What does it mean to be unemployed? 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 provide for yourself and your family—unemployment, we know, has many negative ramifications. However, translating each person’s situation into data and data into policies that can improve the situation of millions of unemployed 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 can threaten growth and social cohesion, they often disagree on how best to quantify joblessness since there are multiple methods of appraising the nuances of a given labor market.
The official unemployment rate is determined by dividing the number of individuals without jobs by the sum total of the labor force. The trouble starts when it comes to figuring out who exactly is—and is not—part of 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 of what they previously made even though they continue to seek additional part-time or freelance work to supplement their income is by default classified as “employed” while another person who actively seeks work but takes a few weeks off from job-hunting is not counted as part of the labor force. An individual who would like to work but is unable to get a job due to a disability or medical condition is in the very same position.
The result is that many economists believe—because of the existence of persons who are unemployed or under-employed—that labor market statistics are inherently skewed and paint a too-rosy picture of the workforce.
Labor Statistics And Labor Force Complexity
Tracking the labor market is made even more complicated when different tracking tools tell different—and sometimes conflicting—stories. Whether through census-type methods, employment office records, surveys of a sample of the population or multi-approach techniques, these datasets will only offer an approximate reflection of the economic and social health of a country.
Despite these limitations, a country’s unemployment rate remains a crucial metric of the health, level of development and growth trajectory of an economy. Rising unemployment results in loss of income for individuals and reduced tax revenue which forces governments to spend greater amounts on unemployment benefits and social subsidies. Long-term unemployment can also weaken a country’s social fabric, lead to mass frustration with and rejection of democratic political orders, prompt cross-border migrations or threaten the economies of trading partners.
Taking into account the many reservations about the inadequacies of workforce tracking methods, how is the global workforce faring four years after the onset of the 2020 pandemic?
Assuming a 48-hour working week, the UN’s International Labor Organization (ILO) has estimated that both the unemployment rate and the jobs gap have declined below pre-pandemic levels. In 2023, the global unemployment rate was 5.1%, a 0.2% improvement over 2022. The global jobs gap—the number of individuals who want employment, regardless of whether they are currently available or searching—narrowed in 2023 to 435 million, down from close to 500 million in 2020, 476 million in 2021 and over 440 million in 2022. However, progress was uneven. The labor force participation rate increased in high-income countries (+0.3%) and lower-middle-income countries (+1.5%), but in low-income and upper-middle-income countries the labor force participation rate fell (by 0.1% and 0.3%, respectively). Even within affluent G20 countries, high inflation rates and rising housing costs significantly eroded much of the recent nominal wage gains.
Furthermore, rates of informal work are also expected to remain static in 2024, accounting for approximately 58% of the global workforce while youth unemployment rates are 3.5 times higher than those of adults and global labor force participation rates of women lag those of men by 25%.
But whereas such broad numbers give us a hint of where jobs are today, they often suggest little about their nature and where they will go eventually. In the span of just a few months, Covid-19 rapidly accelerated developments that were already, albeit gradually, 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 also managed to renew fears that automation will replace entire job categories: robots can assemble car parts, robots can scrub floors, robots can pick up vegetables.
In a pre-pandemic report, 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 could be fully automated using demonstrated technology, McKinsey concluded, was actually small: less than 5%. However, it was also noted, partial automation was set to affect almost all occupations to a greater or lesser degree, with about 60% of them having at least 30% of activities that could be performed by machines. In a follow-up survey of company executives conducted after the pandemic began, McKinsey confirmed that the adoption of automation has accelerated “moderately” or “significantly” in nearly seven businesses out of 10 examined.
It especially worth nothing that long before Covid it was commonly assumed that blue-collar jobs would most likely be the ones eliminated by automation, especially in manufacturing. Tesla CEO Elon Musk promised that a world of self-driving cars, taxis and trucks was not far off.
With the pandemic behind us, it’s clear that many of these assumptions and aspirations have proven to be faulty at best. As Covid-19 spread and governments imposed lockdowns, companies rushed to automate and digitalize their operations, and this mainly affected white-collar occupations. Tech and logistics businesses went on a hiring spree to address the demand for services and products for those working remotely or sheltering in place. Four years on since Covid-19 upended global supply chains, many companies—including Tesla—have reversed course with layoffs and hiring freezes.
