Summary

The K-Shaped Economy describes a growing divide in which some individuals and sectors benefit from digital transformation, advanced skills, and asset ownership, while others face declining opportunities due to job displacement, income pressures, digital exclusion, and the accelerating impact of artificial intelligence. This divergence is reshaping labour markets and increasing the risk of long-term economic and social inequality.

The report argues that non-profit organizations play a critical role in helping vulnerable groups remain on an upward trajectory through skills development, digital inclusion, and workforce-focused support. However, sustainable progress requires more than training alone. Effective responses depend on coordinated efforts that combine evidence-based interventions, employer partnerships, social protection, public policy, and responsible innovation.

Introduction

The concept of the K-Shaped Economy emerged following the COVID-19 pandemic to describe a pattern of uneven recovery in which some sectors and social groups advance while others fall behind. Unlike traditional recovery models that assume a shared economic trajectory, the K-Shaped Economy reflects a widening divide driven by differences in access to skills, technology, capital, and economic opportunities. While some individuals benefit from digital transformation, remote work, and rising asset values, others face job displacement, declining income, and increasing barriers to participation in the modern economy.

Recent technological developments, particularly the rapid adoption of artificial intelligence, are accelerating these disparities. International reports indicate that significant portions of the global workforce will experience substantial changes in job requirements, creating growing demand for reskilling and workforce adaptation. At the same time, persistent gaps in digital access and human development continue to limit the ability of many individuals and communities to benefit from emerging economic opportunities.

In this context, the role of non-profit sector organizations becomes increasingly important. Through capacity building, digital inclusion initiatives, workforce development programs, and support services targeting vulnerable groups, these organizations can help reduce the risk of individuals and households moving into the downward trajectory of the K-Shaped Economy. However, evidence suggests that sustainable impact depends on aligning interventions with labour market needs, building strong employer partnerships, and supporting long-term economic participation.

This article examines the implications of the K-Shaped Economy for social development and explores how non-profit organizations can contribute to reducing economic and social disparities in an era of rapid technological and economic transformation.

Research Design

Purpose and Scope

Based on what has been reported in the publications of international organizations, such as reports by the United Nations and the United Nations Development Program (UNDP), the World Bank, the International Labor Organization (ILO), and the World Economic Forum (WEF), which have addressed labor market transformations and the widening of development gaps in the post-pandemic phase, the scope of this research is defined within a global analytical framework. It examines the phenomenon of the K-Shaped Economy and the associated manifestations of uneven economic recovery between countries and within societies. The research focuses on analyzing the role that non-profit sector organizations can play in limiting the effects of this disparity and in strengthening the economic and social inclusion of groups most vulnerable to marginalization.

The research proceeds from a comparative approach that draws on international data and reports published in both Arabic and English, without being confined to a specific geographic scope. It also draws selectively on several applied models in countries and regions for which published studies and evaluations are available on social development and capacity-building programs, such as the United States, India, Kenya, and some countries in the Arab region. This orientation allows comparative international experience to be used in analyzing the mechanisms through which the role of non-profit sector organizations can be strengthened in reducing economic gaps, supporting job opportunities, and promoting fairness in the distribution of the gains of economic growth (J-PAL, 2022).

Research Methodology and Sources

This research relies on a descriptive and analytical approach supported by induction, with the aim of analyzing the economic and social transformations associated with the phenomenon of the K-Shaped Economy and interpreting the roles that non-profit sector organizations can play in limiting the widening of economic and social gaps. The methodology adopted is based on analyzing international references and reports, published data, and the extrapolation of the key indicators and trends contained therein, followed by their interpretation and an analysis of their implications considering contemporary economic and development literature.

