Künstliche Intelligenz und der Faktor Arbeit

Artificial Intelligence (AI) and its potential impact on the demand for human labour has become a fashionable topic — particularly among economic policy advisors. However, most papers that warn about high substitution rates of conventional jobs use questionable data and methodologies. Future economic policy advice ought to include a company perspective — and discover that disruption will be much more gradual and less dramatic than proclaimed by the current topical papers. As this AI revolution is just at its very beginning, economic policy makers are advised to accompany and support digital change instead of actively intervening in digital value chains with taxes and/or subsidies.     

Wohnimmobilienpreise,Inflationsmessung und Geldpolitik im Euroraum

Wirtschaftsdienst - Die Niedrigzinspolitik der EZB hat in vielen Ländern des Euroraums zu einem starken Anstieg der Wohnimmobilienpreise beigetragen. In einigen Mitgliedstaaten mündeten...     

The investment behavior of buyout funds: Theory and evidence

We analyze the determinants of buyout funds’ investment decisions. We argue that when there is imperfect competition for private equity funds, the timing of funds’ investment decisions, their risk-taking behavior, and their subsequent returns depend on changes in the demand for private equity, conditions in the credit market, and fund managers’ ability to influence perceptions of their talent. We investigate these hypotheses using a proprietary dataset of 207 U.S. buyout funds that invested in 1,957 buyout targets over a 30-year period. Our dataset contains precisely dated cash inflows and outflows in every portfolio company, links every buyout target to an identifiable buyout fund, and is free from reporting and survivor biases. Thus, we are able to characterize every buyout fund's precise investment choices. Our findings are as follows. First, established funds accelerate their investment flows and earn higher returns when investment opportunities improve, competition for deal flow eases, and credit market conditions loosen. Second, the investment behavior of first-time funds is less sensitive to market conditions. Third, younger funds invest in riskier buyouts, in an effort to establish a track record. Finally, following periods of good performance, funds become more conservative, and this effect is stronger for first-time funds.     

Choosing the agent's group identity in a trust game with delegated decision making

Members of a given social group often favor members of their own group identity over people with different group identities. We construct a trust game in which the principal delegates the decision about an investment into a receiver to an agent who either favors the principal's or the receiver's group identity. When choosing the agent's group identity the principal faces a trade‐off between a loyal agent and an agent who might increase the receiver's willingness to cooperate. We solve for the principal's decision in a subgame‐perfect nash equilibrium for the two scenarios of a risk‐neutral and risk‐averse agent, respectively.     

Robust stochastic frontier analysis: a Student’s t-half normal model with application to highway maintenance costs in England

The presence of outliers in the data has implications for stochastic frontier analysis, and indeed any performance analysis methodology, because they may lead to imprecise parameter estimates and, crucially, lead to an exaggerated spread of efficiency predictions. In this paper we replace the normal distribution for the noise term in the standard stochastic frontier model with a Student’s t distribution, which generalises the normal distribution by adding a shape parameter governing the degree of kurtosis. This has the advantages of introducing flexibility in the heaviness of the tails, which can be determined by the data, as well as containing the normal distribution as a limiting case, and we outline how to test against the standard model. Monte Carlo simulation results for the maximum simulated likelihood estimator confirm that the model recovers appropriate frontier and distributional parameter estimates under various values of the true shape parameter. The simulation results also indicate the influence of a phenomenon we term ‘wrong kurtosis’ in the case of small samples, which is analogous to the issue of ‘wrong skewness’ previously identified in the literature. We apply a Student’s t-half normal cost frontier to data for highways authorities in England, and this formulation is found to be preferred by statistical testing to the comparator normal-half normal cost frontier model. The model yields a significantly narrower range of efficiency predictions, which are non-monotonic at the tails of the residual distribution.

     

An Honorable Harvest: Universal Owners Must Take Responsibility for Their Portfolios

Despite a maturing industry of ESG professionals and coordinated efforts by shareholders calling for more responsible corporate behavior, we continue to see unabated climate and water crises, growing political instability, and continuing abuses of human rights in supply chains. The founder of a movement called The Shareholder Commons argues that to help business to address these systemic challenges, corporate responsibility must move beyond the company‐by‐company decision‐making model. An economy based on market competition cannot rely on individual businesses to adopt basic sustainability rules that take priority over profit. Critical sustainability boundaries must be implemented collectively to be effective. The crux of the problem is that although shareholder returns derive mainly from efficiency and productivity gains, they can also result from careless exploitation of common resources or powerless workers. And the competition for margin and capital makes it difficult for companies to recognize, let alone forgo, profitable exploitation. A sustainable economy demands that we help companies to distinguish between honorable and dishonorable profits, and to find ways to eliminate or offset the latter. The author holds out a model of capitalism that limits the availability of choices that exploit negative externalities and inequality while preserving the principles and practices that create value and a reasonable sharing of gains among all stakeholders. Universal owners—long‐term diversified investors—appear to be in the best position to formulate and enforce such a model, given the current design and practices of our capital markets. Such global investors have the incentive and power to engage in the collective decision‐making necessary for a sustainable economy. The power exerted by institutional investors through their allocation and stewardship of equity capital can be used to insist on more sustainable business practices. Because they are diversified across thousands of companies, universal owners can bypass the competitive bottleneck for margin and capital that holds sustainability back at the company level. These large investors can work together to establish authentic sustainability boundaries for the companies they invest in; and by so doing, they can allow us to leverage all the good work done to date on disclosure and ESG integration, and so realize a world in which companies continue to compete for profits, but also for a truly honorable harvest.     

Exchange rate predictability and dynamic Bayesian learning

We consider how an investor in the foreign exchange market can exploit predictive information by means of flexible Bayesian inference. Using a variety of vector autoregressive models, the investor is able, each period, to learn about important data features. The developed methodology synthesizes a wide array of established approaches for modeling exchange rate dynamics. In a thorough investigation of monthly exchange rate predictability for 10 countries, we find that using the proposed methodology for dynamic asset allocation achieves substantial economic gains out of sample. In particular, we find evidence for sparsity, fast model switching, and exploitation of the exchange rate cross-section.