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We investigate how inviting students to set task-based goals affects usage of an online learning platform and course performance. We design and implement a randomized field experiment in a large mandatory economics course with blended learning elements. The low-cost treatment induces students to use the online learning system more often, more intensively, and to begin earlier with exam preparation. Treated students perform better in the course than the control group: they are 18.8% (0.20 SD) more likely to pass the exam and earn 6.7% (0.19 SD) more points on the exam. There is no evidence that treated students spend significantly more time, rather they tend to shift to more productive learning methods. The heterogeneity analysis suggests that higher treatment effects are associated with higher levels of behavioral bias but also with poor early course behavior.
High growth firms (HGFs) are important for job creation and considered to be precursors of economic growth. We investigate how formal institutions, like product- and labor-market regulations, as well as the quality of regional governments that implement these regulations, affect HGF development across European regions. Using data from Eurostat, OECD, WEF, and Gothenburg University, we show that both regulatory stringency and the quality of the regional government influence the regional shares of HGFs. More importantly, we find that the effect of labor- and product-market regulations ultimately depends on the quality of regional governments: in regions with high quality of government, the share of HGFs is neither affected by the level of product market regulation, nor by more or less flexibility in hiring and firing practices. Our findings contribute to the debate on the effects of regulations by showing that regulations are not, per se, “good, bad, and ugly”, rather their impact depends on the efficiency of regional governments. Our paper offers important building blocks to develop tailored policy measures that may influence the development of HGFs in a region.
The present paper proposes a novel approach for equilibrium selection in the infinitely repeated prisoner’s dilemma where players can communicate before choosing their strategies. This approach yields a critical discount factor that makes different predictions for cooperation than the usually considered sub-game perfect or risk dominance critical discount factors. In laboratory experiments, we find that our factor is useful for predicting cooperation. For payoff changes where the usually considered factors and our factor make different predictions, the observed cooperation is consistent with the predictions based on our factor.
While the economic harm of cartels is caused by their price-increasing effect, sanctioning by courts rather targets at the underlying process of firms reaching a price-fixing agreement. This paper provides experimental evidence on the question whether such sanctioning meets the economic target, i.e., whether evidence of a collusive meeting of the firms and of the content of their communication reliably predicts subsequent prices. We find that already the mere mutual agreement to meet predicts a strong increase in prices. Conversely, express distancing from communication completely nullifies its otherwise price-increasing effect. Using machine learning, we show that communication only increases prices if it is very explicit about how the cartel plans to behave.
The experimental literature on antitrust enforcement provides robust evidence that communication plays an important role for the formation and stability of cartels. We extend these studies through a design that distinguishes between innocuous communication and communication about a cartel, sanctioning only the latter. To this aim, we introduce a participant in the role of the competition authority, who is properly incentivized to judge communication content and price setting behavior of the firms. Using this novel design, we revisit the question whether a leniency rule successfully destabilizes cartels. In contrast to existing experimental studies, we find that a leniency rule does not affect cartelization. We discuss potential explanations for this contrasting result.
Numerous studies investigate which sanctioning institutions prevent cartel formation but little is known as to how these sanctions work. We contribute to understanding the inner workings of cartels by studying experimentally the effect of sanctioning institutions on firms’ communication. Using machine learning to organize the chat communication into topics, we find that firms are significantly less likely to communicate explicitly about price fixing when sanctioning institutions are present. At the same time, average prices are lower when communication is less explicit. A mediation analysis suggests that sanctions are effective in hindering cartel formation not only because they introduce a risk of being fined but also by reducing the prevalence of explicit price communication.
This paper sheds new light on the role of communication for cartel formation. Using machine learning to evaluate free-form chat communication among firms in a laboratory experiment, we identify typical communication patterns for both explicit cartel formation and indirect attempts to collude tacitly. We document that firms are less likely to communicate explicitly about price fixing and more likely to use indirect messages when sanctioning institutions are present. This effect of sanctions on communication reinforces the direct cartel-deterring effect of sanctions as collusion is more difficult to reach and sustain without an explicit agreement. Indirect messages have no, or even a negative, effect on prices.
A rich literature links knowledge inputs with innovative outputs. However, most of what is known is restricted to manufacturing. This paper analyzes whether the three aspects involving innovative activity - R&D; innovative output; and productivity - hold for knowledge intensive services. Combining the models of Crepon et al. (1998) and of Ackerberg et al. (2015), allows for causal interpretation of the relationship between innovation output and labor productivity. We find that knowledge intensive services benefit from innovation activities in the sense that these activities causally increase their labor productivity. Moreover, the firm size advantage found for manufacturing in previous studies nearly disappears for knowledge intensive services.
The COVID-19 pandemic created the largest experiment in working from home. We study how persistent telework may change energy and transport consumption and costs in Germany to assess the distributional and environmental implications when working from home will stick. Based on data from the German Microcensus and available classifications of working-from-home feasibility for different occupations, we calculate the change in energy consumption and travel to work when 15% of employees work full time from home. Our findings suggest that telework translates into an annual increase in heating energy expenditure of 110 euros per worker and a decrease in transport expenditure of 840 euros per worker. All income groups would gain from telework but high-income workers gain twice as much as low-income workers. The value of time saving is between 1.3 and 6 times greater than the savings from reduced travel costs and almost 9 times higher for high-income workers than low-income workers. The direct effects on CO₂ emissions due to reduced car commuting amount to 4.5 millions tons of CO₂, representing around 3 percent of carbon emissions in the transport sector.
Optimal carbon pricing with fluctuating energy prices — emission targeting vs. price targeting
(2022)
Prices of primary energy commodities display marked fluctuations over time. Market-based climate policy instruments (e.g., emissions pricing) create incentives to reduce energy consumption by increasing the user cost of fossil energy. This raises the question of whether climate policy should respond to fluctuations in fossil energy prices? We study this question within an environmental dynamic stochastic general equilibrium (E-DSGE) model calibrated on the German economy. Our results indicate that the welfare implications of dynamic emissions pricing crucially depend on how the revenues are used. When revenues are fully absorbed, a reduction in emissions prices stabilizes the economy in response to energy price shocks. However, when revenues are at least partially recycled, a stable emissions price improves overall welfare. This result is robust to different modeling assumptions.