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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.

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.

Research has examined how new ventures strengthen local economic outcomes; however, limited research examines health-oriented ventures and their impact on social outcomes, including health outcomes. Increased VC investment in healthcare service start-ups signals more activity toward this end, and the need for further academic inquiry. We examine the relationship between these start-ups and county-level health outcomes, health factors, and hospital utilization.

Data on start-ups funded via institutional venture capital from PitchBook were merged with US county-level outcomes from the County Health Rankings and Area Health Resources Files for 2010 to 2019. We investigated how the number of VC-funded healthcare service start-ups, as well as a subset defined as innovative, were associated with county-level health measures. We used panel models with two-way fixed effects and Propensity Score Matched (PSM), controlling for demographics and socioeconomic factors.

Each additional VC-funded healthcare service start-up was related to a significant 0.01 percentage point decrease in diabetes prevalence (p < 0.01), a decrease of 1.54 HIV cases per 100,000 population (p < 0.1), a 0.02 percentage point decrease in obesity rates (p < 0.01), and a 0.03 percentage point decrease in binge drinking (p < 0.01). VC-funded healthcare service start-ups were not related to hospital utilization.

This work expands our understanding of how industry-specific start-ups, in this case healthcare start-ups, relate to positive social outcomes. The results underscore the importance of evidence-based evaluation, the need for expanded outcome measures for VC investment, and the possibilities for integration of healthcare services and entrepreneurship ecosystems.

In recent decades, the use of capital punishment has declined, but in its place, a ‘new death penalty’ has arisen: life without parole (LWOP), which is being used far more frequently and for more crimes than capital punishment ever was. Yet, LWOP has received far less scholarly attention than the death penalty. Because of its greater scale, assessing the effects of LWOP on crime has important policy implications and is a better test of extreme penalties. Existing studies of LWOP focus on humanitarian issues and ignore its potentially reciprocal relationship with crime. Therefore, we use available LWOP data to fill these gaps in the literature, using models specifically designed to control for potential reciprocal effects. The results indicate there is no reciprocal causation between LWOP and violent crime and, at best, LWOP’s impact on crime is small, temporary, and, most importantly, no greater than the impact of life with parole.

This paper focuses on the relationship between the regulatory capital requirements and the supply of credit for commercial banks that are operating in the MENA region from 1999 till 2017.

The application of the Fixed Effects Model on a panel of commercial banks in the MENA region has shown a negative relationship between supply of credit and both the capital requirements and solvency ratios.

The results showed that the idiosyncratic, the macroeconomic and the institutional variables affect the supply of credit behavior of banks. The robustness tests using the Two-Stage Least Square method (2SLS) also led to a negative correlation between the growth of credit and capital requirements. Specific macroeconomic and institutional variables have revealed the expected sign and are significant regardless of the estimated specifications.

This work can be subjected to further future extensions. The explanatory power of our model can be improved by incorporating variables that reflect the corporate governance and structure of banking sector. Similarly, we can also include a variable that takes into account the increasing competition that could affect the stability of the banking sector and therefore the prudential banking regulation.

Previous studies that investigated only the relationship between capital level and risk-taking behavior of banks in the MENA region did not take into account neither the economic and institutional environment nor the impact of these regulations on credit (loans) supply.

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 paper aims to assess the impact of corporate social irresponsibility (CSiR) media coverage on firm performance in India. It also analyses the effects of the environment, social, governance, and cross-cutting issues on firm performance.

The paper utilizes a sample of Indian firms from the Reprisk^® database, amounting to 1,103 CSiR media coverage counts for 693 firm-year annual observations from 2008 to 2015. Further, Reprisk^® segregates comprehensive CSiR coverage counts into the environment, social, governance and cross-cutting issues, for which the study runs the fixed effects panel regression. The study takes year-fixed effects, industry-fixed effects and clustered standard errors at the industry level.

The results of this study indicate that CSiR coverage negatively influences the firm performance of Indian firms. All issues, including social, governance and cross-cutting, except environmental issues, negatively impact firm value in India.

The involvement of firms in CSiR costs the firms financially and drives down firm performance. Social issues, including community and employee-related matters, governance issues and cross-cutting issues, also reduce the firm performance.

The insignificant environmental impact on firm performance does not indicate that environmental issues have no detrimental consequences. Instead, it might need more stakeholders' awareness to understand the harmful implications of environmental issues on society.

Limited studies have explored CSiR in India so far. The study is novel as it analyses the Reprisk^® database and its segregation of media counts into the environment, social, governance and cross-cutting issues in the Indian context.

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.

The purpose of this study is to identify how firms' sustainability performance is affected by external knowledge sources and absorptive capacity, accounting for the influence of being located in a science and technology park (STP).

Drawing on data from the Spanish Technological Innovation Panel, the authors estimate the determinants of sustainability performance using fixed effects multiple linear regression models with robust standard errors. The analysis covers the period 2009–2016, with a total panel of 8,874 companies and a total sample of 47,870 observations.

This study highlights the heterogeneity in on-park firms’ sustainability performance, which can be explained by the different capacities of these firms when it comes to embedding themselves in STP networks and processes and effectively absorbing the knowledge from the many knowledge sources that may be on offer in the park.

This paper contributes to the literature by examining the influence of external sources of knowledge and absorptive capacity, and the relationship between them, on sustainability performance. This study approaches sustainability performance as an aggregate measure of firms’ competitiveness and potential for long-term survival from the triple bottom line perspective. In addition, this study examines the effect that location in an STP can have on business sustainability performance and, more specifically, the mediating effect that knowledge sources and absorptive capacity can exert on this relationship.