Perhaps even more remarkably—while the advancements in automation and robotics aimed at carrying out tasks that would otherwise be done manually have been incremental—those in AI technology have been nothing but extraordinary. Such progress could ultimately lead to the compression of wages of people who make their living by manipulating words, data and visual elements rather than physical objects. With artificial intelligence now appearing to be taking the place—or performing some tasks—of workers with college degrees in higher-paying positions, we might be experiencing the first major shift in the white-collar market being caused by modern technology.
So, should we resign ourselves to a future of high unemployment and job insecurity sparing no one? The truth, as a famous quote goes, is that prediction is always very difficult, especially if it is about the future. In the near term, the most immediate threat to our labor markets is inflation according to the International Monetary Fund (IMF). Taming it will come at a cost: typically, when interest rates increase, so do unemployment rates and wage cuts.
Looking further ahead, predictions will likely continue to be partially or entirely wrong: according to the World Economic Forum, in the coming years artificial intelligence (AI) will destroy 85 million jobs (but generates 97 million new jobs). Goldman Sachs estimates that AI systems could expose the equivalent of 300 million full-time jobs to automation. According to various other sources, this figure could go up to 800 million and even 1 billion. McKinsey’s projections indicate that work activities that absorb 60%–70% of an employee’s time today could soon be automated while the IMF believes artificial intelligence could affect about 60% of workers in advanced economies, 40% in emerging market economies and 26% in low-income nations.
Unemployment Rates Around the World 2024
| Country | 2017 | 2018 | 2019 | 2020 | 2021 | 2022 | 2023 | 2024 |
|---|---|---|---|---|---|---|---|---|
| Albania | 13.7 | 12.3 | 11.47 | 11.675 | 11.4 | 11.1 | 11 | 11 |
| Algeria | 11.709 | 11.731 | 11.383 | n/a | n/a | n/a | n/a | n/a |
| Andorra | 2.35 | 1.775 | 2.075 | 2.925 | 3.3 | 2.1 | 1.5 | 1.5 |
| Argentina | 8.35 | 9.2 | 9.825 | 11.55 | 8.75 | 6.825 | 6.575 | 8 |
| Armenia | 17.8 | 19 | 18.3 | 18.2 | 15.5 | 13 | 12.5 | 13 |
| Aruba | 8.923 | 7.283 | 5.2 | 8.6 | 8.8 | 6.6 | 5.691 | 6.728 |
| Australia | 5.593 | 5.3 | 5.173 | 6.5 | 5.094 | 3.697 | 3.669 | 4.21 |
| Austria | 5.933 | 5.208 | 4.808 | 5.475 | 6.175 | 4.767 | 5.1 | 5.398 |
