Labor markets and institutions

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.

Policymakers often propose a minimum wage as a means of  raising incomes and lifting workers out of poverty. However, improvements in some young workers’ incomes due to a minimum wage come at a cost to others. Minimum wages reduce employment opportunities for youths and create unemployment. Workers miss out on on-the-job training opportunities that would have been paid for by reduced wages upfront but would have resulted in higher wages later. Youths who cannot find jobs must be supported by their families or by the social welfare system. Delayed entry into the labor market reduces the lifetime income stream of young unskilled workers.

The shadow (underground) economy has a major impact on society and economy in many countries. People evade taxes and regulations by working in the shadow economy or by employing people illegally. On the one hand, this unregulated economic activity can result in reduced tax revenue and fewer public goods and services, lower tax morale and less tax compliance, higher control costs, and lower economic growth rates. But on the other hand, the shadow economy can be a powerful force fostering institutional change and boosting the overall production of goods and services in an economy. The shadow economy has implications on the political order and institutional change.

Entrepreneurs, creators of new firms, are a rare species. Even in innovation-driven economies, only 1–2% of the work force starts a business in any given year. Yet entrepreneurs, particularly innovative entrepreneurs, are vital to the competitiveness of the economy and may establish new jobs. The gains of entrepreneurship are only realized, however, if the business environment is receptive to innovation. In addition, policymakers need to prepare for the potential job losses that can occur in the medium term through “creative destruction” as entrepreneurs strive for increased productivity.

The Mincer equation—arguably the most widely used in empirical work—can be used to explain a host of economic, and even non-economic, phenomena. One such application involves explaining (and estimating) employment earnings as a function of schooling and labor market experience. The Mincer equation provides estimates of the average monetary returns of one additional year of education. This information is important for policymakers who must decide on education spending, prioritization of schooling levels, and education financing programs such as student loans.

Raising the minimum wage in developing countries could increase or decrease poverty, depending on labor market characteristics. Minimum wages target formal sector workers—a minority in most developing countries—many of whom do not live in poor households. Whether raising minimum wages reduces poverty depends not only on whether formal sector workers lose jobs as a result, but also on whether low-wage workers live in poor households, how widely minimum wages are enforced, how minimum wages affect informal workers, and whether social safety nets are in place.

All developed economies have unemployment benefit programs to protect workers against major income losses during spells of unemployment. By enabling unemployed workers to meet basic consumption needs, the programs protect workers from having to sell their assets or accept jobs below their qualifications. The programs also help stabilize the economy during recessions. If benefits are too generous, however, the programs can lengthen unemployment and raise the unemployment rate. The policy challenge is to protect workers while minimizing undesirable side effects.