Unemployment Rates Around the World 2023

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.


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, especially in these tumultuous times, is rather complex.

Despite the fear of Covid-19 waning, the consequences for jobs and livelihoods across the globe are still very much with us. The pandemic had especially severe effects on the most economically vulnerable people, informally employed women and younger workers in particular. The burden of the crisis 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 were already on the rise in many advanced, emerging and developing economies. Lockdowns and school closures did 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. Covid-19, quite simply, erased decades of progress on poverty reduction and pushed tens of millions of people into job insecurity.

Such a scenario 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.

Three years on since Covid-19 upended global supply chains, many companies—including Amazon, IBM, Alphabet and Microsoft—have reversed course with layoffs and hiring freezes.”

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 of the workforce. Needless to say, unemployment and hidden under-employment 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 (and, eventually, how many gained back) since the beginning of the pandemic?

Assuming a 48-hour working week, the UN’s International Labor Organization (ILO) has estimated that in 2020 the 8.8% of the total global working hours were lost, equivalent to the hours worked in one year by 255 million full-time employees. Around half of those losses were due to the reduced hours of those who remained employed, while half were the result of those jobs being completely eliminated. Had there been no pandemic, about 30 million new jobs would have been created.

However, by the end of 2021, employment had returned to pre-crisis levels or even exceeded them in the majority of high-income countries, while deficits persisted in most low and middle-income economies: “Overall, global labor income surpassed its pre-crisis level by 0.9 percent in 2021, driven by high-income countries and China,” the ILO stated in a report. Yet that upward trend concealed important disparities: in 2021, three out of five workers lived in countries where labor incomes had not yet recovered to their level prior to the crisis.

Just as things were getting better, even that measure of progress that had been made up until that moment was thwarted last year by the Russian aggression on Ukraine and the rise of inflation rates globally. The ILO has estimated that in the third quarter of 2022 the level of hours worked worldwide was 1.5% below pre-pandemic levels, corresponding to a deficit of 40 million full-time jobs for that quarter alone. Based on current trends, the United Nations body stated, once all the data for the last quarter of 2022 is collected and analyzed it will show a further deterioration in year-on-year global employment growth. Not only that, the outlook for the current year does not look brighter: in its “World Employment and Social Outlook: Trends 2023,” the ILO forecasts that global employment growth will be only 1.0% this year, less than half of last year’s level.

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.”

Also, the report indicates that women and young people will particularly continue struggling, with women’s global labor force participation rate standing at 47.4% versus 72.3% for men: this means that for every inactive male worker, there are two inactive female workers. Young people between 15 and 24 too are at a disadvantage in finding and maintaining quality jobs: their unemployment rate is three times that of adults. Overall, global unemployment is projected to reach 208 million people in 2023, with an unemployment rate of 5.8%.

But 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 a 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 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.

Long before Covid-19 spread globally, it was commonly assumed that blue-collar jobs would be 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—and the attendant social distancing-driven automation of jobs—behind us, it’s clear that these assumptions and aspiration have proven to be faulty at best. As Covid-19 spread and governments imposed lockdowns to ease the surge of sick patients into national health care systems, companies rushed to automate and digitalize their operations and this mainly affected white-collar jobs. 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. The productivity gains made during this period often turned out to be minimal and the return to in-person office work necessitating undoing many of the pandemic-era labor force changes. Three years on since Covid-19 upended global supply chains, many companies—including Amazon, IBM, Alphabet and Microsoft—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 IMF. Taming it will come at a cost: typically, when interest rates increase, so do unemployment rates and wage cuts. Looking further ahead, automation and artificial intelligence applications could ultimately create more jobs than they automate and replace: the World Economic Forum estimates that 85 million jobs will be displaced by the robot revolution, but at the same time—by 2025—97 million new ones will also be created. Jobs disappear only to be replaced by new positions to execute previously unimaginable tasks. Hang tight.

*Values are expressed in terms of a percentage.

