Career and job fairs are frequently used instruments to improve the matching between employees and employers as well as between employees and professions. Despite their importance, however, their impact has been under-researched. Using an innovative dataset that measures searches on the largest platform for apprenticeship vacancies in real-time and the necessity that some fairs had to switch from in-person to virtual during the COVID-19 phase unexpectedly, we can show that virtual fairs not only had a stronger immediate positive impact on the number of searches for apprenticeship vacancies but also expanded the search radius for different professions.

In this paper, we show the causal influence of the launch of generative AI in the form of ChatGPT on the search behavior of young people for apprenticeship vacancies. There is a strong and long-lasting decline in the intensity of searches for vacancies, which suggests great uncertainty among the affected cohort. Analyses based on the classification of occupations according to tasks, type of cognitive requirements, and the expected risk of automation to date show significant differences in the extent to which specific occupations are affected. Occupations with a high proportion of cognitive tasks, with high demands on language skills, and those whose automation risk had previously been assessed by experts as lower are significantly more affected by the decline. However, no differences can be found with regard to the proportion of routine vs. non-routine tasks.

Active labor market programs are important instruments used by European employment agencies to help the unemployed find work. Investigating large administrative data on German long-term unemployed persons, we analyze the effectiveness of three job search assistance and training programs using Causal Machine Learning. Participants benefit from quickly realizing and long-lasting positive effects across all programs, with placement services being the most effective. For women, we find differential effects in various characteristics. Especially, women benefit from better local labor market conditions. We propose more effective data-driven rules for allocating the unemployed to the respective labor market programs that could be employed by decision-makers.

Tournaments are frequently used incentive mechanisms to enhance performance. In this paper, we use field data and show that skill disparities among contestants asymmetrically affect the performance of contestants. Skill disparities have detrimental effects on the performance of the lower-ability contestant but positive effects on the performance of the higher-ability contestant. We discuss the potential of different behavioral approaches to explain our findings and discuss the implications of our results for the optimal design of contests. Beyond that, our study reveals two important empirical results: (a) affirmative action-type policies may help to mitigate the adverse effects on lower-ability contestants, and (b) the skill level of potential future contestants in subsequent tournament stages can detrimentally influence the performance of higher-ability contestants but does not affect the lower-ability contestant.

We analyze the causal influence a positive reputation shock for a particular occupation may have on career choice. The measure of the positive reputation shock is the unpredictable event that a young adult from one's own country wins a (gold) medal in a particular occupation at the World Skills—the world championship of vocational skills. In an occupation with a gold medal won, searches for apprenticeship vacancies increase significantly by around 7 percent compared to occupations that do not win a competition. In occupations where only a silver or bronze medal is awarded, the effect is also positive and statistically significant, but less pronounced. More importantly, the increase in searches for apprenticeship vacancies in the current year has also led to around 2.5 percent more contracts being signed in the winning occupation, and there are indications that these apprenticeships have a better match between employers and employees (trainees).

This article investigates the challenges that economics students face when they make the transition from service mathematics course (s) to microeconomics courses with a focus on how the concept of the Lagrange multiplier method is used in constrained utility maximization problems. The study aims to identify and understand discrepancies in the application of the Lagrange multiplier method as introduced in the mathematics course and subsequently applied within the microeconomics course. For this purpose, we conducted a comparative praxeological analysis of textbooks used in the two courses. The analysis reveals significant differences in the techniques and technologies used in both textbooks, which may cause difficulties for students in their transition between the courses. Additionally, the analysis revealed total mismatches in praxeologies within the textbooks. The article concludes with suggestions on topics for service mathematics teachers to address for aligning their instruction with the microeconomics application of the Lagrange multiplier method.

We analyze the causal impact of positive and negative feedback on professional performance. We exploit a unique data source in which quasi-random, naturally occurring variations within subjective ratings serve as positive and negative feedback. The analysis shows that receiving positive feedback has a favorable impact on subsequent performance, while negative feedback does not have an effect. These main results are found in two different environments and for distinct cultural backgrounds, experiences, and gender of the feedback recipients. The findings imply that managers should focus on giving positive motivational feedback.

