Central Bank Independence, Centralization of Wage Bargaining, Inflation and Unemployment - Theory and Some Evidence

This paper proposes a conceptual framework to investigate the effects of central bank independence, of the degree of centralization of wage bargaining and of the interaction between those institutional variables, on real wages, unemployment and inflation, in a framework in which unions are averse to inflation. This aversion moderates unions wage demands as they attempt to induce the central bank to inflate at a lower rate. An increase in the degree of centralization of wage bargaining (a decrease in the number of unions) triggers two opposite effects on real wages, unemployment and inflation. It reduces the substitutability between the labor of different unions and therefore the degree of effective competition between them. This "reduced competition effect" raises real wages, unemployment and in°ation. But the decrease in the number of unions also strengthens the moderating effect of in°ationary fears on the real wage demands of each union. This "strategic effect" lowers real wages, unemployment and inflation. For sufficiently inflation averse unions the interaction between those two effects produces a Calmfors-Driffill type relation between real wages and centralization. The paper analyzes the effects of central bank independence on the position and the shape of this relation, as well as on inflation and unemployment. The paper features two mechanisms, one of which is novel, through which monetary institutions have real effects. The paper's framework implies that social welfare is maximized when the central bank is ultra liberal. This result is critically assessed. Empirical evaluation of some of the theoretical implications, using data from nineteen developed economies, is for the most part supportive of those implications.central banks;independence;wages;bargaining;inflation;unemployment

Labor markets and Monetary Union: A Strategic Analysis

Monetary union;inflation;unemployment;labor unions;strategic monetary policy

Do Policy Makers’ Distributional Desires Lead to an Inflationary Bias

The political interpretation behind the Barro-Gordon model hinges on two assumptions: Inflation and output growth have distributional effects, and policymakers' distributional desires can be represented by a quadratic loss function in terms of output and inflation. In this article we have examined these two assumptions. Our main results are (1) inflation and output growth have significant effects on the size income distribution, (2) if policymakers are only concerned with the income distribution, no inflationary bias will arise; and (3) the Barro-Gordon model may represent a political model in which political parties care about both the size income distribution and output growth. However, the inflationary bias implied by the Barro-Gordon model should not be associated with the political color of the policymakers

Detecting and explaining unfairness in consumer contracts through memory networks

Recent work has demonstrated how data-driven AI methods can leverage consumer protection by supporting the automated analysis of legal documents. However, a shortcoming of data-driven approaches is poor explainability. We posit that in this domain useful explanations of classifier outcomes can be provided by resorting to legal rationales. We thus consider several configurations of memory-augmented neural networks where rationales are given a special role in the modeling of context knowledge. Our results show that rationales not only contribute to improve the classification accuracy, but are also able to offer meaningful, natural language explanations of otherwise opaque classifier outcomes

A simple planning problem for COVID-19 lockdown: a dynamic programming approach

A large number of recent studies consider a compartmental SIR model to study optimal control policies aimed at containing the diffusion of COVID-19 while minimizing the economic costs of preventive measures. Such problems are non-convex and standard results need not to hold. We use a Dynamic Programming approach and prove some continuity properties of the value function of the associated optimization problem. We study the corresponding Hamilton-Jacobi-Bellman equation and show that the value function solves it in the viscosity sense. Finally, we discuss some optimality conditions. Our paper represents a first contribution towards a complete analysis of non-convex dynamic optimization problems, within a Dynamic Programming approach

Pullout tests on the connection to an existing foundation of a steel warehouse rebuilt after the 2012 Emilia (Italy) earthquakes

The tests described in this paper were aimed at evaluating the tensile capacity of the anchorages connecting an automated pallet warehouse with an existing RC foundation. The warehouse is a new steel structure erected in the place of a previous warehouse collapsed due to the Emilia earthquake, but whose foundation remained undamaged. The investigated fastening consists of 10 post-installed, bonded threaded rods with diameter (d) and embedment depth (h(ef)) of 20 and 500 mm, respectively. Neither anchor arrangement nor embedment depth (h(ef) > 20d) was covered by current standards for fastening design. To reproduce the in-situ actual conditions of the fastening, an unconfined test configuration was used. The maximum loads achieved were more than 3 times greater than the seismic demand for the fastening. The tests highlighted the crucial role played by the reinforcing steel which was present in the foundation. Concrete-related failure mechanisms, such as the combined pullout and concrete cone failure mechanism typical of bonded anchors, were not activated. The observed crack patterns rather suggest the onset of a flexural failure mechanism of the concrete slab. This feature is confirmed by analytical calculations showing that, at the maximum loads achieved in the tests, the top reinforcement was likely to be yielded. In six preliminary unconfined tension tests on single anchors, steel rod failure was achieved, associated with limited cracking of the concrete surface in proximity of the anchor

Identification of gait phases with neural networks for smooth transparent control of a lower limb exoskeleton

Lower limbs exoskeletons provide assistance during standing, squatting, and walking. Gait dynamics, in particular, implies a change in the configuration of the device in terms of contact points, actuation, and system dynamics in general. In order to provide a comfortable experience and maximize performance, the exoskeleton should be controlled smoothly and in a transparent way, which means respectively, minimizing the interaction forces with the user and jerky behavior due to transitions between different configurations. A previous study showed that a smooth control of the exoskeleton can be achieved using a gait phase segmentation based on joint kinematics. Such a segmentation system can be implemented as linear regression and should be personalized for the user after a calibration procedure. In this work, a nonlinear segmentation function based on neural networks is implemented and compared with linear regression. An on-line implementation is then proposed and tested with a subject