Central bank credibility and monetary policy in Indonesia

This paper examines the role of central bank credibility in achieving an inflation target and proposes monetary policy rules for Indonesia. Towards that end, we construct and estimate a forward-looking small scale macroeconomic model (SSMM) of the Indonesian economy by adapting the theoretical underpinnings of the well-known Batini–Haldane model, along with the Taylor policy rule. Our results indicate that it is crucial for the Indonesian central bank to bolster its credibility in order to achieve a lower inflation rate. The inflation–output volatility trade-off frontier we derived from the SSMM shows that a monetary policy rule that targets both inflation and output gaps will result in less macroeconomic volatility. We also found that the inclusion of the exchange rate into the rule as an additional feedback variable warrants consideration in the future course of monetary policy management.     

A Small-Sample Estimator for the Sample-Selection Model

A semiparametric estimator for evaluating the parameters of data generated under a sample selection process is developed. This estimator is based on the generalized maximum entropy estimator and performs well for small and ill-posed samples. Theoretical and sampling comparisons with parametric and semiparametric estimators are given. This method and standard ones are applied to three small-sample empirical applications of the wage-participation model for female teenage heads of households, immigrants, and Native Americans.     

An Optimization‐Based Framework for the Identification of Vulnerabilities in Electric Power Grids Exposed to Natural Hazards

This article proposes a novel mathematical optimization framework for the identification of the vulnerabilities of electric power infrastructure systems (which is a paramount example of critical infrastructure) due to natural hazards. In this framework, the potential impacts of a specific natural hazard on an infrastructure are first evaluated in terms of failure and recovery probabilities of system components. Then, these are fed into a bi‐level attacker–defender interdiction model to determine the critical components whose failures lead to the largest system functionality loss. The proposed framework bridges the gap between the difficulties of accurately predicting the hazard information in classical probability‐based analyses and the over conservatism of the pure attacker–defender interdiction models. Mathematically, the proposed model configures a bi‐level max‐min mixed integer linear programming (MILP) that is challenging to solve. For its solution, the problem is casted into an equivalent one‐level MILP that can be solved by efficient global solvers. The approach is applied to a case study concerning the vulnerability identification of the georeferenced RTS24 test system under simulated wind storms. The numerical results demonstrate the effectiveness of the proposed framework for identifying critical locations under multiple hazard events and, thus, for providing a useful tool to help decisionmakers in making more‐informed prehazard preparation decisions.     

Media Exposure to Terrorism and Perception of Immigrants as a Threat: The Role of Emotional Intelligence and Psychophysiological Self-Regulation

To better understand how media exposure to terrorism-related images can lead to perceiving immigrants as more threatening, in the present study we manipulated participants’ exposure to media coverage of terrorist attacks and investigated how this may influence people's perception of Arab immigrants. Considering the important role of regulatory abilities when facing stressful events like terrorist attacks, we measured individual differences in both trait emotional intelligence and resting heart rate variability (HRV). Results showed that participants perceived Arab immigrants as more threatening in the media exposure condition than in the control condition. Importantly, there were moderating effects of both trait emotional intelligence and HRV. People with lower trait emotional intelligence or lower HRV at rest felt more threatened in the media exposure condition compared to the control condition, whereas this effect was not observed among participants with higher trait emotional intelligence or higher resting HRV. The present study highlights some of the complexities related to how media exposure to terrorism-related images influences people's reactions to, and evaluations of, an outgroup that is related to the perpetrators of the attacks.     