| Azerbaijan | 4.961 | 4.944 | 5.004 | 7.244 | 6.039 | 5.646 | 5.583 | 5.521 |
| Bahrain | 4.1 | 4.3 | 4.7 | 5.9 | 5.9 | 7.7 | n/a | n/a |
| Barbados | 9.95 | 10.05 | 10.088 | 15.724 | 14.1 | 8.467 | 8.438 | 8.228 |
| Belarus | 5.684 | 4.825 | 4.191 | 4.082 | 3.893 | 3.58 | 3.455 | 3.044 |
| Belgium | 7.117 | 5.975 | 5.375 | 5.575 | 6.267 | 5.558 | 5.533 | 5.514 |
| Belize | 9.328 | 9.377 | 9.041 | 13.743 | 10.188 | 6.071 | 3.4 | 3.4 |
| Bhutan | 3.138 | 3.4 | 2.72 | 5.03 | 4.8 | 5.9 | 3.5 | n/a |
| Bolivia | 5.085 | 4.913 | 5.012 | 8.336 | 6.937 | 4.74 | 4.9 | 5 |
| Bosnia and Herzegovina | 20.5 | 18.4 | 15.7 | 15.9 | 17.355 | 15.386 | 13.3 | 13.3 |
| Brazil | 12.85 | 12.375 | 11.975 | 13.775 | 13.2 | 9.25 | 7.975 | 8.032 |
| Brunei Darussalam | 9.3 | 8.7 | 6.82 | 7.3 | 4.91 | 5.91 | 4.9 | 4.9 |
| Bulgaria | 6.23 | 5.273 | 4.275 | 5.207 | 5.346 | 4.206 | 4.419 | 4.3 |
| Cabo Verde | 12.2 | 12.2 | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 | 8.5 |
| Canada | 6.408 | 5.85 | 5.7 | 9.725 | 7.483 | 5.267 | 5.408 | 6.323 |
| Chile | 6.965 | 7.377 | 7.223 | 10.77 | 8.862 | 7.878 | 8.84 | 8.713 |
| China | 5 | 4.9 | 5.2 | 5.2 | 5.1 | 5.5 | 5.2 | 5.1 |
| Colombia | 9.675 | 9.942 | 10.883 | 16.675 | 13.8 | 11.208 | 10.1 | 9.872 |
| Costa Rica | 9.293 | 11.951 | 12.417 | 19.98 | 13.68 | 11.669 | 7.297 | 8.289 |
| Croatia | 12.433 | 9.858 | 7.758 | 9 | 8.092 | 6.783 | 6.217 | 5.77 |
| Cyprus | 11.05 | 8.35 | 7.075 | 7.575 | 7.475 | 6.775 | 6.135 | 5.856 |
| Czech Republic | 2.89 | 2.176 | 1.96 | 2.512 | 2.732 | 2.166 | 2.579 | 2.56 |
| Denmark | 5.825 | 5.125 | 5 | 5.625 | 5.092 | 4.475 | 4.892 | 4.892 |
| Dominican Republic | 5.509 | 5.656 | 6.167 | 5.829 | 7.384 | 5.293 | 6.2 | 6 |
| Ecuador | 4.62 | 3.69 | 3.84 | 5.346 | 4.15 | 3.19 | 3.7 | 4.2 |
| Egypt | 12.245 | 10.932 | 8.612 | 8.296 | 7.292 | 7.323 | 7.185 | 7.074 |
| El Salvador | 7.05 | 6.35 | 6.34 | 6.9 | 6.3 | 5 | 5.5 | 5.5 |
| Estonia | 5.763 | 5.371 | 4.448 | 6.806 | 6.177 | 5.571 | 6.376 | 8.058 |
| Fiji | 4.5 | 4.5 | 4.5 | 13.351 | 9 | 6.5 | 5.5 | 5 |
| Finland | 8.825 | 7.425 | 6.725 | 7.767 | 7.625 | 6.767 | 7.208 | 7.599 |
| France | 9.417 | 9.025 | 8.425 | 8.033 | 7.875 | 7.308 | 7.413 | 7.357 |
| Georgia | 21.6 | 19.2 | 17.6 | 18.5 | 20.6 | 17.3 | 16.4 | 15.7 |
| Germany | 3.567 | 3.208 | 2.975 | 3.625 | 3.575 | 3.067 | 3.008 | 3.288 |
| Greece | 21.45 | 19.3 | 17.325 | 16.325 | 14.775 | 12.425 | 10.891 | 9.357 |
| Honduras | 5.528 | 5.648 | 5.386 | 10.912 | 8.569 | 8.9 | 8.082 | 8.005 |
