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The standard method to estimate a stochastic frontier (SF) model is the maximum likelihood (ML) approach with the distribution assumptions of a symmetric two-sided stochastic error v and a one-sided inefficiency random component u. When v or u has a nonstandard distribution, such as v follows a generalized t distribution or u has a χ2 distribution, the likelihood function can be complicated or untractable. This chapter introduces using indirect inference to estimate the SF models, where only least squares estimation is used. There is no need to derive the density or likelihood function, thus it is easier to handle a model with complicated distributions in practice. The author examines the finite sample performance of the proposed estimator and also compare it with the standard ML estimator as well as the maximum simulated likelihood (MSL) estimator using Monte Carlo simulations. The author found that the indirect inference estimator performs quite well in finite samples.

The extra-low minimum wage for US restaurant workers has remained unchanged for over 30 years. Periodic campaigns have brought this wage, and its connection to the perpetuation of inequality and exploitative work, to public attention, but these campaigns have met resistance from both employers and restaurant workers. This article draws on a workplace ethnography in a restaurant front-of-house, and in-depth interviews with tipped food service workers, to examine the tipped labour process and begin to answer a central question: why would any workers oppose a wage increase? It argues that the constituting of tips as a formal wage created for workers a two-employer problem, wherein customers assume the role of secondary, unregulated, employers in the workplace. Ultimately, the tipped wage poses a longer-term strategic obstacle for workers in their position relative to management and ability to organize to shape the terms and conditions of their work.

After summarizing what has been learned so far, the purpose of this review is to suggest several promising avenues for future research on work-to-family enrichment (WFE) and family-to-work enrichment (FWE).

This is a literature review. After reviewing the existing research and searching for gaps in the literature, new areas of research will be proposed to fill these gaps.

While much has been learned about the antecedents and consequences of work–family enrichment in both directions, WFE and FWE, much remains to be learned.

Three important outcomes – job performance, organizational citizenship behavior, and counterproductive work behavior – need to be studied regarding WFE and FWE. Although supervisor support has been studied, the field needs to incorporate leadership theories and models to understand this phenomenon. Additional predictors of work outcomes – including emotional intelligence, leadership, emotional labor, social support, gender, and cross-cultural variables – need to be examined. Experience sampling methods and advanced research methodologies should also be used.

Although prior research has demonstrated the important effects of WFE and FWE, the practical effects on organizations in terms of job performance still need to be investigated.

The literature review conclusively demonstrates that WFE and FWE are both related to job satisfaction and family satisfaction.

This is the first review to summarize the existing meta-analytical research in this area and to propose the particular avenues of research advocated in this article.

This chapter will identify readily accessible existing sources of public data. Thechallenges of using that data are considerable and require extensive time to ensure validity for reporting purposes. Summaries of data field selection and data wrangling requirements are presented in conjunction with data aggregation strategies.