When the main job tasks are perceived to be ‘irrelevant’ in the workplace: the internal uselessness of corporate social responsibility work in Japan

ABSTRACT

Despite a symbolic shift toward ‘emotional taint’ in dirty work literature, the role of the workplace has not been studied in relation to socially admired professions, such as corporate social responsibility (CSR) work. This article carries out an in-depth, critical examination of CSR as an emotionally tainted occupation in Japan. Its findings, substantiated by an analysis of 34 CSR workers’ rhetoric, help conceptualise ‘internal uselessness’. This emerges when workers feel their organisations publicly foster an image of their work that is decoupled from its internal reality, assigning them chief tasks they consider irrelevant. This leads to negative consequences that damage their workplace social relationships, professional aspirations and emotional well-being. The findings ultimately show that these CSR workers in Japan attempt to counterbalance internal uselessness through a social quest with peers outside their workplace, but also manage their emotions by rationalising their job status as inescapable, influenced by situated commitment norms.     

Optimization of Cascade‐Resilient Electrical Infrastructures and its Validation by Power Flow Modeling

Large‐scale outages on real‐world critical infrastructures, although infrequent, are increasingly disastrous to our society. In this article, we are primarily concerned with power transmission networks and we consider the problem of allocation of generation to distributors by rewiring links under the objectives of maximizing network resilience to cascading failure and minimizing investment costs. The combinatorial multiobjective optimization is carried out by a nondominated sorting binary differential evolution (NSBDE) algorithm. For each generators–distributors connection pattern considered in the NSBDE search, a computationally cheap, topological model of failure cascading in a complex network (named the Motter‐Lai [ML] model) is used to simulate and quantify network resilience to cascading failures initiated by targeted attacks. The results on the 400 kV French power transmission network case study show that the proposed method allows us to identify optimal patterns of generators–distributors connection that improve cascading resilience at an acceptable cost. To verify the realistic character of the results obtained by the NSBDE with the embedded ML topological model, a more realistic but also more computationally expensive model of cascading failures is adopted, based on optimal power flow (namely, the ORNL‐Pserc‐Alaska) model). The consistent results between the two models provide impetus for the use of topological, complex network theory models for analysis and optimization of large infrastructures against cascading failure with the advantages of simplicity, scalability, and low computational cost.     

Assessing the Performance of a Classification‐Based Vulnerability Analysis Model

In this article, a classification model based on the majority rule sorting (MR‐Sort) method is employed to evaluate the vulnerability of safety‐critical systems with respect to malevolent intentional acts. The model is built on the basis of a (limited‐size) set of data representing (a priori known) vulnerability classification examples. The empirical construction of the classification model introduces a source of uncertainty into the vulnerability analysis process: a quantitative assessment of the performance of the classification model (in terms of accuracy and confidence in the assignments) is thus in order. Three different app oaches are here considered to this aim: (i) a model–retrieval‐based approach, (ii) the bootstrap method, and (iii) the leave‐one‐out cross‐validation technique. The analyses are presented with reference to an exemplificative case study involving the vulnerability assessment of nuclear power plants.     

A characterization of the dilation order and its applications

LetX andY be two random variables with finite expectationsE X andE Y, respectively. ThenX is said to be smaller thanY in the dilation order ifE[ϕ(X-E X)]≤E[ϕ(Y-E Y)] for any convex functionϕ for which the expectations exist. In this paper we obtain a new characterization of the dilation order. This characterization enables us to give new interpretations to the dilation order, and using them we identify conditions which imply the dilation order. A sample of applications of the new characterization is given. Partially supported by MURST 40% Program on Non-Linear Systems and Applications. Partially supported by “Gruppo Nazionale per l'Analisi Funzionale e sue Applicazioni”—CNR.     

The Structure of the Chinese Banking System and Credit Access Problems for SMEs

This paper examines the structure and the main characteristics of the Chinese banking industry. In particular, the paper analyses the problematic aspects of credit relations of small and medium enterprises (SMEs). The SMEs are facing several problems in accessing the bank loans market, dominated by the political and contractual weight of state-owned enterprises. However, some recent reforms have improved the situation, a fundamental condition for additional positive results. Nevertheless, further and more pervasive modernization efforts are needed to consolidate these results and to develop the relationships between banks and SMEs.