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This chapter revisits the Hausman (1978) test for panel data. It emphasizes that it is a general specification test and that rejection of the null signals misspecification and is not an endorsement of the fixed effects estimator as is done in practice. Non-rejection of the null provides support for the random effects estimator which is efficient under the null. The chapter offers practical tips on what to do in case the null is rejected including checking for endogeneity of the regressors, misspecified dynamics, and applying a nonparametric Hausman test, see Amini, Delgado, Henderson, and Parmeter (2012, chapter 16). Alternatively, for the fixed effects die hard, the chapter suggests testing the fixed effects restrictions before adopting this estimator. The chapter also recommends a pretest estimator that is based on an additional Hausman test based on the difference between the Hausman and Taylor estimator and the fixed effects estimator.

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.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This paper aims to appraise the effectiveness of gender quotas in breaking the glass ceiling for women on boards (WoBs) in companies that are legally obliged to comply with quotas (listed companies and state-owned companies, LP) and in those that are not (unlisted companies and nonstate-owned companies, NLNP). Furthermore, it investigates the glass cliff phenomenon, according to which women are more likely to be appointed to apical positions in underperforming companies.

A balanced panel data of the top 116 Italian companies by total assets, which are present in both 2010 and 2017, is used for estimating ANOVA tests across sectors and fixed-effects panel regression models.

WoBs significantly increased in both the LP and the NLNP companies, and this increase was greater in the financial sector. Furthermore, the relationship between the percentage of WoBs and firm performance is not linear but depends on the financial corporate health. Specifically, the situation in which a woman ascends to a leadership position in challenging circumstances where the risk of failure is high (glass cliff phenomenon) is only present in companies with the lowest performance in the sample, in other words, when negative values of Roe and negative or zero values of Roa occur together.

These findings have relevant policy implications that encourage the adoption of gender quotas even in specific top positions, such as CEO or president, as this could lead to a “double spillover effect” both vertically, that is, in other job positions, and horizontally, toward other companies not targeted by quotas. Practical interventions to support women in glass cliff positions, on the other hand, relate to the extent of supervisor mentoring and support to prevent women from leaving director roles and strengthen their chances for career advancement.

The authors explore the ability of gender quotas to break through the glass ceiling in companies that are not legally obliged to do so, and to the best of the authors’ knowledge, for the first time, the glass cliff phenomenon in the Italian context.

A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production frontier is considered without log-transformation to prevent induced non-negligible estimation bias. Second, the model flexibility is improved via semiparameterization, where the technology is an unknown function of a set of environment variables. The technology function accounts for latent heterogeneity across individual units, which can be freely correlated with inputs, environment variables, and/or inefficiency determinants. Furthermore, the technology function incorporates a single-index structure to circumvent the curse of dimensionality. Third, distributional assumptions are eschewed on both stochastic noise and inefficiency for model identification. Instead, only the conditional mean of the inefficiency is assumed, which depends on related determinants with a wide range of choice, via a positive parametric function. As a result, technical efficiency is constructed without relying on an assumed distribution on composite error. The model provides flexible structures on both the production frontier and inefficiency, thereby alleviating the risk of model misspecification in production and efficiency analysis. The estimator involves a series based nonlinear least squares estimation for the unknown parameters and a kernel based local estimation for the technology function. Promising finite-sample performance is demonstrated through simulations, and the model is applied to investigate productive efficiency among OECD countries from 1970–2019.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

Homelessness has many causes and also is stigmatized in the United States, leading to much misunderstanding of its causes and what policy solutions may ameliorate the problem. The problem is of course getting worse and impacting many communities far removed from the West Coast cities the authors examine in this study. This analysis examines the socioeconomic variables influencing homelessness on the West Coast in recent years. The authors utilize a panel fixed effects model that explicitly includes measures of healthcare access and availability to account for the additional health risks faced by individuals who lack shelter. The authors estimate a spatial error model (SEM) in order to better understand the impacts that systemic shocks, such as the COVID-19 pandemic, have on a variety of factors that directly influence productivity and other measures of welfare such as income inequality, housing supply, healthcare investment, and homelessness.

This article provides the first empirical study on the effects of the COVID-19 pandemic on sustainability reporting in US financial institutions using institutional, stakeholder and legitimacy theories.

The study used the independent sample t-test and Mann–Whitney U test throughout as well as OLS, random effects, fixed effects and heteroskedasticity corrected model to test the impact of the COVID-19 pandemic on sustainability reporting in the US financial sector. A sample from all listed US financial firms was used after controlling for both the Refinitiv Eikon sector classification and the NAICS sector classification.

Using U Mann–Whitney test and independent sample t-test the study revealed that the average ESG score for the pre-COVID19 period is 53% compared with 62.3% for the COVID-19 period, indicating that the sustainability reporting during COVID-19 is much higher compared with the pre-pandemic period. The findings of regression analysis also confirm that the US financial companies increased their sustainability reporting during the COVID-19 pandemic.

This study is an early attempt to look at how the COVID-19 epidemic has affected financial reporting procedures, although it is focused only on one area and other entity-related factors like stock market implications, company governance, internal audit practice, etc could have been considered.

This research offers useful recommendations for policymakers to create standards for regulators on the significance of raising sustainability awareness. The findings are crucial for accounting regulators as they work to implement COVID-19 and enforce required integrated reporting rules and regulations.

The study provides the first empirical evidence on the impact of the COVID-19 pandemic on sustainability reporting, by examining how US financial institutions approach the topic of sustainability during the COVID-19 pandemic and assessing the pandemic's current consequences on sustainability.

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.

Professional sports teams employ highly paid managers and coaches to train players and make tactical and strategic team decisions. A large literature analyzes the impact of manager decisions on team outcomes. Empirical analysis of manager decisions requires a quantifiable proxy variable for manager decisions. Previous research focused on manager dismissals, tenure on teams, the number of substitutions made in games or the number of healthy players on rosters held out of games for rest, generally finding small positive impacts of manager decisions on team success.

The authors quantify manager decisions by developing a novel measure of game-specific coaching decisions: the Herfindahl–Hirschman Index (HHI) of playing-time across players on a team roster over the course of a season.

Evidence from two-way fixed effects regression models explaining observed variation in National Basketball Association team winning percentage over the 1999–2000 to 2018–2019 seasons show a significant association between managers’ allocation of playing time and team success. A one standard deviation change in playing-time HHI that reflects a flattened distribution of player talent is associated with between one and two additional wins per season, holding the talent of players on the team roster constant. Heterogeneity exists in the impact across teams with different player talent.

This is one of the first papers to examine playing-time concentration in the NBA. The results are important for understanding how managerial decisions about resource allocation lead to sustained competitive advantage. Linking coaching decisions to wins can help teams to better promote this core product.