being of a Gig Worker

By: Joel John & Moses Sam Paul

Classifying Jobs In A Dynamic Environment

In order to provide a solution for the gig economy, as researchers we need to see things from their point of view. Individuals may be contract laborers that are engaged with a single firm or choosing to hold multiple, full-term employment — given the status of the economy today. A general classification of work alone will not help us create models that look at what contributes to a gig economy laborer’s productivity but it is where we began. The ISCO-08 classification (International Standard Classification of Occupations) for instance looks primarily at the nature of the job. They have it ranging from routine, menial work to managers and even army personnel. Closer to home in India, the classification is on basis of the tenure of the contract and number of parties in it. The problem again is the fact that the typical idea of having a single 9 to 5 job is slowly vanishing away with individuals having multiple “side-hustles”, part-time roles and jobs that help in paying the bill. Due to this, we concur that the way to classify work will be to look at the most basic abstraction of what makes work possible. That is skills. To study the classification of jobs, we considered the ISCO-08 model (2) (International Standard Classification of Occupations). However, we found it to be too restrictive in how work was defined at a time when alternative work agreements were becoming the trend. Germany’s work categorisation model from 2015 (3) has over 24,000 jobs mentioned, in a 5 tiered job classification system. We found it similar to the National Skill Development Corporation’s approach and have decided to opt for it due to its proximity to regional regulations. (4) The National Skill Development Corporation’s approach to categorising jobs breaks it down to give levels ranging across the sector, proficiency and sub-category of the industry a job is positioned in. This helps us get enough of an understanding of the seniority of the role and the level of skills it requires.

Evolution of our model

Naturally, skills on their own are not the sole constituents of what contributes to the overall well being of an individual. In order to explore the other factors, we had considered the financial well-being and social-net of the individual. Our assumption was that having a strong social-net would enable individuals to take on more risk, and access to financial services ensures the individual is able to save sufficiently. Our previous assumption failed to take into account the external factors that affect an employee’s productivity. These are matters that the individual cannot influence at the personal level but rather speak about as a community. One of the ways this manifests is in the form of internet access and public infrastructure: such as road connectivity can affect the likelihood of an individual being able to access a job. While these factors are not influenced directly by an individual at a large enough scale — democratic functions such as election of strong political leaders that ensure these public utilities are in place — is how a labourer ensures these matters are taken care of. We see institutions being instrumental in the new model. Similarly, the power of one’s passport can greatly affect the range of opportunities available to them. In order to account for this, our new model takes a look at the geographic functions of a region a worker is employed from.

The Base model failed to account for structural issues with the environment labor finds itself in
The Working Model: The new model explores community as the means through which a gig worker can optimise for his/her overall well-being by improving their bargaining power to negotiate better with the system (state & market).

Finding Strength Through Data Ownership

We observed that while we suggest taking a holistic view of an individual’s data to measure their well-being, we have not accounted for the fact that different data has different levels of sensitivity or availability. Financial data for instance can be readily accessed in a secure form through account aggregators but when it comes to healthcare data, even hospitals don’t share it with one another at a huge scale. This means our model will need to account for the difficulties in sourcing, analysing and storing varying data-forms of the individual. In doing so, it will need to align itself with the data privacy laws of the region, the task is undertaken at.

  1. International Labour Organization. (2012). International Standard Classification of Occupations: Structure, group definitions and correspondence tables
  2. Paulus, W., & Matthes, B. (2013). The German classification of occupations 2010: structure, coding and conversion table. FDZ-Methodenreport, 8, 2013.
  3. Ministry of Labour & Employment, Government of India. (2016). National classification of Occupation — 2015



Bharat Inclusion aims to build knowledge, foster innovation & entrepreneurial activity towards improving financial inclusion and livelihood for the poor.

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Bharat Inclusion Initiative

We aim to build knowledge, foster innovation & entrepreneurial activity towards improving financial inclusion and livelihood for the poor.