Country/Territory 2016 2017 2018 2019 2020 2021 2022 2023
Albania 15.2 13.7 12.3 11.5 11.9 10.6 10.3 10.0
Algeria 10.5 11.7 11.7 11.4 N/A N/A N/A N/A
Andorra 3.0 1.7 1.5 1.8 2.9 2.9 2.0 1.8
Argentina 8.5 8.4 9.2 9.8 11.6 8.7 6.9 6.9
Armenia 18.0 17.8 19.0 18.3 18.1 15.3 15.2 15.1
Aruba 7.7 8.9 7.3 5.2 8.6 8.8 7.8 7.7
Australia 5.7 5.6 5.3 5.2 6.5 5.1 3.6 3.7
Austria 6.5 5.9 5.2 4.8 5.4 6.2 4.5 4.6
Azerbaijan 5.0 5.0 4.9 4.9 7.2 5.6 5.9 5.8
Bahrain 3.7 3.6 3.9 4.7 5.9 6.6 5.6 4.4
Barbados 9.7 10.0 10.1 10.1 21.3 14.1 10.6 10.0
Belarus 5.9 5.7 4.8 4.2 4.1 3.9 4.5 4.3
Belgium 7.9 7.2 6.0 5.5 5.8 6.3 5.5 5.6
Belize 9.5 9.3 9.4 9.0 13.7 10.2 8.5 8.0
Bhutan 2.1 3.1 3.4 2.3 5.0 4.8 N/A N/A
Bolivia 4.7 5.1 4.9 5.2 8.5 7.0 4.5 4.0
Bosnia and Herzegovina 25.4 20.5 18.4 15.7 15.9 17.4 17.3 17.2
Brazil 11.7 12.9 12.4 12.0 13.8 13.2 9.8 9.5
Brunei Darussalam 8.5 9.3 8.7 6.8 6.8 6.8 6.8 6.8
Bulgaria 7.7 6.2 5.3 4.3 5.2 5.4 5.1 4.7
Cabo Verde 15.0 12.2 12.2 8.5 8. 12.2 8.5 8.5
Canada 7.1 6.4 5.3 4.3 5.2 5.3 5.1 4.7
Chile 6.7 7.0 7.4 7.2 10.8 8.9 7.9 8.4
China 4.0 3.9 3.8 3.6 4.2 4.0 4.2 4.1
Colombia 9.2 9.3 9.5 10.4 15.9 13.8 11.3 11.1
Costa Rica 9.5 9.3 12.0 12.4 20.0 13.7 12.5 13.2
Croatia 15.0 12.4 9.9 7.8 9.0 8.1 6.9 6.6
Cyprus 13.0 11.1 8.4 7.1 7.6 7.5 6.7 6.5
Czech Republic 4.0 2.9 2.2 2.0 2.5 2.8 2.5 2.3
Denmark 6.0 5.8 5.1 4.0 5.6 5.1 5.2 5.3
Dominican Republic 7.1 5.5 5.7 6.2 5.8 7.4 6.4 6.2
Ecuador 5.2 4.6 3.7 3.8 5.4 4.2 4.0 3.8
Egypt 12.7 12.3 10.9 8.6 8.3 7.3 7.3 7.3
El Salvador 7.0 7.1 6.4 6.3 12.0 10.3 8.2 7.5
Estonia 6.8 5.8 5.4 4.5 6.8 6.2 6.6 6.8
Fiji 5.5 4.5 4.5 4.5 13.4 9.0 6.5 5.5
Finland 9.0 8.8 7.4 6.7 7.8 7.6 7.1 7.4
France 10.1 9.4 9.0 8.4 8.0 7.9 7.5 7.6
Georgia 21.7 21.6 19.2 17.6 18.5 20.6 18.7 19.5
Germany 3.9 3.6 3.2 3.0 3.6 3.6 2.9 3.4
Greece 23.6 21.5 19.3 17.3 16.4 15.0 12.6 12.2
Honduras 6.7 5.5 5.7 5.4 10.9 8.6 4.6 4.6
Hong Kong SAR 3.4 3.1 2.8 2.9 5.8 5.2 4.5 4.0
Hungary 5.0 4.0 3.6 3.3 4.12 4.1 3.4 3.9
Iceland 3.3 3.3 3.1 3.9 6.4 6.0 4.0 4.0
Indonesia 5.6 5.5 5.2 5.2 7.1 6.5 5.5 5.3
Ireland 8.4 6.8 5.8 5.0 5.8 6.3 4.7 4.8
Iran 12.4 12.1 12.1 10.7 9.6 9.2 9.4 9.7
Israel 4.8 4.2 4.0 3.8 4.3 5.0 3.9 3.8
Italy 11.7 11.3 10.6 9.9 9.3 9.5 8.8 9.4
Jamaica 13.2 11.7 10.6 9.9 9.3 9.5 8.8 9.4
Japan 3.1 2.8 2.4 2.4 2.8 2.8 2.6 2.4
Jordan 15.3 18.3 18.6 19.1 22.7 24.4 N/A N/A
Kazakhstan 4.9 4.9 4.8 4.8 4.9 4.9 4.9 4.8
Kosovo 27.5 30.5 29.5 25.6 26.0 25.8 N/A N/A
Kuwait 1.3 1.3 1.1 1.2 1.3 1.3 N/A N/A
Kyrgyz Republic 7.2 6.9 6.9 6.9 8.7 9.0 9.0 9.0
Latvia 9.6 8.7 7.4 6.3 8.1 7.6 7.4 7.2
Lithuania 7.9 7.1 6.2 6.3 8.5 7.1 7.3 7.0