Within this framework, the research adopted a multi-source analytical methodology based on three main categories of data and references, as follows:

• International and institutional reports: The research drew on a set of official reports and working papers issued by international organizations and research institutions, such as the International Monetary Fund, the International Labor Organization, the United Nations Development Program, the World Bank, the International Telecommunication Union, the Organization for Economic Co-operation and Development, and the World Economic Forum. These sources were relied upon because of their scientific credibility and methodological transparency, as these entities clearly publish their measurement methodologies, the datasets used, and the limits of statistical inference. This makes them essential references in comparative economic and development studies.
• Labor market analyses and applied economic data: The research benefited from economic analyses and labor market data derived from reports by global economic and consulting institutions, especially those that rely on analyzing job advertisement data and the skills required in markets. Among the sources used were reports by firms such as PricewaterhouseCoopers (PwC) and Goldman Sachs, which provide estimates on productivity growth trends, changes in required skills, and wage levels associated with new technologies such as artificial intelligence.
• Impact evaluation studies of development programs: The research also relied on studies that evaluate the impact of development programs implemented by non-profit sector organizations or their associated partnerships, giving priority to studies that use experimental or quasi-experimental methodologies in impact evaluation, such as studies using Randomized Controlled Trials or quasi-experimental evaluation designs. Among the entities whose reports were consulted in this field are well-known evaluation institutions such as MDRC, in addition to public policy evaluation firms such as Abt Global, given their methodological studies on the effectiveness of labor market–linked training programs and economic inclusion programs (PwC, 2025).
• The reliability criteria adopted are:
• Transparency of methodology and definitions.
• The recency of data to the greatest extent possible, especially in relation to artificial intelligence (2023–2025/2026).
• The presence of institutional review or endorsement.

The possibility of verification across multiple sources when a claim is sensitive.

Time Scope

In analyzing productivity, jobs, and skills, the research relied on data and estimates covering the period 2018–2024/2025, to allow comparison between the phase preceding the spread of generative artificial intelligence and the phase that followed it. Some analyses were based on PwC reports that measure changes in the labor market before and after the expansion of the use of generative artificial intelligence since 2022, while global labor market indicators (WESO) and digital connectivity data relied on the most recent editions available for the period 2023–2024.

The K-Shaped Economy as a Framework for Social Inequality

The K-Shaped Economy describes an economic and social trajectory in which outcomes diverge among population groups or sectors, such that one group’s ability to recover or advance rises (the ascending arm of the Latin letter K), while another group deteriorates or slows down (the descending arm of the same letter). The Abt Global report links this concept to showing how higher-income households benefited from remote work, rising asset values, and digital transformation, while low-wage workers were exposed to job layoffs, longer labor market disruption, and inflationary pressure that erodes real wages, with a warning that rapid technological changes, such as artificial intelligence, may widen this gap¹ (Abt Global, 2022).

To support this definition with data, a study by the U.S. Bureau of Labor Statistics was reviewed. The study uses official survey data to track recovery by wage segments in the United States after the COVID-19 pandemic. It found that lower-wage jobs experienced a sharper decline and more persistent losses compared with higher-wage jobs, with a clear gap in employment levels between groups continuing through the early stages of recovery (J-PAL, 2022).

At a broader level, the United Nations Development Program adds an international dimension to this phenomenon. After nearly two decades of relative improvement in human development indicators, the gaps between countries at the top of the index and those at its base began widening again, especially since 2020. This indicates that the K-Shaped Economy pattern is not limited to inequality within a single country, but also extends to relations between countries, depending on differences in their capacities to invest in education, health, and digital infrastructure.

Through the analysis of the relevant literature and reports, four main mechanisms can be identified as contributing to the formation of the ascending and descending paths in the K-Shaped Economy:

• Disparity in asset ownership and the ability to absorb shocks: Individuals or groups that possess assets or the ability to save benefit from rising asset values, while groups that rely on nominal wages are harmed as these wages are eroded by inflation.
•  The skills gap and the acceleration of technological change: Knowledge-based and technology sectors adopt new innovations at a faster pace, leading to higher wages in these sectors and creating new barriers to entry into the labor market.
• The digital divide and the ability to access technologies: Weak access to the internet, devices, and digital skills, as well as the ability to engage professionally with artificial intelligence, limits opportunities for learning, work, and services, and deepens the path of economic and social decline.
•  Imbalances in social protection and job quality: The expansion of temporary or unstable forms of work reduces individuals’ ability to withstand economic shocks and reposition themselves in the labor market (UNDP, 2024).

