Programming and Coding Skills by Country – 2026
Can you write code or program software applications? Programming and coding skills represent advanced digital competencies that enable people to create software, develop applications, and solve complex technical problems. This analysis examines programming and coding skills by country across 92 nations, revealing how populations have adopted these advanced digital skills and the significant disparities in technical literacy that persist globally. Understanding coding proficiency provides insight into broader patterns of digital skill adoption, technology workforce development, and digital inequality between countries. This indicator is widely used to assess advanced ICT skills and global technology workforce readiness. This analysis is based on the latest available UNESCO ICT skills data, with projections extending to 2026.
Programming and coding skills measure the percentage of people who code or program in digital environments including computer software development, app development, and web development. This includes writing code in programming languages, developing applications, and creating software solutions. A proficiency rate of 15% means 15 out of 100 people can code or program, while 85 cannot or do not engage in programming activities. This metric captures advanced digital skills that indicate technical literacy and represents a key indicator of technology workforce development across populations. Programming and coding skills enable creation of digital solutions and software applications. People who code can develop software, create mobile applications, build websites, and solve technical problems through programming. These advanced digital skills are foundational for technology careers, enabling people to work in software development, app creation, and technology innovation. Inability to code limits access to high-value technology careers and restricts participation in digital innovation economies that increasingly depend on software development capabilities. Several countries show exceptionally high programming and coding proficiency rates, indicating populations with strong technical literacy and technology workforce development. Brunei leads with 31.9% (2022), followed by Saudi Arabia (31.8%), and United Arab Emirates (26.0%). These nations combine excellent digital infrastructure, strong technology education, and populations comfortable with advanced programming skills. The high coding proficiency in these countries reflects broader digital skill adoption and significant investment in technology education and workforce development programs. Developed nations in Northern Europe and East Asia show particularly strong adoption rates of programming and coding skills. Oman (20.2%), Malaysia (19.2%), and Bahrain (17.2%) demonstrate strong coding proficiency. These countries have invested in technology education and digital literacy programs that ensure populations can develop software and create applications. The global digital skills gap is evident when comparing these high-performing nations with countries where programming skills remain limited. Many countries show growing programming and coding proficiency, driven by increasing technology education and digital skill initiatives. Countries in Latin America, Southeast Asia, and Eastern Europe demonstrate strong growth trajectories in coding skill adoption as technology education expands and younger populations with native programming skills become larger shares of the population. Chile (16.1%), Tunisia (16.1%), and Kuwait (15.9%) show strong coding proficiency. These emerging markets represent the global digital skill adoption trend toward more universal technology literacy. Developing nations increasingly recognize programming and coding skills as essential for technology workforce development and economic participation. As technology education expands and coding bootcamps and online learning platforms proliferate, populations gain opportunities to develop advanced programming capabilities. However, significant gaps persist between countries with mature technology education systems and those with limited technical training infrastructure. The digital inequality between countries remains a critical challenge for global technology skill adoption. Many countries show low programming and coding proficiency rates, reflecting multiple barriers to technology skill development and technical literacy. Limited access to technology education prevents populations from learning programming languages and coding concepts. Lack of quality computer science education means populations never develop advanced technical skills. Language barriers limit access to programming resources and training materials, as most programming documentation is in English. Older populations show lower proficiency than younger demographics, reflecting generational differences in technology exposure and coding skill adoption. Economic factors significantly influence programming and coding proficiency and broader technology workforce development. Populations in low-income countries often lack access to computers and internet connectivity needed to learn programming. Educational systems in developing nations may not prioritize computer science and coding education. Limited availability of programming training in local languages restricts learning opportunities for non-English speakers. These factors contribute to the global digital skill gaps observed across countries. Programming and coding proficiency creates fundamental opportunities for