We propose a statistical framework for quantifying the importance of single events that do not provide intermediate rewards but offer implicit incentives through scheduling and the reward structure at the end of a multi-event contest. Applying the framework to primary elections in the US, where earlier elections have greater importance and influence, we show that schedule variations can mitigate the problem of front-loading elections. When applied to European football, we demonstrate the utility and meaningfulness of quantified event importance in relation to the in-match performance of contestants to improve outcome prediction and to provide an early indication of public interest.

We investigate the impact of the presence of university dropouts on the academic success of first-time students. Our identification strategy relies on quasi-random variation in the proportion of returning dropouts. The estimated average zero effect of dropouts on first-time students’ success masks treatment heterogeneity and non-linearities. First, we find negative effects on the academic success of their new peers from dropouts re-enrolling in the same subject and, conversely, positive effects of dropouts changing subjects. Second, using causal machine learning methods, we find that the effects vary nonlinearly with different treatment intensities and prevailing treatment levels.

We analyse a sequential contest with two players in darts where one of the contestants enjoys a technical advantage. Using methods from the causal machine learning literature, we analyse the built-in advantage, which is the first-mover having potentially more but never less moves. Our empirical findings suggest that the first-mover has an 8.6% points higher probability to win the match induced by the technical advantage. Contestants with low performance measures and little experience have the highest built-in advantage. With regard to the fairness principle that contestants with equal abilities should have equal winning probabilities, this contest is ex-ante fair in the case of equal built-in advantages for both competitors and a randomized starting right. Nevertheless, the contest design produces unequal probabilities of winning for equally skilled contestants because of asymmetries in the built-in advantage associated with social pressure for contestants competing at home and away.

A substantial share of customers in emerging markets use dual-SIM phones and subscribe to two mobile networks. A primary motive for so called multi-simming is to take advantage of cheap on-net services from both networks. In our modelling effort, we augment the seminal model of competing telephone networks a la Laffont, Rey and Tirole (1998b) by a segment of flexible price hunters that may choose to multi-sim. According to our findings, in equilibrium, the networks set a high off-net price in the linear tariffs to achieve segmentation. This induces the price hunters to multi-sim. We show that increased deployment of dual-SIM phones may induce a mixing equilibrium with high expected on-net prices. Thus, somewhat paradoxically, deployment of a technology that increases substitutability, and thereby competition, may end up raising prices.

A loss-averse buyer and seller face an uncertain environment. Should they write a long-term contract or wait until the state of the world is realized? I show that simple long-term contracts perform better than insinuated in Herweg and Schmidt (2015), even though loss aversion makes renegotiation sometimes inefficient. During renegotiation, the outcome induced by the long-term contract constitutes the reference point to which the parties compare gains and losses induced by the renegotiated transaction. Whereas Herweg and Schmidt consider that the long-term contract is always performed, it should not in "bad'' states. This alters the threat point in renegotiation, making it easier to renegotiate and thus improves the performance of long-term contracts. Specific performance contracts perform better than in Herweg and Schmidt but are still problematic. Option contracts perform much better since only one party has the ex-post trade decision making it much easier to prevent the contract is inefficiently enforced due to loss aversion. My findings suggest that loss aversion alone cannot explain why parties sometimes abstain from writing beneficial long-term contracts but give important insights on how long-term contracts should be written when parties are aware they are loss averse.

Predicting the outcome of football (i.e. soccer) games based on past information is a non-standard predictive task because of the nature of the game outcome, as well as because of the importance of uncertainty (luck and unobservables). The game outcome consists of the scores of the two teams that are usually either collapsed into a goal-difference or further aggregated to reflect whether the game ended as a win for the home or away team, or as a draw. From a statistical perspective, such outcomes have bounded support and, thus, standard linear modelling can be expected to perform poorly. The large amount of uncertainty in the game outcomes due to just luck or due to game- or team-specific unobservables (e.g. hidden injuries of players, etc.) makes it imperative to use prediction methods that fully exploit the potential of the available information, as well as to uncover the uncertainty of a match outcome. The latter is also relevant when interest is not only in single games but also in a league table at the end of the season. Obviously, such league tables should capture the uncertainty for the single games accumulated over a season to be useful guides on what to expect. Recently, machine learning methods have shown their power in all sorts of prediction problems, in particular in situations where the relation of the variables capturing the information used to predict with the target of the prediction, i.e. here the outcome of the game, is non-linear. However, so far there has been only little development in gearing these methods explicitly towards the estimation of the probabilities of ordered outcomes, such as score differences and points, or just wins, draws, and losses. Lechner and Okasa (2019) propose adapting classical random forest estimation, which is known to have excellent predictive performance (e.g. Biau and Scornet (2016), FernAindez-Delgado et al. (2014)) to the problem of predicting probabilities of ordered categorical outcomes, such as the win-draw-loss problem of a football game. In this chapter, we use their approach to predict game outcomes of the German Bundesliga 1 (BL1) based on more than ten years' data on game outcomes as well as extensive information about teams, their players, and their environment. These predictions are then used to obtain the final season rankings in a way that reflects and shows the magnitude of the inherent uncertainty of football games.