Based on the foregoing, the K-Shaped Economy does not merely reflect economic disparity; it also expresses deeper social outcomes. In this context, social development consists of strengthening individuals’ ability to participate in a dignified and effective manner in the labor market and services, and reducing the social risks associated with inequality, such as poverty, weak social integration, and a declining sense of participation in decision-making. This intersects with what the United Nations Development Program emphasizes: that the path of development is not measured by income alone but also includes individuals’ sense of empowerment and their ability to influence the trajectories of their lives and communities (MDRC, 2016).

The Impact of Artificial Intelligence on Jobs and Skills

Most of the literature indicates that contemporary analyses focus on the automobility of tasks more than on the complete disappearance of jobs. A single job often consists of a set of tasks, some of which may be automated, while others require human intervention. As a result, the impact of artificial intelligence generally tends toward reshaping the nature of work and transforming its components, rather than eliminating it entirely.

This distinction appears clearly in the analysis of the International Monetary Fund, which differentiates between the degree to which jobs are exposed to technologies and the actual impact of these technologies on the labor market. Its estimates indicate that around half of jobs in advanced economies are exposed, to varying degrees, to the effects of artificial intelligence. Some jobs may be negatively affected because of the substitution of certain tasks, while other jobs may benefit from the integration of human capabilities with intelligent technologies, thereby enhancing productivity and opening new opportunities for professional and economic growth.

Three sets of data help us understand how artificial intelligence fuels the K-Shaped Economy:

1. Exposure and displacement estimate at the macroeconomic level: International Monetary Fund estimates indicate that around 40% of jobs worldwide are exposed to varying degrees, to being affected by artificial intelligence, with the share rising to nearly 60% in advanced economies. Goldman Sachs also estimates that generative artificial intelligence could raise global output by around 7%, or approximately USD 7 trillion annually, and that up to 300 million full-time jobs could be affected through the reshaping of their workflows. The estimates stress that this exposure does not mean the complete disappearance of jobs, but rather the partial or full restructuring of their tasks (UN Bahrain, 2024).
2. Market evidence of accelerating skills change: PwC’s analysis indicates that wages associated with artificial intelligence skills are around 56% higher than comparable jobs without those skills. Industries more exposed to artificial intelligence recorded revenue-per-employee growth of 27%, compared with 9% in less-exposed industries. The skills required in the most exposed jobs are also changing at a faster rate, reaching 66%. This acceleration means that those who do not keep pace with training face wage stagnation and a gradual decline in their professional opportunities, which in practical terms reflects the two-arm dynamic of the K-Shaped Economy.

Differences in exposure by job type, gender, and group: A working paper by the International Labor Organization on generative artificial intelligence indicates that clerical work is among the most exposed categories: 24% of its tasks fall within the high-exposure category and 58% within medium exposure, with these estimates considered an upper bound for exposure. In the Arab region, United Nations reports in Bahrain summarize the findings of the International Labor Organization by noting that around 14.6% of jobs may benefit from augmentation through artificial intelligence, compared with 2.2% that may be fully automatable. A gender gap also appears: the share of women’s jobs exposed to automation is higher than that of men’s, while their potential benefit from augmentation is also higher (U.S. Bank, 2026).

When these indicators are brought together, it becomes clear that artificial intelligence acts as a multiplier of the K-Shaped Economy through three main channels:

1. Raising the productivity of those who possess digital skills and tools.
2. Accelerating the obsolescence of skills in specific jobs, which leads to their faster professional depreciation.
3. Transferring the burden of risk to groups with weaker social protection, particularly workers in the informal economy or those with low incomes.

Reports by the International Labor Organization reinforce this trend. They indicate that informal employment reached nearly two billion workers in 2024, with the phenomenon of working poverty persisting. This means that a broad segment is entering the age of artificial intelligence while already in a vulnerable position.

