27 August 2026 - Leading researchers came together for Ä¢¹½ÊÓƵ DESA¡¯s three-part Development Policy Seminar series hosted by the Economic Analysis and Policy Division in July to discuss productivity convergence ¡ª the process through which lower-income economies narrow productivity gaps with more advanced economies.
Discussions covered structural transformation, artificial intelligence, technology diffusion, intangible capital and their implications for inequality and development policy. Insights from the series will inform the World Economic Situation and Prospects 2027, Ä¢¹½ÊÓƵ DESA¡¯s flagship report on the state of the world economy, to be launched in January.
Is productivity catch-up stalling?
The first seminar explored whether lower-income countries are catching up¡ªin economic terms¡ªwith more advanced economies.
Davide Fiaschi (University of Pisa) presented evidence suggesting that this catch-up process is not a stable, continuous process. He argued that the convergence in gross domestic product (GDP) per capita observed during the 2000s may have been temporary, with the poorest countries benefiting relatively little.
Robert Inklaar (University of Groningen) examined the sources of productivity convergence, highlighting the central role of agricultural productivity improvements and labour reallocation out of agriculture. He showed that convergence accelerated after 2000 but has stalled since around 2015, with differences in total factor productivity becoming increasingly important in explaining cross-country productivity gaps.
The global AI productivity divide: Prospects for convergence and divergence
The second seminar explored how AI could reshape productivity growth and convergence across countries. Francesco Filippucci (OECD) argued that while AI has significant potential to raise productivity, gains are likely to be distributed unevenly because countries differ in AI exposure, adoption and complementary capabilities.
Giovanni Melina (IMF) showed that advanced economies generally combine higher AI exposure with stronger AI preparedness, whereas many developing countries face constraints related to digital infrastructure, skills, financing, innovation capacity and regulatory frameworks.
Understanding the firm-level drivers of productivity divergence
The third seminar examined the growing divergence in productivity performance across firms.
Giuseppe Berlingieri (ESSEC Business School) showed that productivity gaps increasingly reflect the inability of laggard firms to catch up with the frontier, rather than exceptional gains among leading firms. He emphasized that successful technology diffusion depends on firms¡¯ absorptive capacity, supported by complementary investments in skills, organizational capabilities, innovation and managerial practices.
Cecilia Jona-Lasinio (Luiss Business School) discussed the growing importance of intangible capital¡ªincluding software, databases, research and development, organizational capital and other knowledge-based assets¡ªas a key driver of productivity growth. She argued that understanding productivity divergence increasingly requires analysing intangible investment together with market power, rather than considering each in isolation.
New policy approaches for inclusive productivity growth
Across the three seminars, speakers highlighted that sustaining productivity growth requires more than technological progress alone. Policies that strengthen firms¡¯ capabilities, support innovation and investment, expand access to finance, improve workforce skills and facilitate technology diffusion will be critical.
Maintaining open trade and knowledge-sharing channels can also help ensure that the benefits of AI and other new technologies are more widely shared, reducing productivity gaps across countries, sectors and firms.
To learn more about Ä¢¹½ÊÓƵ DESA¡¯s work in macroeconomic analysis and research, please visit policy.desa.un.org.