Matching-type estimators using the propensity score are the major workhorse in active labour market policy evaluation. This work investigates if machine learning algorithms for estimating the propensity score lead to more credible estimation of average treatment effects on the treated using a radius matching framework. Considering two popular methods, the results are ambiguous: We find that using LASSO based logit models to estimate the propensity score delivers more credible results than conventional methods in small and medium sized high dimensional datasets. However, the usage of Random Forests to estimate the propensity score may lead to a deterioration of the performance in situations with a low treatment share. The application reveals a positive effect of the training programme on days in employment for long-term unemployed. While the choice of the “first stage” is highly relevant for settings with low number of observations and few treated, machine learning and conventional estimation becomes more similar in larger samples and higher treatment shares.

I consider a bilateral trade setting in which the agents exert unverifiable investments before playing a revelation mechanism and subsequently advancing to the trading stage. Watson (2007) has demonstrated that the set of implementable outcomes (i) is largest when renegotiation can be ruled out, (ii) decreases when renegotiation takes place before the mechanism is played, and (iii) is the smallest when renegotiation takes place after playing the mechanism. I show that the agents can often attain the first best in case (ii), implying that the smaller size of the set may hold little importance from an efficiency perspective.

We analyze a complete information multilateral bargaining model in which a buyer is to purchase two complementary goods from two sellers. Binding cash-offer contracts are used to govern transactions. In contrast to preexisting literature, we do not normalize the parties' reservation utilities to zero. We show that this assumption holds critical importance by demonstrating that a complete breakdown of negotiations may occur as the unique equilibrium outcome, even if only two sellers are present.

I consider a setting of complete but unverifiable information in which two agents enter a contractual relationship to induce mutually beneficial investments. As my main result, I establish that the famous irrelevance of contracting paradigm, that arises due to the detrimental effect of renegotiation, is resolved if there is a fixed point in time when actions have to be chosen and one accounts for the fact that renegotiation takes time. What drives my optimality result is that, by stipulating when the mechanism is to be played, the agents ensure that renegotiation is possible ex ante but not ex post.

Our analysis focuses on a situation where a landowner and the government invest prior to the government's taking decision. When the government suffers from budgetary "fiscal illusion," optimal compensation amounts to the hypothetical value of the landowner's property had she invested efficiently. In contrast, under a government that maximizes social welfare, the only regime to induce the first best grants as compensation the social benefit of the taking. Consequently, if the government can only raise capital up to a certain amount, society may be better off under a non-benevolent government.

Institutions for collective decision making often defer to the status quo, granting it a privileged position relative to proposed policy innovations. The possible benefits of status quo deference must be weighed against a cost: status quo deference can prevent a society from learning the merits of innovations. This paper explores the potential for learning through adaptive diversification of treatment choice in decision systems that feature status quo deference. I first review the basic elements of my earlier analysis of adaptive diversification by a planner and then extend the analysis to two collective decision processes, voting and bilateral negotiation.

We examine the efficiency of the standard breach remedy expectation damages in a setting of bilateral cooperative investment by a buyer and a seller. Contracts may specify a required quality level and an upper bound to the cost of production. We find that it is optimal to write an augmented Cadillac contract that sets one threshold such that it cannot be met with positive probability together with an extreme price. Then, one of the parties becomes a residual claimant of the trade relationship. The other threshold can be used to balance the incentives of the other party.