Bibliographic record
Abstract
papers beneted from seminar audiences in Munich, Konstanz, the 2009 Annual Confer-stay together when their match quality has become relatively bad and they would rather prefer to break up (absent divorce costs).If the gains from an increased commitment are lower than this welfare loss, a couple might abstain from getting married ex ante and rather choose to cohabit.The second chapter, Minimum Wages and Relational Contracts, develops a tractable model that shows that if agents must be motived to exert eort, various empirically observed consequences of a minimum wage can be explained.Furthermore, if relational contracts, i.e., contracts based on observable but non-veriable measures, are used and agents can be replaced, an appropriate minimum wage increases the total surplus created within an employment relationship.The driving factor behind these results is a rm's optimal choice of incentives.If rms are forced to pay a higher wage than actually intended, they will also require their employees to work harder.More precisely, a labor market with many homogenous rms and employees exists, with more employees than rms.The market is frictionless, and no (exogenous) turnover costs exist, why it is always possible for a rm to costlessly replace an agent.Furthermore, the market is not fully transparent in a sense that if turnover occurs, it is not possible to detect the reason, i.e., if an agent is red or leaves voluntarily.Thus, a rm cannot build up an external or market reputation for honoring its promises.This creates a commitment problem: Instead of making promised payments as a reward for previous eort, rms might have an incentive to renounce and replace employees.Therefore, the only way to induce agents to work is the existence of endogenous turnover costs.However, rms are also exposed to these turnover costs whenever their employees leave for exogenous reasons.Although they have all bargaining power, rms are thus not able to capture the whole surplus of an employment relationship.Then, they face a tradeo between giving high incentives (induced by high wages) and reducing turnover costs (which also increase with equilibrium wages).Even if maximum incentives are possible, employers voluntarily decrease them and enforce an eort level which is ineciently low.Forcing rms to pay a minimum wage will make it optimal for rms to let agents work harder, inducing a surplus increase.To capture employment eects as well, the model is extended accordingly.In one specication it is assumed that prots are positive.Furthermore, a rm can employ many agents.Then, employment is chosen eciently for a given level of equilibrium eort.However, since rms voluntarily decrease incentives to reduce endogenous turnover costs, Preface 4 eort and consequently also employment will be ineciently low.By increasing eort, a minimum wage thus also induces a rm to employ more agents than before.The third chapter, On the Genesis of Multinational Networks (joint with Peter Egger, Valeria Merlo, and Georg Wamser) deals with information problems.Specically, this part explores how multinational enterprises (MNEs) develop their network of foreign aliates.It is commonly observed that MNEs tend to pursue a gradual expansion strategy of their network of foreign aliates over time rather than exploring all protable opportunities simultaneously.They typically establish themselves in their home countries and then enter new foreign markets step by step.We propose a model where MNEs face uncertainty concerning their success in new markets and learn about that after entry.Conditions in dierent markets are not independent, and the information gathered in one country can also be used to learn about conditions in other, in particular, similar countries.This so-called correlated learning can explain why rms expand step by step: market entry is associated with considerable costs, and sequential investments help to economize on these costs by reducing uncertainty.The learning model developed in this paper serves to derive a number of testable hypotheses regarding market entry in general and simultaneous versus sequential market entry in specic.These hypotheses are assessed in a data-set of the universe of German MNEs and their foreign aliates.The results provide empirical evidence for correlated learning as a main driver behind international expansion strategies.1965) to 1.53 (2005-2010).During the same time period, the decrease in the US was from 3.31 to 2.07 and in Canada from 3.68 to 1.65.Source: United Nations Department of Economic and Social Aairs (2011).3 For example the birth control pill, an easier access to abortion, or the decline in infant mortality (see Doepke, 2005).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".