| Hong Kong SAR | 3.124 | 2.805 | 2.916 | 5.808 | 5.175 | 4.319 | 2.9 | 2.813 |
| Hungary | 4 | 3.6 | 3.3 | 4.125 | 4.05 | 3.6 | 4.125 | 4.4 |
| Iceland | 3.283 | 3.1 | 3.925 | 6.433 | 6.017 | 3.75 | 3.383 | 3.838 |
| Indonesia | 5.5 | 5.24 | 5.18 | 7.07 | 6.49 | 5.86 | 5.32 | 5.2 |
| Ireland | 6.767 | 5.808 | 5 | 5.842 | 6.242 | 4.45 | 4.317 | 4.385 |
| Islamic Republic of Iran | 12.075 | 12.125 | 10.65 | 9.6 | 9.175 | 9 | 9 | 8.9 |
| Israel | 4.217 | 3.983 | 3.808 | 4.3 | 4.967 | 3.758 | 3.467 | 3.7 |
| Italy | 11.292 | 10.617 | 9.9 | 9.358 | 9.525 | 8.117 | 7.658 | 7.828 |
| Jamaica | 11.65 | 9.125 | 7.7 | 10.2 | 8.375 | 6.267 | 4.4 | n/a |
| Japan | 2.825 | 2.442 | 2.358 | 2.783 | 2.808 | 2.592 | 2.567 | 2.5 |
| Jordan | 18.3 | 18.6 | 19.075 | 22.7 | 24.075 | 22.85 | n/a | n/a |
| Kazakhstan | 4.872 | 4.828 | 4.794 | 4.925 | 4.9 | 4.875 | 4.775 | 4.775 |
| Korea | 3.683 | 3.833 | 3.783 | 3.942 | 3.675 | 2.883 | 2.7 | 3 |
| Kosovo | 30.475 | 29.5 | 25.65 | 25.95 | 20.75 | 12.575 | n/a | n/a |
| Kyrgyz Republic | 6.891 | 6.922 | 6.922 | 8.656 | 9.014 | 9.014 | 9.014 | 9.014 |
| Latvia | 8.715 | 7.415 | 6.311 | 8.099 | 7.557 | 6.851 | 6.503 | 6.452 |
| Lithuania | 7.073 | 6.146 | 6.254 | 8.488 | 7.113 | 5.925 | 6.558 | 6.3 |
| Luxembourg | 5.843 | 5.103 | 5.406 | 6.363 | 5.746 | 4.817 | 5.23 | 5.993 |
| Macao SAR | 1.975 | 1.8 | 1.725 | 2.55 | 2.95 | 3.675 | 2.65 | 2 |
| Malaysia | 3.425 | 3.325 | 3.275 | 4.525 | 4.65 | 3.825 | 3.625 | 3.525 |
| Malta | 4 | 3.658 | 3.633 | 4.358 | 3.408 | 2.917 | 2.5 | 2.5 |
| Mauritius | 7.1 | 6.9 | 6.7 | 9.2 | 9.1 | 6.803 | 6.27 | 6.27 |
| Mexico | 3.42 | 3.321 | 3.489 | 4.409 | 4.14 | 3.278 | 2.796 | 2.798 |
| Moldova | 4.125 | 3.05 | 5.125 | 3.825 | 3.25 | 4.6 | 4.457 | 3.498 |
| Mongolia | 8.8 | 7.8 | 10 | 7 | 8.1 | 6.7 | 6.03 | 5.427 |
| Morocco | 10.2 | 9.5 | 9.2 | 11.9 | 12.3 | 11.8 | 13 | 11.995 |
| Netherlands | 5.873 | 4.881 | 4.436 | 4.851 | 4.226 | 3.537 | 3.553 | 3.9 |
| New Zealand | 4.75 | 4.325 | 4.125 | 4.6 | 3.775 | 3.3 | 3.725 | 5.036 |
| Nicaragua | 3.67 | 5.5 | 5.341 | 6.564 | 11.1 | 7.518 | 7.157 | 6.763 |
| Nigeria | 17.462 | 22.562 | n/a | n/a | n/a | n/a | n/a | n/a |
| North Macedonia | 22.375 | 20.725 | 17.25 | 16.375 | 15.425 | 14.375 | 14.3 | 14.1 |
| Norway | 4.216 | 3.854 | 3.728 | 4.595 | 4.41 | 3.252 | 3.6 | 3.8 |
| Pakistan | 5.832 | 5.8 | 6.9 | 6.562 | 6.3 | 6.2 | 8.5 | 8 |
| Panama | 6.13 | 5.956 | 7.07 | 18.548 | 11.293 | 8.8 | 7.431 | 8.4 |
| Paraguay | 6.086 | 6.236 | 6.569 | 7.708 | 7.51 | 6.808 | 6.19 | 5.995 |