technology careers and innovation. People who can code can develop software, create applications, and access high-value technology employment opportunities. Organizations benefit from workforces with strong programming skills that enable them to develop software solutions and compete in digital innovation economies. Programming proficiency represents a critical foundation for technology workforce development and digital economic participation. Low programming and coding proficiency creates barriers to technology careers and limits innovation capacity. Countries with low coding proficiency face challenges developing domestic technology sectors and competing in global software development markets. Workers without programming skills cannot access technology careers and become increasingly isolated from high-value digital economic participation. The global digital skill gaps directly impact technology workforce development and economic competitiveness across nations. The 2026 projections show continued growth in programming and coding skills across most countries. High-performing nations like Brunei, Saudi Arabia, and UAE are projected to maintain strong proficiency rates, representing populations where coding skills are increasingly common. Mid-tier countries show growth potential as technology education expands and coding bootcamps proliferate. Low-proficiency countries will likely see accelerating growth in programming skills as online learning platforms expand and younger generations with native coding skills become larger population shares. The global digital skill adoption trend points toward more universal technology literacy. Emerging technologies including artificial intelligence, cloud computing, and low-code development platforms will likely make programming more accessible and intuitive. However, significant gaps will persist between developed and developing nations, and between connected and disconnected populations within countries. Programming and coding proficiency will remain a critical determinant of technology career access and digital inequality between countries. About the Data Data years vary by country (2011–2024). Where recent data is unavailable, projections are applied using historical trends. Year labels in the data table reflect projection targets, not survey years. This approach ensures comprehensive coverage while maintaining methodological transparency. This analysis uses UNESCO Institute for Statistics (UIS) data from ICT skills surveys across 92 countries (2011-2024). The data measures self-reported behavior among individuals aged 15-74 who code or program in digital environments including computer software development, app development, and web development. This UNESCO digital skills data provides comprehensive coverage of programming skill adoption globally. Programming and coding proficiency rate represents: (Number of people who code or program ÷ Total surveyed population aged 15-74) × 100. For example, 20% means 20 out of 100 people code or program to develop software or applications. This metric captures advanced digital skills that indicate technology workforce development and technical literacy adoption. Our dataset includes 82 countries (89%) with current data from 2020-2024, while 10 countries (11%) have older data from 2011-2019. Of the 92 countries in the dataset, 81 had multiple historical data points suitable for linear regression analysis, while 11 countries had single data points. For 2026 projections, we applied linear regression analysis using all available historical data points for each country. Countries with single data points received projections based on growth patterns adjusted for economic development and technology education infrastructure. This approach provides insight into global programming skill adoption rates and technology workforce development patterns. Projections include growth dampening for high-performing countries (>20% current rate) to reflect saturation effects in coding skill adoption. Countries with older data (>5 years) received additional dampening (50% growth reduction) to account for data uncertainty. All estimates are capped at each country's historical maximum observed value to prevent unrealistic projections. For example, if a country's highest recorded programming proficiency was 27.7%, the 2026 projection cannot exceed 27.7%. This approach ensures projections remain grounded in observed technology skill adoption patterns while allowing for modest growth in countries with lower current rates. Survey methodologies follow UNESCO's standardized ICT skills measurement framework, though self-reported proficiency may not capture actual coding ability or application in real-world software development. The UNESCO digital skills data provides valuable insight into global patterns of technology literacy and digital inequality between countries.Understanding Programming and Coding Skills
Programming and Coding Skills by Country – 2026
1
31.9%
2
31.8%
3
26%
4
20.2%
5
19.9%
6
19.2%
7
16.1%
8
16.1%
9
15.9%
10
11.5%
11
10.7%
12
10.2%
13
10.1%
14
9.9%
15
9.8%
16
9.7%
17
9.6%
18
9.4%
19
9.4%
20
9.4%
21
9.3%
22
9.2%
23
9.2%
24
9%
25
8.9%
26
8.8%
27
8.8%
28
8.7%
29
8.7%
30
8.7%
31
8%
32
7.9%
33
7.5%
34
7.3%
35
7%
36
6.9%
37
6.9%
38
6.2%
39
6.1%
40
6%
41
6%
42
5.8%
43
5.8%
44
5.8%
45
5.8%
46
5.7%
47
5.6%
48
5.5%
49
5.4%
50
5.3%
51
5.3%
52
5.2%
53
5.2%
54
5.1%
55
5%
56
5%
57
4.9%
58
4.9%
59
4.9%
60
4.8%
61
4.5%
62
4.4%
63
4.2%
64
4.1%
65
4%
66
3.9%
67
3.9%
68
3.5%
69
3.3%
70
3.2%
71
2.9%
72
2.9%
73
2.9%
74
2.8%
75
2.6%
76
2.6%
77
2.6%
78
2.1%
79
1.7%
