Evidence-based policy making

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.

Environmental regulations raise production costs at regulated firms, though in most cases the costs are only a small fraction of a firm’s total costs. Productivity tends to fall, and firms may shift new investment and production to locations with less stringent regulation. However, environmental regulations have had enormous benefits in terms of lives saved and illnesses averted, especially through reductions in airborne particulates. The potential health gains may be even greater in developing countries, where pollution levels are high. The benefits to society from environmental regulation hence appear to be much larger than the costs of compliance.

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).

Over the past decades, academics worldwide have conducted experiments with fictitious job applications to measure discrimination in hiring. This discrimination leads to underutilization of labor market potential and higher unemployment rates for individuals from vulnerable groups. Collectively, the insights from the published research suggest that three groups face more discrimination than ethnic minorities: people with disabilities, less physically attractive people, and older people. The discrimination found in Western economies generally persists across countries and is stable over time, although some variation exists.

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.

The use of anonymous job applications (or blind recruitment) to combat hiring discrimination is gaining attention and interest. Results from field experiments and pilot projects in European countries (France, Germany, the Netherlands, Finland and Sweden are considered here), Canada, and Australia shed light on their potential to reduce some of the discriminatory barriers to hiring for minority and other disadvantaged groups. But although this approach can achieve its primary aims, there are also important cautions to consider.

There is a strong link between mothers’ primary school completion (8 or more years of schooling) and better socioeconomic outcomes, such as improved child health and reduced teenage fertility, but establishing causality is challenging. A 1997 compulsory schooling law in Turkey, which extended education from five to eight years, provides a natural experiment to identify causal effects. Empirical evidence suggests that increased female education from such reform significantly improves many socioeconomic outcomes of mothers and their children. While suggested mechanisms include changes in healthcare services utilization and risky pregnancy behaviors, such as smoking, thorough investigation of underlying channels is lacking.

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.