Unemployment is simple enough to understand: it is an economic condition in which individuals seeking jobs remain un-hired. Yet measuring how many people are unemployed at any given moment in any given country is rather complex.

Author: Luca Ventura
Project Coordinator: Binh P.

The COVID-19 pandemic will eventually go away; the consequences for jobs and livelihoods across the globe will be felt for many years to come. This is the main takeaway from the latest World Economic Outlook released by the International Monetary Fund in October. The pandemic had—and will continue to have—especially severe effects on the most economically vulnerable people, informally employed women and younger workers in particular. The burden of the crisis, the report says, has fallen unevenly across economic sectors: while the impact of the recession has been less grievous for those able to work from home, workers employed in industries such as accommodation and food services, transportation, retail and wholesale have been especially hard hit. Furthermore, the pandemic exacerbated pre-existing trends in poverty and income inequality which was already on the rise in many advanced, emerging and developing economies. Lockdowns and school closures cast a long shadow on millions of children’s future prospects too: a trove of academic studies has demonstrated that lower and interrupted lifetime schooling is associated with lower lifetime income and earnings trajectories. The toll of COVID-19, quite simply, threatens to fully undo decades of progress and push tens of millions of people into job insecurity, not just today but tomorrow as well.

The scenario depicted by the economists at the Fund, it is important to highlight, stands in stark contrast with that of the global financial crisis of 2007-2009, when the top quintile of the income scale (meaning the upper-middle class) bore the brunt of a recession triggered by the housing speculative bubble in the US and the cascade of banking and corporate failures that followed. While not all recessions are the same, they all tend to result in rapidly rising unemployment rates that take a very long time to fall after positive economic growth returns.

But what does unemployment mean? It is a question that appears easy to answer, at least superficially. Not being able to afford rent, to get an education or visit a doctor, to care for yourself and your family: unemployment, we know, has many ramifications. However, translating each individual situation into data, and data into policies that can improve the situation of millions of individuals, is remarkably arduous. While experts agree that the jobless rate represents the percentage share of the labor force out of work, and that high unemployment ratios can threaten growth and social cohesion, they often disagree when it comes to measuring joblessness. There are multiple ways to appraise the job market's myriad realities.

The official unemployment rate is determined by calculated by dividing the number of unemployed individuals by all individuals in the labor force. The trouble starts when it comes to figuring out who exactly is and is not in the labor force? The very individuals in question often cannot tell whether they should consider themselves employed or unemployed.

For example: a person who loses a well-compensated full-time job and settles for a part-time position that pays a fraction is by default classified as “employed,” while another person who actively seeks work but takes a few weeks off from the search is not even counted as part of the labor force. An individual who would like to work but is unable due to a disability or medical condition is in the very same position.

The result is that many economists believe that—because of the existence of persons who are unemployed or hidden under-employed—statistics are inherently skewed and paint a too-rosy picture (yes, even during these gloomy times) of the workforce. Needless to say, unemployment and hidden underemployed too are very difficult to measure.

Tracking the labor market is made even more complicated when different tracking tools tell different stories. Whether through census-type methods, employment office records, surveys of a sample of the population or multi-approach techniques, their conclusions will only offer an approximate reflection of the economic and social health of a country.

Nevertheless, over time, unemployment rates remain a crucial indicator of the health, level of development and growth trajectory of an economy. Rising unemployment results in loss of income for individuals and reduced collection of taxes for governments, forcing them to spend greater amounts on unemployment benefits and social subsidies. Long-term unemployment can also weaken the strength of the social fabric, lead to mass frustration and rejection of democratic political orders, prompt cross-border migrations or threaten the economy of trading partners.

In conclusion then—taking into account the many reservations about the accuracy of workforce tracking methods—how many jobs have been lost globally since the beginning of the COVID-19 pandemic? The IMF tries to answer this question by referencing estimates by the UN's International Labour Organization (ILO). Assuming a 48-hour working week, the reduction in work hours in the second quarter of 2020 was equivalent to the loss of 495 million full-time jobs, which added to the equivalent of 160 million full-time jobs lost in the first quarter. The ILO expects the losses to pile up as the year nears to the end, with a decline in working hours in the third quarter equal to 345 million full-time positions—19.8% of which in the Americas, 12.4% in the Arab states, 11.6% in Europe and Central Asia, followed by Africa at 11.5% and the Asia-Pacific region at 10.7%. Looking further ahead, in a baseline scenario, an estimated equivalent of 245 million full-time jobs could be lost in the fourth quarter (whereas the pessimistic view projects a loss corresponding to 515 million posts, and the “optimistic” view to 160 million). Gone in the blink of an eye, many of these work hours will take months or years to come back while otheres might not even exist anymore once the pandemic is behind us.