80
1.7%
81
1.6%
82
1.5%
83
1.5%
84
1.5%
85
1.5%
86
1.4%
87
1.4%
88
1.4%
89
1.3%
90
1.2%
91
1.2%
92
1.1%
93
1.1%
94
1%
95
1%
96
0.7%
97
0.7%
98
0.5%
99
0.5%
100
0.5%
101
0.1%
Global Leaders in Programming and Coding Skills
Emerging Technology Workforce Development
Barriers to Programming and Coding Proficiency
Programming Skills and Technology Careers
Future Trends in Technology Skill Development
Programming and Coding Skills by Country – 2026
#
1
31.9 (2022)
31.9%
2
31.8 (2023)
31.8%
3
26.0 (2023)
26%
4
20.2 (2023)
20.2%
5
17.2 (2020)
19.9%
6
19.2 (2023)
19.2%
7
16.1 (2023)
16.1%
8
16.1 (2019)
16.1%
9
15.9 (2023)
15.9%
10
11.5 (2023)
11.5%
11
10.7 (2023)
10.7%
12
10.2 (2023)
10.2%
13
10.1 (2021)
10.1%
14
9.9 (2021)
9.9%
15
9.8 (2022)
9.8%
16
9.7 (2023)
9.7%
17
9.6 (2023)
9.6%
18
9.4 (2022)
9.4%
19
9.4 (2023)
9.4%
20
9.4 (2023)
9.4%
21
9.3 (2023)
9.3%
22
9.2 (2023)
9.2%
23
9.2 (2023)
9.2%
24
9.0 (2022)
9%
25
8.9 (2019)
8.9%
26
8.8 (2018)
8.8%
27
13.0 (2023)
8.8%
28
8.7 (2023)
8.7%
29
8.7 (2023)
8.7%
30
8.7 (2019)
8.7%
31
8.0 (2023)
8%
32
7.9 (2022)
7.9%
33
7.5 (2023)
7.5%
34
7.3 (2023)
7.3%
35
7.0 (2020)
7%
36
6.9 (2023)
6.9%
37
6.9 (2018)
6.9%
38
6.2 (2023)
6.2%
39
6.1 (2023)
6.1%
40
6.0 (2017)
6%
41
6.0 (2023)
6%
42
5.8 (2023)
5.8%
43
5.8 (2020)
5.8%
44
5.8 (2023)
5.8%
45
5.8 (2022)
5.8%
46
5.7 (2023)
5.7%
47
5.6 (2023)
5.6%
48
5.5 (2023)
5.5%
49
5.4 (2023)
5.4%
50
5.3 (2015)
5.3%
51
5.3 (2024)
5.3%
52
5.2 (2023)
5.2%
53
5.2 (2021)
5.2%
54
5.1 (2019)
5.1%
55
5.0 (2023)
5%
56
5.0 (2019)
5%
57
4.9 (2023)
4.9%
58
4.9 (2023)
4.9%
59
4.9 (2023)
4.9%
60
4.8 (2014)
4.8%
61
4.5 (2017)
4.5%
62
4.4 (2023)
4.4%
63
4.2 (2023)
4.2%
64
4.1 (2017)
4.1%
65
4.0 (2022)
4%
66
3.9 (2023)
3.9%
67
3.9 (2023)
3.9%
68
3.5 (2017)
3.5%
69
3.3 (2019)
3.3%
70
3.2 (2020)
3.2%
71
2.9 (2016)
2.9%
72
2.9 (2023)
2.9%
73
2.9 (2023)
2.9%
74
2.8 (2022)
2.8%
75
2.6 (2023)
2.6%
76
2.6 (2021)
2.6%
77
2.6 (2023)
2.6%
78
2.1 (2021)
2.1%
79
1.7 (2022)
1.7%
80
1.7 (2023)
1.7%
81
1.6 (2016)
1.6%
82
1.5 (2023)
1.5%
83
1.5 (2021)
1.5%
84
1.5 (2020)
1.5%
85
1.5 (2023)
1.5%
86
1.4 (2023)
1.4%
87
1.4 (2021)
1.4%
88
1.4 (2020)
1.4%
89
1.3 (2021)
1.3%
90
1.2 (2022)
1.2%
91
1.2 (2021)
1.2%
92
1.1 (2023)
1.1%
93
1.1 (2023)
1.1%
94
1.0 (2024)
1%
95
1.0 (2020)
1%
96
0.7 (2018)
0.7%
97
0.7 (2019)
0.7%
98
0.5 (2023)
0.5%
99
0.5 (2023)
0.5%
100
0.5 (2017)
0.5%
101
0.1 (2017)
0.1%
Methodology and Data Sources
Frequently Asked Questions
Q: What does programming and coding proficiency mean and why is it important for technology careers?
A: Programming and coding proficiency measures the percentage of people who can code or program in digital environments such as software development, app creation, and web development. If your country has 18%, it means 18 out of 100 people can write code or develop applications while 82 cannot. This matters because programming and coding skills are advanced digital competencies that enable people to create software solutions, develop applications, and solve technical problems. People with coding proficiency can access high-value technology careers in software development, app development, and technology innovation. Countries with high proficiency like Brunei (31.9%), Saudi Arabia (31.8%), and UAE (26.0%) have populations capable of developing software and creating digital solutions essential for technology sector development. Low-proficiency countries face barriers where populations cannot develop software or create applications, limiting access to technology careers and innovation capacity. Programming and coding skills represent a key indicator of broader technology workforce development and digital economic participation.
Q: Why do some countries have high programming and coding proficiency while others lag significantly behind?
A: Programming and coding proficiency depends on multiple interconnected factors that determine technology workforce development and technical skill adoption across countries. Technology education quality is fundamental—countries with strong computer science education see higher proficiency in coding skills. Access to programming resources and training is critical since people need computers and internet to learn coding. Online learning platforms and coding bootcamps significantly influence proficiency by making programming education more accessible. Younger populations demonstrate higher proficiency than older demographics due to greater exposure to technology education. Economic development generally correlates with higher proficiency. Educational systems that prioritize computer science and coding training produce populations with stronger programming skills. Developed nations like Brunei, Saudi Arabia, and UAE combine excellent technology education, strong digital infrastructure, and comprehensive coding training programs that ensure populations develop advanced programming capabilities. Developing countries often show lower proficiency due to limited technology education, lower computer access, and fewer coding training opportunities, though proficiency is growing as online learning expands and younger generations become larger population shares. The global digital skill gaps reflect broader patterns of technology inequality between countries.
Data Disclaimer: Projected data (future years) are estimates based on mathematical models. Actual values may differ. Learn about our methodology →
Sources
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Updated: 26.01.2026https://databrowser.uis.unesco.org/browser/EDUCATION/UIS-SDG4Monitoring/t4.4/i4.4.1
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