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This model looks at the effects of the Corona Virus on globalization, in terms of migration of workers to other countries, their skills levels and the work-environment available to them. Since countries all over the world have been closing their borders to migration and trade, in order to control the spread of the virus, the global economic landscape is changing. We want to look at its effects on the work-environment available to workers, as well as their skill levels, given restrictions on travelling and that no brain drain is taking place. And in the near future when countries slowly start opening their borders, the effects of people migrating to better developed countries in search for better opportunities given the probability of travelling. Furthermore, we also look at how the worker’s skill level is translated into the work environment and how the work environment is translated into the worker’s skill level, as well as the consequences of interaction of workers with different skill levels.

Literature Review:

With the unprecedented nature of the coronavirus comes uncertainty of the economic impact it can have. World Trade Organization (WTO) claimed the fall in the global economy will be worse than the financial crises of 2008 – 2009. WTO also predicted that there will be a decline in trade between 13 and 32% in 2020. Although North America and Asia will be affected the most, nearly every country will face a decline in their economy. International trade patterns will inevitably change. Movement of workers and people are being limited, nations are closing their borders and workers are being sent back to their countries. (1)
Suspension of flights and lockdowns nationwide has led to travel restrictions being imposed around the world. On March 19, the government of USA imposed a Level 4 travel restriction. This was followed by the European Union banning traveling for 30 days. (2) According to an article in Arab News, there are over 10,000 Pakistanis who lost their jobs in the United Arab Emirates. All these Pakistanis wanted to migrate back to their countries after being jobless. Although the entire world will be impacted, developing countries will suffer more because of the limited financial resources and inadequate health services quality. Most countries are expecting a decline in economic growth of 2%.


In our model, we assumed there are only three different kinds of countries – one developed (represented in our model by green), one underdeveloped (yellow) and one medium-developed country (red). This is an agent-based model. We have turtles here classified as people, or agents, with skillsets. Skillset can cover a number of different variables, such as education, knowledge and expertise required to do a job or task. Every country has an average skill set. Every country has a work-environment, which is based on the skills of the people in that country. Agents migrate to the countries with better work-environment than their own country’s work-environment, according to the level of their skillset. Once settled in a certain work-environment, the agents interact with two things: each other and the environment. Accordingly, due to this interaction, their skills can improve the work-environment of a country, and the work-environment can have a positive effect on their skills. Furthermore, when people interact, they can also enhance or degrade each other’s skills. In our model, we assumed that all the workers have same preferences. The model demonstrates migration before and after the spread of coronavirus, and how this migration of people impacts globalization after the world is virus-free. After the spread of the corona virus, countries imposed a travel restriction and only a certain number of highly skilled workers can migrate, given that their probability to migrate to a better work-environment country is less than the travel restriction.


Setup: sets the countires up

Life: creates a given number of workers in the model and spreads them randomly across
Red, Green, and Yellow countries.

Migrate: Opens the door to workers for migration to any other country. (Each agent assess the work-environment of their country and the country that they are to migrate. A worker will opt for a favvorable migration only.)

Impact: This initiates the effects that environemt has on the workers (skill-effect), the effect the worker has on the environment (environment-effect) and the effect of a worker on their neighbouring worker (skill-sharing). Each of these effects can be toggled using switches.

CORONAVIRUS: Deducts a fixed amount of work-environemt from each country.

migration-post-corona: pens the door to workers for migration to any other country with travel restrictions (which can be varied using the slider)

life-after-corona: This initiates the effects that environemt has on the workers (skill-effect), the effect the worker has on the environment (environment-effect) and the effect of a worker on their neighbouring worker (skill-sharing). Each of these effects can be toggled using switches.

For simple migration:
Setup -> Life -> migrate (on) -> migrate (off) -> impact

For migration with travel restrictions:
Setup -> Life -> CORONAVIRUS (optional) -> migration-post-corona -> life-after-corona


The effect on work-environment and avg-skill by toggling "skill-effect", "environment-effect", and "skill-sharing".


Setup -> Life -> impact: There can be scenarios when the lesser developed country (Yellow or Red) taking over the most developed country (Green)

Toggling "color" will show workers according to skill level with pink being the highly skilled and blue being the second highly skilled.

Effect of Migration:
Setup -> Life -> impact -> Migrate

Turning on "ordinary" will create networks in between agents. Turning ordinary off and turning "lettuce" on will create lettuce networks in between agents. Through this, Skill-sharing can potentially be shown through networks.



The model can be modifed to more accurately show the real world of how skills may be transferred through a network structure.


(interesting or unusual features of NetLogo that the model uses, particularly in the Code tab; or where workarounds were needed for missing features)


Aleeha Nadeem - 20020543
Alveena Shafiq - 20020263
Hareem Shafat - 20020471
Mahroz Ashraf - 21020346
Massab Qayum - 21020232

Group 7, Complexity and Economics, Spring 2020,
Lahore University of MAnagement Sciences,

Work cited:


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