Whereas such broad numbers give us a hint of where jobs are (or no longer are) today, they often suggest little about their nature and where they will go eventually. Experts argue that in the span of just a few months, COVID-19 rapidly accelerated developments that were slowly becoming mainstream—the increase in remote working, the digitization of many processes and the replacement of full-time employees with contingent workers being the most obvious ones. The pandemic has also renewed fears that automation will replace entire job categories: robots can assemble car parts, robots can scrub floors, and robots can pick up vegetables. 

In pre-pandemic research, the McKinsey Global Institute studied more than 2,000 work activities focusing on 46 countries representing about 80% of the global workforce and quantified the technical feasibility of automating each of them. The proportion of occupations that can be fully automated using demonstrated technology, McKinsey concluded, is actually small: less than 5%. However, as it became clear over these past several months, even if whole occupations are not automated, partial automation is set to affect almost all occupations to a greater or lesser degree, with about 60% of them having at least 30% of activities that can be performed by machines. In a more recent survey of company executives around the world, McKinsey confirmed that, due to the pandemic, the adoption of automation has accelerated “moderately” or “significantly” in almost 7 businesses out of 10 among those examined.

So, should we resign ourselves to a future of high unemployment and widening social inequality? The truth, as a famous quote goes, is that prediction is always very difficult, especially if it's about the future. The example of those handful of countries that have managed to bring the number of new COVID-19 cases to near zero and their economic activity and unemployment rates to near pre-pandemic levels, should guide us and give us hope. Jobs are lost, jobs are created, jobs are learned. Hang tight.

*Values are expressed in terms of a percentage.

