The economic reasons why firms engage in apprenticeship training are twofold. First, apprenticeship training is a potentially cost-effective strategy for filling a firm’s future vacancies, particularly if skilled labor on the external labor market is scarce. Second, apprentices can be cost-effective substitutes for other types of labor in the current production process. As current and expected business and labor market conditions determine a firm’s expected work volume and thus its future demand for skilled labor, they are potentially important drivers of a firm’s training decisions.
Empirical studies have found that the business cycle affects apprenticeship markets. However, while the economic magnitude of these effects is moderate on average, there is substantial heterogeneity across countries, even among those that at first sight seem very similar in terms of their apprenticeship systems. Moreover, identification of business cycle effects is a difficult task. First, statistics on apprenticeship markets are often less developed than labor market statistics, making empirical analyses of demand and supply impossible in many cases. In particular, data about unfilled apprenticeship vacancies and unsuccessful applicants are paramount for assessing potential market failures and analyzing the extent to which business cycle fluctuations may amplify imbalances in apprenticeship markets. Second, the intensity of business cycle effects on apprenticeship markets is not completely exogenous, as governments typically undertake a variety of measures, which differ across countries and may change over time, to reduce the adverse effects of economic downturns on apprenticeship markets. During the economic crisis related to the COVID-19 global pandemic, many countries took unprecedented actions to support their economies in general and reacted swiftly to introduce measures such as the provision of financial subsidies for training firms or the establishment of apprenticeship task forces. As statistics on apprenticeship markets improve over time, such heterogeneity in policy measures should be exploited to improve our understanding of the business cycle and its relationship with apprenticeships.
Autonomes Fahren statt Stop and Go: Vorschläge zur effektiven Gestaltung der deutschen Kurzarbeit