| Peru | 6.877 | 6.64 | 6.574 | 13.078 | 10.763 | 7.742 | 6.787 | 6.6 |
| Philippines | 5.725 | 5.325 | 5.1 | 10.4 | 7.783 | 5.4 | 4.35 | 5.083 |
| Poland | 4.888 | 3.846 | 3.279 | 3.163 | 3.373 | 2.874 | 2.82 | 2.928 |
| Portugal | 9.208 | 7.175 | 6.675 | 7.15 | 6.692 | 6.142 | 6.583 | 6.484 |
| Puerto Rico | 10.8 | 9.2 | 8.3 | 8.8 | 8 | 6 | 6.852 | 6.677 |
| Romania | 6.092 | 5.25 | 4.892 | 6.075 | 5.608 | 5.625 | 5.567 | 5.6 |
| Russia | 5.2 | 4.8 | 4.6 | 5.783 | 4.825 | 3.942 | 3.167 | 3.117 |
| São Tomé and Príncipe | 13.472 | n/a | n/a | n/a | n/a | n/a | n/a | n/a |
| San Marino | 8.095 | 8.009 | 7.663 | 7.342 | 5.238 | 4.318 | 4.045 | 3.945 |
| Saudi Arabia | 5.9 | 6.025 | 5.625 | 7.65 | 6.6 | 5.6 | n/a | n/a |
| Serbia | 14.493 | 13.673 | 11.193 | 9.728 | 11.008 | 9.396 | 9.458 | 9.4 |
| Seychelles | 3 | 3 | 3 | 3 | 3 | 3 | 3 | 3 |
| Singapore | 2.175 | 2.1 | 2.25 | 3 | 2.65 | 2.1 | 1.925 | 1.9 |
| Slovak Republic | 8.058 | 6.508 | 5.717 | 6.633 | 6.808 | 6.175 | 5.842 | 5.9 |
| Slovenia | 6.575 | 5.125 | 4.45 | 5 | 4.725 | 4 | 3.675 | 3.721 |
| South Africa | 27.45 | 27.125 | 28.7 | 29.175 | 34.3 | 33.5 | 32.8 | 33.465 |
| Spain | 17.225 | 15.255 | 14.105 | 15.533 | 14.785 | 12.918 | 12.115 | 11.642 |
| Sri Lanka | 4.2 | 4.4 | 4.8 | 5.5 | 5.1 | 5.25 | n/a | n/a |
| Sudan | 19.6 | 19.5 | 22.1 | 26.83 | 28.328 | 32.137 | 45.961 | 49.543 |
| Suriname | 7 | 9 | 8.797 | 11.147 | 11.2 | 10.9 | 10.6 | 10.3 |
| Sweden | 6.783 | 6.433 | 6.908 | 8.483 | 8.892 | 7.483 | 7.667 | 8.365 |
| Switzerland | 3.088 | 2.547 | 2.306 | 3.17 | 2.993 | 2.166 | 2.035 | 2.25 |
| Türkiye | 10.918 | 10.907 | 13.731 | 13.147 | 11.96 | 10.466 | 9.388 | 9.6 |
| Taiwan | 3.76 | 3.71 | 3.73 | 3.85 | 3.95 | 3.67 | 3.67 | 3.67 |
| Thailand | 1.2 | 1.1 | 1 | 1.7 | 1.9 | 1.3 | 1.2 | 1.1 |
| The Bahamas | 10.1 | 10.35 | 10.1 | 26.218 | 17.646 | 10.814 | 8.79 | 8.79 |
| Tunisia | 15.513 | 15.53 | 14.889 | 17.4 | 16.2 | 15.2 | 16.4 | n/a |
| Ukraine | 9.65 | 9 | 8.5 | 9.15 | 9.835 | 24.528 | 19.072 | 14.514 |
| United Kingdom | 4.45 | 4.175 | 3.925 | 4.65 | 4.625 | 3.875 | 4.025 | 4.15 |
| United States | 4.358 | 3.892 | 3.675 | 8.092 | 5.35 | 3.633 | 3.625 | 3.993 |
| Uruguay | 7.925 | 8.367 | 8.925 | 10.35 | 9.375 | 7.867 | 8.325 | 8.09 |
| Uzbekistan | 5.83 | 9.347 | 8.976 | 10.531 | 9.625 | 8.851 | 8.351 | 7.851 |
| Venezuela | 27.886 | 35.554 | n/a | n/a | n/a | n/a | n/a | n/a |
| Vietnam | 2.24 | 2.19 | 2.17 | 2.48 | 3.2 | 2.32 | 2.01 | 2.063 |
| West Bank and Gaza | 25.45 | 26.25 | 25.35 | 25.925 | 26.392 | 24.42 | 28.65 | n/a |