Country 2013 2014 2015 2016 2017 2018 2019 2020
Albania 15.9 17.5 17.1 15.2 13.7 12.3 11.5 11.8
Algeria 9.829 10.6 11.214 10.498 11.709 11.731 11.383 14.103
Argentina 7.075 7.25 6.533 8.467 8.35 9.2 9.825 10.981
Armenia 16.2 17.6 18.5 18 20.9 20.5 18.9 22.273
Aruba 7.599 7.451 7.298 7.694 8.923 7.283 7.544 13.084
Australia 5.658 6.058 6.05 5.7 5.583 5.292 5.158 6.908
Austria 5.333 5.608 5.742 6.042 5.508 4.883 4.5 5.8
Azerbaijan 4.973 4.913 4.958 5.043 4.961 4.944 4.848 6.541
Bahrain 4.392 3.767 3.358 3.7 3.58 3.932 4 4.897
Barbados 11.625 12.275 11.3 9.65 9.95 10.05 10.357 14.853
Belarus 0.508 0.488 0.912 1.017 0.768 0.428 0.295 1.403
Belgium 8.45 8.55 8.483 7.842 7.117 5.958 5.358 6.136
Belize 12.95 11.578 10.157 9.55 9.35 9.725 9 25.059
Bhutan 2.9 2.6 2.5 2.1 3.138 n/a n/a n/a
Bolivia 4 4 4 4 4 4 4 8
Bosnia and Herzegovina 27.5 27.5 27.7 25.4 20.5 18.4 15.7 19
Brazil 7.2 6.783 8.283 11.258 12.767 12.258 11.925 13.371
Brunei Darussalam 7.7 6.9 7.7 8.5 9.3 8.7 6.822 6.822
Bulgaria 13.038 11.524 9.233 7.666 6.23 5.2 4.2 5.6
Cabo Verde 16.4 15.8 12.4 15 12.2 12.2 8.5 8.5
Canada 7.1 6.925 6.9 6.992 6.342 5.833 5.667 9.748
Chile 6.082 6.495 6.328 6.685 6.965 7.377 7.223 11.441
China 4.05 4.09 4.05 4.02 3.9 3.8 3.62 3.8
Colombia 9.658 9.092 8.908 9.217 9.367 9.683 10.517 17.289
Costa Rica 8.311 9.658 9.604 9.541 9.293 11.951 12.417 22
Country 2013 2014 2015 2016 2017 2018 2019 2020
Croatia 19.808 19.275 17.067 14.958 12.433 9.858 7.758 9.267
Cyprus 15.85 16.075 14.9 12.95 11.05 8.35 7.075 8.003
Czech Republic 6.942 6.097 5.04 3.946 2.89 2.243 2.001 3.1
Denmark 7.375 6.867 6.283 6 5.808 5.117 5.042 6.2
Dominican Republic 9.196 8.538 7.328 7.08 5.509 5.656 6.167 16
Ecuador 4.15 3.8 4.77 5.21 4.62 3.69 3.84 8.074
Egypt 12.992 13.365 12.859 12.705 12.245 10.932 8.612 8.296
El Salvador 5.9 7 7 7.1 7.049 6.347 6.733 9.39
Estonia 8.628 7.351 6.185 6.758 5.763 5.371 4.448 7.8
Fiji 6.367 6.2 5.5 5.5 4.5 4.5 4.5 13.351
Finland 8.325 8.825 9.575 8.975 8.825 7.425 6.833 8.418
France 10.3 10.283 10.367 10.042 9.425 9.025 8.467 8.879
Georgia 16.9 14.6 14.1 14 13.9 12.7 11.6 n/a
Germany 5.242 5.008 4.633 4.158 3.758 3.417 3.133 4.267
Greece 27.475 26.5 24.9 23.55 21.45 19.3 17.325 19.876
Honduras 4.096 5.488 4.592 4.668 4.048 4.076 4.109 5.713
Hong Kong SAR 3.376 3.262 3.307 3.387 3.119 2.816 2.955 5.234
Hungary 10.178 7.726 6.814 5.115 4.156 3.708 3.418 6.1
Iceland 5.392 4.958 3.992 3.008 2.825 2.742 3.55 7.2
Indonesia 6.25 5.94 6.18 5.61 5.5 5.34 5.28 8
Ireland 13.767 11.892 9.933 8.4 6.725 5.775 4.958 5.629
Islamic Republic of Iran 10.4 10.6 11 12.425 12.075 12.025 10.65 12.176
Israel 6.25 5.9 5.25 4.825 4.225 4 3.8 5.989
Italy 12.142 12.608 11.908 11.65 11.275 10.625 9.9 11
Jamaica 15.275 13.75 13.5 13.2 11.65 9.125 7.7 n/a
Japan 4.008 3.583 3.375 3.108 2.825 2.442 2.358 3.305
Jordan 12.6 11.875 13.075 15.275 18.3 18.6 19.075 n/a
Kazakhstan 5.206 5.042 5.11 4.951 4.9 4.854 4.779 7.779
Korea 3.1 3.492 3.592 3.675 3.683 3.833 3.783 4.075
Kosovo 30 35.3 32.9 27.5 30.5 29.6 25.7 n/a
Kuwait 1.902 1.722 1.322 1.248 1.28 1.087 n/a n/a
Kyrgyz Republic 8.332 8.046 7.554 7.211 6.891 6.605 6.605 6.605