Digital access is not a secondary detail, but a basic condition for benefiting from opportunities in training and digital work. Data from the International Telecommunication Union indicate that 2.6 billion people were offline in 2024, with a clear gap between high-income countries, where connectivity reaches 93%, and low-income countries, where it stands at only 27%.

In this sense, artificial intelligence does not create this division out of a vacuum in the economic structure of countries. Rather, it deepens an existing divide and redistributes opportunities within a single economy according to the ability to access digital technologies, the level of skills, and the availability of social protection.

How the Non-Profit Sector Contributes to Social Development within a K-Shaped Economy

The value of the non-profit sector increases in the context of a K-Shaped Economy when it moves from merely providing fragmented services to playing a role in building a fair transition system. In other words, it should not be limited to providing training or temporary assistance, but should instead design an integrated pathway that combines training linked to actual labor market demand, social support that reduces barriers facing target groups, and an active influence in the public sphere that ensures a fairer distribution of productivity gains. This shift is consistent with what international reports emphasize: that skills gaps and the pace of technology integration are decisive factors in preventing the widening of the social gap (IMF, 2024).

Accordingly, the roles of the non-profit sector can be organized into four interconnected tracks:

1. The service track: Designing and implementing reskilling and upskilling programs directed at specific groups, alongside supportive services such as transportation, childcare, and career guidance. Evidence from sectoral training programs indicates that operating costs fall within a moderate range, in exchange for a tangible impact on beneficiaries’ income in some cases. This makes such programs a social development tool with both economic and social returns.
2. The public policy–related track: Producing data and field evidence, advocating for funding policies and results-based grants, expanding the scope of funded training, and linking social protection systems to labor market transitions, such as providing temporary income support during training periods. This track aims to reduce the individual risks associated with occupational transition, so that individuals do not bear the cost of reskilling alone.
3. The private sector partnerships track: Moving from a general training offering to building skills supply chains in partnership with employers. This includes designing curricula in collaboration with them, providing practical training, and linking programs to actual employment. Research evaluations indicate that the difference between high-impact programs and lower-impact ones is related to the maturity of employer relationships and the quality of implementation (BLS, 2021).
4. The operational and social innovation track: Testing low-cost models for blended learning and using artificial intelligence as a tool to personalize training and guidance, with strict governance of data and privacy, so that technology does not become a new source of risk for vulnerable groups. This is consistent with international warnings that artificial intelligence may deepen inequality between countries and within them if governance is absent or local adoption is delayed.

In this sense, the role of the non-profit sector is not limited to mitigating the effects of the K-Shaped Economy; it extends to reshaping the transition pathways within it, through demand-driven interventions, supported by public policies, and built on genuine productive partnerships.

Table (1) Comparison of Non-Profit Sector Intervention Models

Methodological Note:

Under the item “training with practical training,” the cost is an inferred approximation, because the open evaluation source reviewed focuses on net return and impact, and does not provide a direct breakdown of total cost in the available section. Therefore, we treated it as a range rather than a definitive figure.

Table (2) Comparison of the Population Groups Most Vulnerable to Marginalization in a K-Shaped Economy

Figure (1) Relationship between the K-Shaped Economy and Social Outcomes

Figure (2) Typical Non-Profit Organization Intervention to Bridge the Skills Gap

Case Studies: What Worked, and What Failed?

This research addressed several cases for which published impact evaluations are available and can be relied upon to distinguish what succeeds in moving individuals from the descending arm closer to the ascending arm.

Case One — Year Up as a model of intensive training followed by practical training in partner companies.

A summary of an evaluation conducted by Abt Global, which was based on a randomized trial in several cities, shows that participants’ quarterly earnings after seven years were around 28% higher (USD 1,895) compared with the comparison group, with a social return estimated at around USD 2.46 for every dollar of cost spent on training, and a net gain to society of around USD 34,328 per participant. This evidence indicates that integrating intensive training with on-the-job practical training and partnerships can produce a relatively sustained impact and income (WEF, 2025).

