JBNST - Vol. 239/4 - 2019

The debate about the impact of technology on employment has always had a central role in economic theory. At the same time, the nexus of technological progress and employment might depend on macroeconomic regimes. In this work we investigate the interrelations among technology, output and employment in the U.S. economy in growth recessions vs. growth expansions. More precisely, using U.S. data we estimate different threshold vector autoregressions (TVARs) with TFP, hours, and GDP, employing the latter as threshold variable, and assess the generalized impulse responses of GDP and hours as to TFP shocks. For our entire period of observation, 1957Q1–2011Q4, positive technology shocks, while spurring GDP growth, by and large, display a negative effect on hours worked in growth recessions, but they are not significantly different from zero in good times. Yet, since the mid eighties (1984Q1–2011Q4) productivity shocks increase hours worked in low growth periods. The results are mainly driven by the response of labor along the extensive margin (number of employees), and remain persistent so in the face of a battery of robustness checks.

Since the instability of housing markets may be quite harmful for the real economy, we explore whether public housing construction programs may tame housing market fluctuations. As a workhorse, we use a behavioral stock-flow housing market model in which the complex interplay between speculative and real forces triggers reasonable housing market dynamics. Simulations reveal that plausible and well-intended policy measures may turn out to be a mixed blessing. While public housing construction programs may reduce house prices, they seem to be incapable of bringing house prices much closer towards their fundamental values. In addition, these programs tend to drive out private housing constructions.

The current situation in the German apprenticeship market is characterized by two contradictory developments. On the one hand, establishments are experiencing increasing problems filling the training positions they offer, and the number of vacant training positions is climbing. On the other hand, the transition into training is still difficult for many young people, and the number of unsuccessful vocational training applicants is rising. Hence, matching supply with demand is becoming increasingly difficult in the German job market for training positions. Using the Linked Employer-Employee dataset (LIAB) from the Institute for Employment Research (IAB), our paper provides a closer examination of the phenomenon of unfilled training positions. It presents an overview of the evolution of vacant training positions in eastern and western Germany and attempts to explain the number of vacancies by investigating the factors responsible for the establishments’ inability to fill their training positions. We assume that training position vacancies are due not only to internal company reasons such as firm size or the wage offer for apprentices but also to external conditions such as general regional factors. Therefore, our analysis also considers the situation on the demand side of the labor market within a region. The results of our multilevel mixed-effects estimations show that in addition to characteristics on the enterprise level, regional determinants also affect the share of vacant apprenticeships. With respect to establishment-related factors, mainly characteristics that describe the attractiveness of the firm prove to be important. With regard to regional-specific factors, we find that the availability of school leavers in a region in addition to the level of regional-sectoral competition impacts the recruiting success of establishments. Our analysis also shows that there are remarkable differences between eastern and western Germany concerning both the quantitative importance of unfilled training positions and the factors affecting them.

We suggest twenty immediately actionable steps to reduce widespread inferential errors related to “statistical significance testing.” Our propositions refer to the theoretical preconditions for using p-values. They furthermore include wording guidelines as well as structural and operative advice on how to present results, especially in research based on multiple regression analysis, the working horse of empirical economists. Our propositions aim at fostering the logical consistency of inferential arguments by avoiding false categorical reasoning. They are not aimed at dispensing with p-values or completely replacing frequentist approaches by Bayesian statistics.