Latvia 11.868 10.847 9.875 9.642 8.715 7.415 6.311 9
Lithuania 11.77 10.699 9.119 7.861 7.073 6.146 6.254 8.2
Luxembourg 6.827 7.081 6.636 6.256 5.845 5.105 5.401 6.47
Macao SAR 1.85 1.7 1.825 1.9 1.975 1.8 1.725 2.325
Malaysia 3.1 2.875 3.15 3.45 3.425 3.325 3.275 4.9
Malta 6.108 5.733 5.392 4.7 4 3.658 3.633 4.2
Mauritius 8 7.8 7.9 7.3 7.1 6.9 6.7 21
Mexico 4.903 4.823 4.349 3.883 3.423 3.331 3.493 5.238
Moldova 5.1 3.9 5.016 4.226 3.939 2.961 5.12 8
Mongolia 7.9 7.9 7.5 10 8.8 7.8 10 12
Morocco 9.235 9.879 9.707 9.4 10.2 9.8 9.2 12.5
Netherlands 7.257 7.434 6.891 6.024 4.854 3.839 3.39 5.5
New Zealand 5.75 5.35 5.35 5.1 4.725 4.275 4.075 6.027
Nicaragua 5.746 6.557 5.909 4.481 3.67 5.5 6.127 10.601
Nigeria 9.955 7.841 9 13.375 17.462 22.562 n/a n/a
North Macedonia 29 28.025 26.05 23.75 22.375 20.725 17.25 20.248
Norway 3.773 3.617 4.531 4.74 4.216 3.854 3.728 4.5
Pakistan 5.975 6 5.9 5.958 6.018 5.55 4.08 4.5
Panama 4.098 4.823 5.052 5.494 6.13 5.956 7.07 10.923
Paraguay 5.021 6.038 5.351 5.998 6.086 6.236 6.086 6.978
Peru 5.945 5.938 6.49 6.742 6.876 6.7 6.6 12.473
Philippines 7.075 6.8 6.275 5.475 5.725 5.325 5.075 10.35
Poland 10.328 8.988 7.499 6.161 4.888 3.846 3.279 3.767
Portugal 16.183 13.894 12.444 11.066 8.867 6.994 6.463 8.125
Puerto Rico 14.3 13.9 12 11.8 10.8 9.2 8.3 12
Romania 7.096 6.802 6.812 5.902 4.927 4.187 3.912 7.9
Russia 5.5 5.158 5.575 5.525 5.2 4.8 4.6 5.6
São Tomé and Príncipe 13.525 13.472 13.433 13.416 13.472 n/a n/a n/a
San Marino 8.079 8.739 9.182 8.597 8.095 8.009 7.66 10.08
Saudi Arabia 5.568 5.721 5.591 5.6 6 6 5.625 n/a
Serbia 23 19.894 18.231 15.917 14.051 13.273 10.909 13.384
Seychelles 3.325 2.983 2.684 2.684 3 3 3 3
Singapore 1.9 1.95 1.9 2.075 2.175 2.1 2.25 3
Slovak Republic 14.258 13.183 11.483 9.683 8.108 6.542 5.758 7.783
Slovenia 10.158 9.742 8.992 8.008 6.575 5.125 4.6 8
South Africa 24.725 25.1 25.35 26.725 27.45 27.125 28.7 36.989
Spain 26.095 24.443 22.058 19.635 17.225 15.255 14.105 16.805
Sri Lanka 4.4 4.3 4.7 4.4 4.2 4.4 4.8 8.35
Sudan 15.2 19.8 21.6 20.6 19.6 19.5 22.1 25
Suriname 6.6 5.5 7 10 7 9 8.953 11.225
Sweden 8 7.933 7.4 6.95 6.683 6.325 6.767 8.669
Switzerland 3.158 3.044 3.178 3.323 3.088 2.547 2.306 3.211
Syria n/a n/a n/a n/a n/a n/a n/a n/a
Taiwan Province of China 4.18 3.96 3.78 3.92 3.76 3.71 3.8 3.9
Tajikistan n/a n/a n/a n/a n/a n/a n/a n/a
Thailand 0.7 0.8 0.9 1 1.2 1.1 1 1
The Bahamas 15.782 14.636 13.379 12.15 10.1 10.35 10.653 25.408
Trinidad and Tobago 3.675 3.3 3.425 3.95 4.825 4.067 n/a n/a
Tunisia 15.33 14.959 15.39 15.544 15.513 15.53 14.889 n/a
Turkey 9.041 9.915 10.279 10.907 10.904 10.955 13.709 14.622
Ukraine 7.172 9.275 9.143 9.45 9.65 9 8.5 11.037
United Kingdom 7.575 6.2 5.375 4.875 4.425 4.075 3.825 5.375
United States 7.358 6.158 5.275 4.875 4.342 3.892 3.667 8.891
Uruguay 6.5 6.625 7.533 7.867 7.925 8.367 8.925 9.677
Venezuela 7.47 6.7 7.4 20.863 27.886 35.543 n/a n/a
Vietnam 2.75 2.1 2.33 2.33 2.21 2.21 2.21 3.3
West Bank and Gaza 23.4 26.9 25.9 26.9 25.45 26.25 25.35 32.208

Source: International Monetary Fund, World Economic Outlook Database, October 2020.