Case Two — Per Scholas as a model of targeted sectoral training.

An evaluation conducted by MDRC showed that the impact was achieved primarily through higher wages, not through increased employment. Participants’ income in the seventh year reached around USD 40,494 compared with USD 35,651 for the control group, a difference of USD 4,844, with an increase in the share of those earning more than USD 40,000 annually.

In terms of cost, it ranged between USD 5,200 and USD 6,700 per participant, with a lower net cost in some sites. This means that the program did not so much expand opportunities to enter the market as it improved the quality of jobs and income within it. The issue was that a later evaluation after ten years showed that the effect had begun to fade and was no longer statistically significant in some sites after the ninth year. This indicates that improvement is possible, but its continuation is not guaranteed. In rapidly changing sectors, one-time training is not sufficient; continuous updating of skills is required.

Case Three — Generation as a youth-oriented training model with an independent evaluation.

An independent evaluation conducted by Mathematica in 2019 on a sample of program graduates in India showed that 44% of trainees were employed, around 19 percentage points higher than the control group, and that their total income was around 75% higher. In a parallel evaluation in Kenya, the employment rate reached 55% among graduates, compared with 34% among non-admitted applicants. This means that success here was not due to training, but rather to program design, careful selection of beneficiaries, highly focused training, and direct linkage to employment.

However, the important caveat here is that the general literature indicates that many training programs do not achieve a tangible impact, even in high-income countries. The U.S. Job Corps program, despite its large government funding and operating contracts, showed in its expanded evaluations (Schochet et al., 2008) that its effect on wages faded after four years for most age groups. Similarly, J-PAL reviews (2022) found that most youth-oriented training programs in low-income countries do not reach the threshold of statistical significance. This means that success is not the rule, but rather the result of design quality, implementation quality, and contextual suitability, and that replicating a successful model outside its context may lead to opposite outcomes.

Case Four — YouthBuild as a broader social intervention model (education + training + youth development).

The MDRC evaluation showed that, after four years, there was improvement in obtaining a high school credential, an increase in enrollment in education, and an increase of around 19% in weekly wages according to survey data. However, the same results did not appear clearly in official records, and the short-term benefits did not decisively exceed the costs. This means that comprehensive social interventions may achieve an impact, but they are slower, more difficult to measure, and their outcomes depend on the long term.

Analytical conclusion from the cases

What succeeds in reducing gaps in the K-Shaped Economy through the non-profit sector is repeated across three elements: (1) close linkage to labor market demand and employer partnerships, (2) supportive services that reduce barriers to regular attendance and completion, and (3) post-employment follow-up and subsequent skills updating, because skills change rapidly.

Impact Measurement, Limitations, and Recommendations

In a K-Shaped Economy, impact measurement is not a luxury, but a condition for funding and sustainability. Therefore, we recommend that measurements be built on two levels:

•  Individual level: (a) training completion, (b) acquisition of a verifiable certificate/skill, (c) employment in a target sector within 3–6 months, (d) job retention for 6 to 12 months, and (e) income differential compared with a baseline or a comparison group after 1–3 years. The findings of MDRC and Abt show that long-term income measures provide a more accurate picture than immediate measures, and that the impact of some models may appear or fade over a period of years.

• System level: (a) cost per successful transition (Cost per placement/retention), (b) the share of beneficiaries from highly vulnerable groups (women, low-skilled workers, informal workers, and poorly connected areas), (c) fair measurement of the digital divide (connectivity, use, skills), and (d) impact on reliance on benefits or debt, when data are available, as noted in the Year Up evaluation.

