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The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

Work-life flexibility policies (e.g., flextime, telework, part-time, right-to-disconnect, and leaves) are increasingly important to employers as productivity and well-being strategies. However, policies have not lived up to their potential. In this chapter, the authors argue for increased research attention to implementation and work-life intersectionality considerations influencing effectiveness. Drawing on a typology that conceptualizes flexibility policies as offering employees control across five dimensions of the work role boundary (temporal, spatial, size, permeability, and continuity), the authors develop a model identifying the multilevel moderators and mechanisms of boundary control shaping relationships between using flexibility and work and home performance. Next, the authors review this model with an intersectional lens. The authors direct scholars’ attention to growing workforce diversity and increased variation in flexibility policy experiences, particularly for individuals with higher work-life intersectionality, which is defined as having multiple intersecting identities (e.g., gender, caregiving, and race), that are stigmatized, and link to having less access to and/or benefits from societal resources to support managing the work-life interface in a social context. Such an intersectional focus would address the important need to shift work-life and flexibility research from variable to person-centered approaches. The authors identify six research considerations on work-life intersectionality in order to illuminate how traditionally assumed work-life relationships need to be revisited to address growing variation in: access, needs, and preferences for work-life flexibility; work and nonwork experiences; and benefits from using flexibility policies. The authors hope that this chapter will spur a conversation on how the work-life interface and flexibility policy processes and outcomes may increasingly differ for individuals with higher work-life intersectionality compared to those with lower work-life intersectionality in the context of organizational and social systems that may perpetuate growing work-life and job inequality.

We examine how authorities' use of fair decision-making procedures and power benevolently shape workers' impressions of them as competent and warm, which serve as a mechanism whereby authorities' behaviors shape workers' emotional responses. We investigate how the role of these impressions differs depending on authority gender and consider whether emotional responses differ for male and female subordinates.

We conducted a between-subjects experimental vignette study in which we manipulate an authority's behaviors and gender. We use multigroup mediation analysis to test our predictions.

Authorities who employ procedural justice and benevolent power elicit reports of heightened positive emotion experiences and intended displays and reports of reduced negative emotion experiences and intended displays. These behaviors also enhance views of authorities as competent and warm. The mediating role of impressions differs by authority gender. Authority behaviors prompt reports of positive emotions through conveying impressions that align with authorities' gender stereotypes (competence for men, warmth for women). In contrast, warmth impressions mediate effects of behaviors on reported negative emotions when authorities are men, whereas when authorities are women, benevolent power use directly reduces reported negative experience, and procedural justice reduces negative display. Female respondents are more likely to report positive emotion experience and display toward male authorities and negative display toward female authorities.

By examining competence and warmth impressions as mechanisms, we gain insight into how the process by which authority behaviors affect worker emotions is gendered and shed light on micro-level dynamics contributing to gender inequality at work.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

To operate with high efficiency and minimise the risks of power failures, power systems require careful monitoring. The availability of real-time data is crucial for assessing the performance of the grid and assisting operators in gauging the present security of the grid. Traditional supervisory control and data acquisition (SCADA)-based systems actually employed provides steady-state measurement values which are the calculation premise of State Estimation. More often, however, the power grid operates under dynamic state and SCADA measurements can lead to erroneous and inaccurate calculation results. The introduction of the phasor measurement unit (PMU) which provides real-time synchronised voltage and current phasors with very high accuracy is universally recognised as an important aspect of delivering a secure and sustainable power system. PMUs are a relatively new technology and because of their high procurement and installation costs, it is imperative to develop appropriate methodologies to determine the minimum number of PMUs as well as their strategic placements to guarantee full observability of a power system. Thus, the problem of the optimal PMU placement (OPP) is formulated as an optimisation problem subject to various constraints to minimise the number of PMUs while ensuring complete observability of the grid. In this chapter, integer linear programming (ILP), genetic algorithm (GA) and non-linear programming (NLP) constrained models of the OPP problem are presented. A new methodology is proposed to incorporate several constraints using the NLP. The optimisation methods have been written in Matlab software and verified on the standard Institute of Electrical and Electronics Engineers (IEEE) 14-bus test system to authenticate their effectiveness. This chapter targets United Nations Sustainable Development Goal 7.