Evidence-based policy making

There is little to no consensus in the academic literature over whether centralised, standardised exams are better for students than teacher assessments. While a growing body of evidence from economics highlights bias in teacher assessments, educationalists and psychologists point to the harm caused by high-stakes exam-related stress and argue that exams and teacher assessments generally agree very closely. This lack of academic consensus is reflected in policy: a wide variety of assessment methods are used across (and even within) countries. Policymakers should be aware of the potential for inequalities in non-blind assessments and consider carefully the consequences of relying on a single method of assessment.

The relationship between female labor force participation and economic development is far more complex than often portrayed in both the academic literature and policy debates. Due to various economic and social factors, such as the pattern of growth, education attainment, and social norms, trends in female labor force participation do not conform consistently with the notion of a U-shaped relationship with gross domestic produc (GDP). Despite the initial impact, Covid-19 did not have a lasting negative effect, on average, on women’s participation. At the same time, some countries have made significant progress in increasing participation rates for women, including those who have started from a lower level.

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.