The main recurring limitations and challenges include:

• Funding: Effective interventions are not always inexpensive, and some require corporate partnerships or public funding. The absence of multi-year funding pushes organizations toward short-term measurements that do not capture the real income effect.
• Implementation capacity: Work Advance evaluations show substantial variation across different sites, with explanations linked to the maturity of relationships with employers and local expertise. This means that “copying a successful model” without building local implementation capacity may produce a weak impact.
• Public policies: The WEF report notes that skills gaps are the greatest barrier to business transformation, and that training funding and training policies are among the most welcomed policies. In the absence of policy support, training will remain fragmented and insufficient, especially if 59 out of every 100 workers will need training by 2030.
• Risks of reliance on technology: International reports warn that artificial intelligence may exacerbate inequality between countries and within them if local adoption is delayed, governance is absent, or digital infrastructure is unavailable. This makes “digital inclusion and data governance” part of social development, not a sidetrack.

Table (3): Policy Recommendations by Time Horizon

A Reading in the National Context

The frameworks of the K-Shaped Economy apply to the local context of the Kingdom of Saudi Arabia in a distinctive way, as three trajectories intersect within it: a broad economic transformation led by Vision 2030, a labor market that is reshaping the relationship between citizens and the private sector, and a system of government tools for qualifying cadres and providing employment support.

Data from the General Authority for Statistics show that the unemployment rate among Saudis stood at 6.8% in the second quarter of 2025, then rose to 7.5% in the third quarter, bringing it close to Vision 2030’s target of 7% (GASTAT, 2025). The state responds to skills gaps through the Human Resources Development Fund (HADAF), which is a key instrument for implementing Saudization policies and qualifying cadres, alongside the Taqat platform as the national labor portal.

In the same context, Vision 2030 has made the non-profit sector a developmental actor through the National Center for Non-Profit Sector, which aims to raise the sector’s contribution to GDP to 5% by 2030. The Center’s 2024 annual report shows that the direct contribution of associations and institutions under its supervision reached 0.99%, and that their number has grown by 252% since the launch of Vision. The share of specialized organizations supporting development priorities reached 92%, the beneficiary satisfaction index exceeded 88%, and the target of one million volunteers was achieved six years ahead of schedule (NCNP, 2024). However, the report “Non-Profit Sector Outlook 2025,” issued by King Khalid Foundation and based on data from the General Authority for Awqaf and the General Authority for Statistics, presents a broader picture using a more comprehensive methodology that combines the spending of organizations, awqaf, and cooperative associations, as well as the economic value of volunteering. According to this methodology, the sector’s total contribution exceeded the SAR 100 billion threshold for the first time, equivalent to 3.3% of GDP, distributed across awqaf (SAR 48 billion), organizational spending (SAR 47 billion), volunteering (SAR 5 billion), and cooperative associations (SAR 2 billion) (King Khalid Foundation, 2025).

Yet the two pictures converge around one challenge: workers in the sector account for no more than 0.64% of the total labor force. This means that the sector’s role in absorbing national cadres and reskilling those affected by digital transformation is still in the formative stage. From here, three practical priorities emerge: first, expanding partnerships between the non-profit sector and the government and private sectors to undertake reskilling programs for groups most exposed to job displacement; second, adapting evaluated international models such as Year Up, Per Scholas, and Generation to the specificities of the national context, rather than importing them literally; and finally, building local evaluation capacity based on experimental and quasi-experimental methodologies, so that training programs become measurable interventions before scaling, rather than initiatives measured by the size of spending alone.

Closing

The analysis of the K-Shaped Economy shows that inequality is no longer a transient phenomenon, but a structural feature of contemporary economies, one that deepens with digital transformation and the acceleration of technological change. In this context, it is no longer sufficient to deal with outcomes; rather, it has become necessary to intervene in the transition pathways themselves, to ensure fairness of opportunity and enable access to resources and skills.

The non-profit sector emerges as a pivotal actor in this transformation, not only through service provision, but also through building integrated systems that support a just transition in the labor market, strengthen economic and social inclusion, and link training, policies, and actual market needs.

Ultimately, reducing the gaps of the K-Shaped Economy requires integrated efforts among the non-profit, government, and private sectors, within a comprehensive development vision that balances economic efficiency with social justice, and ensures that technological transformations become shared opportunities rather than factors that deepen inequality.

