Evidence-based policy making

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.

Randomized control trials are often considered the gold standard to establish causality. However, in many policy-relevant situations, these trials are not possible. Instrumental variables affect the outcome only via a specific treatment; as such, they allow for the estimation of a causal effect. However, finding valid instruments is difficult. Moreover, instrumental variables (IV) estimates recover a causal effect only for a specific part of the population. While those limitations are important, the objective of establishing causality remains; and instrumental variables are an important econometric tool to achieve this objective.

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.