Luxembourg 6.3 5.8 5.1 5.4 6.4 5.7 5.0 5.0
Macao SAR 1.9 2.0 1.8 1.7 2.6 3.0 3.0 2.7
Malaysia 3.5 3.4 3.3 3.3 4.5 4.7 4.5 4.3
Malta 4.7 4.0 3.7 3.6 4.4 3.5 3.2 3.3
Mauritius 7.3 7.1 6.9 6.7 9.2 9.1 7.7 7.4
Mexico 3.9 3.4 3.3 3.5 4.4 4.1 3.4 3.7
Moldova 4.2 4.1 3.1 5.1 3.8 3.5 3.5 3.5
Mongolia 10.0 8.8 7.8 10.0 7.0 8.1 7.3 6.6
Morocco 9.0 10.6 9.4 10.2 12.2 11.9 11.1 10.7
Netherlands 7.0 5.9 4.9 4.4 4.9 4.2 3.5 3.9
New Zealand 5.2 4.7 4.3 4.1 4.6 3.8 3.4 3.9
Nicaragua 4.5 3.7 5.5 6.2 7.2 11.1 7.5 7.2
Nigeria 13.4 17.5 22.6 N/A N/A N/A N/A N/A
North Macedonia 23.8 22.4 20.7 17.3 16.4 15.7 15.3 15.0
Norway 4.7 4.2 3.9 3.7 4.6 4.4 3.9 3.8
Pakistan 5.9 5.8 5.8 6.9 6.6 6.3 6.2 6.4
Panama 5.5 6.1 6.0 7.1 18.6 11.3 9.5 10.0
Paraguay 6.0 6.1 6.2 6.6 7.7 7.7 7.2 6.4
Peru 6.7 6.9 6.7 6.6 13.9 10.9 7.6 7.5
Philippines 5.5 5.7 5.3 5.1 10.4 7.8 5.7 5.4
Poland 6.3 5.0 3.9 3.3 3.2 3.4 2.8 3.2
Portugal 11.5 9.2 7.2 6.7 7.1 6.6 6.1 6.5
Puerto Rico 11.8 10.8 9.2 8.3 8.9 7.9 6.0 7.9
Romania 5.9 5.2 4.8 4.6 5.8 4.8 4.0 4.3
Russia 5.5 5.2 4.8 4.6 5.8 4.8 4.0 4.3
São Tomé and Príncipe 13.4 13.5 N/A N/A N/A N/A N/A N/A
San Marino 8.6 8.1 8.0 7.7 7.3 6.1 5.9 5.7
Saudi Arabia 5.6 6.0 6.0 5.7 7.4 6.7 N/A N/A
Serbia 16.9 14.9 14.1 11.6 10.1 10.1 9.9 9.7
Seychelles 2.7 3.0 3.0 3.0 3.0 3.0 3.0 3.0
Singapore 2.1 2.2 2.1 2.3 3.0 2.7 2.1 2.1
Slovak Republic 9.7 8.1 6.5 5.7 6.6 6.8 6.2 6.2
Slovenia 8.0 6.9 5.1 4.4 5.0 4.8 4.3 4.3
South Africa 26.7 27.5 27.1 28.7 29.2 34.3 34.6 35.6
South Korea 3.7 3.7 3.8 3.8 3.9 3.7 3.0 3.4
Spain 19.6 17.2 15.3 14.1 15.5 14.8 12.7 12.3
Sri Lanka 4.4 4.3 4.7 4.4 4.2 4.4 4.8 8.35
Sudan 20.6 19.6 19.5 22.1 26.83 28.33 30.6 30.6
Suriname 10.0 7.0 9.0 8.8 11.2 11.2 10.9 10.6
Sweden 7.2 6.9 6.5 7.0 8.5 8.8 7.6 7.4
Switzerland 3.3 3.1 2.6 2.3 3.2 3.0 2.2 2.4
Syria 10.9 10.9 10.9 13.7 13.2 12.0 10.8 10.5
Taiwan 3.9 3.8 3.7 3.7 3.9 4.0 3.6 3.6
Thailand 1.0 1.2 1.1 1.0 2.0 1.5 1.0 1.0
The Bahamas 12.2 10.1 10.4 10.1 25.5 18.2 13.9 12.7
Tunisia 15.5 15.5 15.5 14.9 17.4 16.2 N/A N/A
Turkey 10.9 10.9 10.9 13.7 13.2 12.0 10.8 10.5
Ukraine 9.5 9.7 9.0 8.5 9.2 9.8 N/A N/A
United Kingdom 4.9 4.4 4.1 3.8 4.6 4.5 3.8 4.8
United States 4.9 4.4 3.9 3.7 8.1 5.4 3.7 4.6
Uruguay 7.9 7.9 8.4 8.9 10.4 9.4 7.9 7.9
Uzbekistan 5.2 5.8 9.4 9.0 10.5 9.5 10.0 9.5
Venezuela 20.9 27.9 35.6 N/A N/A N/A N/A N/A
Vietnam 2.3 2.2 2.2 2.2 2.5 2.7 2.4 2.3
West Bank and Gaza 26.9 25.5 26.3 25.4 25.9 26.4 25.7 25.0

Source: International Monetary Fund, World Economic Outlook Database, October 2022.

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