Labor markets and institutions

The Netherlands has long been an example of a highly and centrally institutionalized labor market paying considerable attention to equity concerns. Fracturing of the labor force by the rapid demise of the single-earner model and accelerating immigration, falling union density, and reductions in welfare state provisions have shrunk labor’s market power centrally and decentrally. Wages lagged far behind productivity growth, job security strongly declined and wage inequality increased. This comes to the fore with a lack of offensive union power when after 2016 labor demand accelerated and the economy and employment quickly reached new heights after the pandemic crisis.

The Indian economy entered an ongoing process of trade liberalization, domestic deregulation, and privatization of public sector units in 1991. Since then, per capita output has increased significantly, while the overall unemployment rate has remained moderate. However, labor force participation rates fell sharply, though recovering for women since 2020. Youth unemployment remains high, an overwhelming proportion of the labor force continues to work in the informal sector, labor movement out of agriculture is slow, and there is little evidence of a sustained rise in wages for either unskilled rural or factory workers.

In the first two decades of the 2000s, Turkey has relied on structural change from traditional to modern sectors on the one hand and educational compositional change on the other hand to create formal employment in the modern sector. In 2000 the share of formally employed salaried employees in total employment was less than 40% for men and 30% for women. By 2021, this ration converged to 60% for men and women. Formal employment has increased for both men and women and the gender gap in formal employment declined substantially until 2020. However, relying on structural change and education to improve job quality has likely run its course. Since Covid-19, time-related underemployment has increased from virtually zero to 10% of the labor force and wages are stagnating if not declining.

China experienced significant economic progress over the past few decades, with an annual average GDP growth of approximately 8.6%. Population expansion has certainly been a contributing factor, but that is now changing as China rapidly ages. Rural migrants are set to play a key role in compensating for future labor shortages. However, they still face significant barriers to live in cities permanently, resulting in surging waves of return migration in recent years. Additionally, China faces a low fertility rate of 1.01 births per woman, although the population control policy has been relaxed. Millions of people are employed in the food delivery and courier industry, yet with little social benefit and insurance, which poses potential challenges for China’s labor market stability.

In 2024, Italy's labor market has reached record-high levels of employment and permanent contracts, marking a significant recovery from past downturns. Yet, persistent challenges remain. Youth unemployment and labor market duality remain high, and wages and productivity have stagnated for over two decades. Although several major labor market reforms have aimed to increase flexibility and reduce segmentation, many of their effects remain contested. Female participation has risen and long-term unemployment has declined, yet regional disparities remain deep and persistent, with the south lagging behind. Self-employment is widespread but often low income and non-entrepreneurial, while undeclared work continues to weigh on labor standards and fiscal capacity. Targeted reforms are needed to improve labor market inclusion, reduce fragmentation, and support sustainable growth.

In the early 2000s, Hungary’s employment rate in the working-age population was below 60%. That is now a distant memory, as labor force participation is among the highest in the EU, unemployment is consistently low, and the purchasing power of wages keeps growing at a high rate. While undoubtedly a success story, it is also a cautionary tale of coerced activation, labor market segmentation, rising inequalities, declining social mobility, and strained employment relations.

Arguments for increasing gender diversity on corporate boards of directors by gender quotas range from ensuring equal opportunity to improving firm performance. The introduction of gender quotas in a number of countries, mainly in Europe, has increased female representation on boards. Current research does not unambiguously justify gender quotas on grounds of economic efficiency. In many countries, the number of women in top executive positions is limited, and it is not clear from the evidence that quotas lead to a larger pool of female top executives, who, in turn, are the main pipeline for boards of directors. Thus, other supplementary policies may be necessary if politicians want to increase the number of women in senior management positions.

Machine learning (ML) improves economic policy analysis by addressing the complexity of modern data. It complements traditional econometric methods by handling numerous control variables, managing interactions and non-linearities flexibly, and uncovering nuanced differential causal effects. However, careful validation and awareness of limitations such as risk of bias, transparency issues, and data requirements are essential for informed policy recommendations.

The relationship between technology and employment has always been a source of concern, at least since the first industrial revolution. However, while process innovation can be job-destroying (provided that its direct labor-saving effect is not compensated through market mechanisms), product innovation can imply the emergence of new firms, new sectors, and thus new jobs (provided that its welfare effect dominates the crowding out of old products). Nowadays, the topic is even more relevant because the world economy is undergoing a new technological revolution centred on automation and the diffusion of Artificial Intelligence (AI).

Artificial intelligence (AI) has streamlined processes, improved workforce allocation, and created new jobs to meet the needs of digitalization and automation. Individuals with AI capital experience greater employment opportunities and higher wages, particularly in high-skilled roles and large firms. Training in AI helps reduce gender-based digital disparities, empowers individuals, and enhances their employability. Policymakers should promote inclusive AI development policies to prevent widening AI-related divides and unemployment, and to ensure equitable opportunities.