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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.
The effects of energy price increases are heterogeneous between households and firms. Financially constrained poorer households, who spend a larger relative share of their income on energy, are particularly affected. In this analysis, we examine the macroeconomic and welfare effects of energy price shocks in the presence of credit-constrained households that have subsistence-level energy demand. Within a Dynamic Stochastic General Equilibrium (DSGE) model calibrated for the German economy, we compare the performance of different policy measures (transfers and energy subsidies) and different financing schemes (income tax vs. debt). Our results show that credit-constrained households prefer debt over tax financing regardless of the compensation measure due to their difficulty to smooth consumption. On the contrary, rich households tend to prefer tax-financed measures as they increase the labor supply of poor households. From an aggregate perspective, tax-financed measures targeting firms effectively cushion aggregate output losses.
The self-employed faced strong income losses during the Covid-19 pandemic. Many governments introduced programs to financially support the self-employed during the pandemic, including Germany. The German Ministry for Economic Affairs announced a €50bn emergency-aid program in March 2020, offering one-off lump-sum payments of up to €15,000 to those facing substantial revenue declines. By reassuring the self- employed that the government ‘would not let them down’ during the crisis, the program had also the important aim of motivating the self-employed to get through the crisis. We investigate whether the program affected the confidence of the self-employed to survive the crisis using real-time online-survey data comprising more than 20,000 observations. We employ propensity score matching, making use of a rich set of variables that influence the subjective survival probability as main outcome measure. We observe that this program had significant effects, with the subjective survival probability of the self- employed being moderately increased. We reveal important effect heterogeneities with respect to education, industries, and speed of payment. Notably, positive effects only occur among those self-employed whose application was processed quickly. This suggests stress-induced waiting costs due to the uncertainty associated with the administrative processing and the overall pandemic situation. Our findings have policy implications for the design of support programs, while also contributing to the literature on the instruments and effects of entrepreneurship policy interventions in crisis situations.
We use a quantitative spatial equilibrium model to evaluate the distributional and welfare impacts of a recent temporary rent control policy in Berlin, Germany. We calibrate the model to key features of Berlin’s housing market, in particular the recent gentrification of inner city locations. As expected, gentrification benefits rich homeowners, while poor renter households lose. Our counterfactual analysis mimicks the rent control policy. We find that this policy reduces welfare for rich and poor households and in fact, the percentage change in welfare is largest for the poorest households. We also study alternative affordable housing policies such as subsidies and re-zoning policies, which are better suited to address the adverse consequences of gentrification.
We study the effect of energy and transport policies on pollution in two developing country cities. We use a quantitative equilibrium model with choice of housing, energy use, residential location, transport mode, and energy technology. Pollution comes from commuting and residential energy use. The model parameters are calibrated to replicate key variables for two developing country cities, Maputo, Mozambique, and Yogyakarta, Indonesia. In the counterfactual simulations, we study how various transport and energy policies affect equilibrium pollution. Policies may be induce rebound effects from increasing residential energy use or switching to high emission modes or locations. In general, these rebound effects tend to be largest for subsidies to public transport or modern residential energy technology.
Property tax competition
(2022)
We develop a model of property taxation and characterize equilibria under three alternative taxa-tion regimes often used in the public finance literature: decentralized taxation, centralized taxation, and “rent seeking” regimes. We show that decentralized taxation results in inefficiently high tax rates, whereas centralized taxation yields a common optimal tax rate, and tax rates in the rent-seeking regime can be either inefficiently high or low. We quantify the effects of switching from the observed tax system to the three regimes for Japan and Germany. The decentralized or rent-seeking regime best describes the Japanese tax system, whereas the centralized regime does so for Germany. We also quantify the welfare effects of regime changes.
We use panel data from Germany to analyze the effect of population density on urban air pollution (nitrogen oxides, particulate matter and ozone). To address unobserved heterogeneity and omitted variables, we present long difference/fixed effects estimates and instrumental variables estimates, using historical population and soil quality as instruments. Our preferred estimates imply that a one-standard deviation increase in population density increases air pollution by 3-12%.
Urban pollution
(2022)
We use worldwide satellite data to analyse how population size and density affect urban pollution. We find that density significantly increases pollution exposure. Looking only at urban areas, we find that population size affects exposure more than density. Moreover, the effect is driven mostly by population commuting to core cities rather than the core city population itself. We analyse heterogeneity by geography and income levels. By and large, the influence of population on pollution is greatest in Asia and middle-income countries. A counterfactual simulation shows that PM2.5 exposure would fall by up to 36% and NO2 exposure up to 53% if within countries population size were equalized across all cities.
Economists are worried that the lack of property rights to natural capital goods jeopardizes the sustainability of the economic growth miracle that has existed since industrialization. This article questions their position. A vertical innovation model with a portfolio of technologies for abatement, adaptation, and general (Harrod-neutral) technology reveals that environmental damage spillovers have a comparable effect on research profits as technology spillovers so that the social costs of depleting public natural capital are internalized. As long as there is free access to information and technology, growth is sustainable and the allocation of research efforts among alternative technologies is socially optimal. While there still is a need to address externalities from monopolistic research markets, no environmental policy is necessary. These results suggest that environmental externalities may originate in restricted access to information and technology, demonstrating that (i) information has a similar effect as an environmental tax and (ii) knowledge and technology transfers have an impact comparable to that of subsidies for research in green technology.
This paper presents an experiment on the effect of retroactive price-reduction schemes on buyers’ repeated purchase decisions. Such schemes promise buyers a reduced price for all units that are bought in a certain time frame if the total quantity that is purchased passes a given threshold. This study finds a loyalty-enhancing effect of retroactive price-reduction schemes only if the buyers ex-ante expected that entering into the scheme would maximize their monetary gain, but later learn that they should leave the scheme. Furthermore, the effect crucially hinges on the framing of the price reduction.
Strategic uncertainty is the uncertainty that players face with respect to the purposeful behavior of other players in an interactive decision situation. Our paper develops a new method for measuring strategic-uncertainty attitudes and distinguishing them from risk and ambiguity attitudes. We vary the source of uncertainty (whether strategic or not) across conditions in a ceteris paribus manner. We elicit certainty equivalents of participating in two strategic 2x2 games (a stag-hunt and a market-entry game) as well as certainty equivalents of related lotteries that yield the same possible payoffs with exogenously given probabilities (risk) and lotteries with unknown probabilities (ambiguity). We provide a structural model of uncertainty attitudes that allows us to measure a preference for or an aversion against the source of uncertainty, as well as optimism or pessimism regarding the desired outcome. We document systematic attitudes towards strategic uncertainty that vary across contexts. Under strategic complementarity [substitutability], the majority of participants tend to be pessimistic [optimistic] regarding the desired outcome. However, preferences for the source of uncertainty are distributed around zero.