References

• Abt Global (2022) Benefits that last: long-term impact and cost-benefit findings for Year Up. Available at: https://www.abtglobal.com/insights/publications/report/benefits-that-last-long-term-impact-and-cost-benefit-findings-for-year (Accessed: 25 April 2026).
• General Authority for Statistics (GASTAT) (2025) Labor market statistics, Q3 2025. Available at: https://www.stats.gov.sa/w/news/146 (Accessed: 20 May 2026).
• International Labor Organization (ILO) (2024) World employment and social outlook. Available at: https://www.ilo.org/sites/default/files/2024-06/WESO_May2024%20-%20Final_30-05-24_2.pdf (Accessed: 25 April 2026).
• International Monetary Fund (IMF) (2024) Artificial intelligence and the future of work. Available at: https://www.imf.org/-/media/files/publications/sdn/2024/english/sdnea2024001.pdf (Accessed: 25 April 2026).
• International Telecommunication Union (ITU) (2024) Measuring digital development: facts and figures 2024. Available at: https://www.itu.int/itu-d/reports/statistics/wp-content/uploads/sites/5/2024/11/2402588_1e_Measuring-digital-development-Facts-and-Figures-2024_v4.pdf (Accessed: 25 April 2026).
• King Khalid Foundation (2025) Non-profit sector outlook 2025 (آفاق القطاع غير الربحي 2025). Available at: https://kkf.org.sa/media/nllewn4j/trends2025_0.pdf (Accessed: 20 May 2026).
• MDRC (2016) Encouraging evidence on a sector-focused advancement strategy. Available at: https://www.towardsemployment.org/wp-content/uploads/2016_Workadvance_Final_Web.pdf (Accessed: 25 April 2026).
• MDRC (2022) Employment and earnings effects of the Work Advance demonstration after seven years. Available at: https://www.mdrc.org/work/publications/employment-and-earnings-effects-workadvance-demonstration-after-seven-years/file-full (Accessed: 25 April 2026).
• MIT Poverty Action Lab (J-PAL) (2022) Evidence review: sectoral employment programs. Available at: https://www.povertyactionlab.org/sites/default/files/publication/Evidence-Review_Sectoral-Employment_2222022_0.pdf (Accessed: 25 April 2026).
• National Center for Non-Profit Sector (NCNP) (2025) Annual report 2024 (التقرير السنوي للمركز الوطني لتنمية القطاع غير الربحي لسنة 2024). Available at: https://ncnp.gov.sa/ar/reports (Accessed: 20 May 2026).
• PwC (2025) Global AI jobs barometer. Available at: https://www.pwc.com/gx/en/news-room/press-releases/2025/ai-linked-to-a-fourfold-increase-in-productivity-growth2025. Available at:
• https://www.pwc.com/gx/en/issues/artificial-intelligence/ai-jobs-barometer.html (Accessed: 25 April 2026).
• United Nations Development Program (UNDP) (2024) Human development report 2023/2024. Available at: https://www.undp.org/sites/g/files/zskgke326/files/2024-03/hdr2023-24overviewen.pdf (Accessed: 25 April 2026).
• United Nations Bahrain (2024) Digitalization and artificial intelligence promise to boost jobs in Arab states. Available at: https://bahrain.un.org/en/302538-digitalization-and-artificial-intelligence-promise-boost-jobs-arab-states (Accessed: 25 April 2026).
• U.S. Bank (2026) K-shaped economy report. Available at: https://www.usbank.com/content/dam/usbank/en/documents/pdfs/corporate-and-commercial-banking/k-economy.pdf (Accessed: 25 April 2026).
• U.S. Bureau of Labor Statistics (BLS) (2021) Working papers. Available at: https://www.bls.gov/osmr/research-papers/2021/pdf/ec210020.pdf (Accessed: 25 April 2026).
• World Economic Forum (WEF) (2025) The future of jobs report 2025. Available at: https://www.weforum.org/publications/the-future-of-jobs-report-2025/digest/ (Accessed: 25 April 2026).

Tags: Articles