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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 investigates the association between corporate sustainability disclosures and firm performance and value.

This study collected data from 694 manufacturing companies operating in 34 countries between 2007 and 2019, yielding 6,181 firm-year observations. This study employs a dual-model framework to analyze the influence of environmental, social, and governance (ESG) performance on return on assets (ROA), return on equity (ROE), and Tobin's Q ratio. Two sets of control variables, firm- and country-specific, were incorporated to account for potential confounding factors. To validate the robustness of the findings, we utilized a battery of econometric techniques, including traditional ordinary least squares (OLS), firm-fixed effects, quantile regression, and instrumental variables-generalized method of moments (IV-GMM), applied to both the pooled and firm-fixed effects models.

The findings are contradictory: there is a negative relationship between sustainability disclosure and operating performance and return on equity, but a positive relationship between sustainability disclosure and firm value. The negative correlation is consistent with agency theory and the positive correlation is consistent with the legitimacy and shareholder theories. These results are robust to performance measures and estimation methods.

Short-term profit shouldn't deter sustainability. It boosts legitimacy, reputation, efficiency, and long-term market value. Investors must look beyond profitability ratios, embracing ESG metrics. Firms should see sustainability as strategic investment, not cost. Patience pays off: long-term gains await. Regulation can guide balanced growth, prioritizing both shareholders and societal well-being.

This study is the first to adopt a firm’s fixed-effect quantile regression, which provides deep insights into the role of sustainability disclosure in meeting stakeholders’ expectations.

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.

Corporate social responsibility (CSR) promotes society, reduces risk, and encourages ethical business practices. Due to its relevance, we study how CSR influences firms' sustainable development. We analyze data from 427 New York Stock Exchange (NYSE)-listed firms from 2008 to 2022. The Refinitiv environmental and social score is used to measure CSR, whereas for firms' sustainable development we rely on corporate sustainable growth rate (SGR) and market-based metrics. The analysis employs various econometric techniques, including ordinary least square, fixed effect regression, two-stage least square, generalized method of moment, and simultaneous quantile regression. The results indicate that CSR has a positive and significant effect on firms' sustainable development across all models. This relationship supports the notion that socially responsible business can contribute to long-term financial sustainability in line with “stakeholder theory”, indicating that companies should accommodate the concerns of various stakeholders, including society and the environment, to achieve sustainable development. We evaluate how the conditional distributions of SGR and firms’ value are affected by CSR, categorizing them into high, moderate, and low regimes. The quantile regression estimates indicate that the effect of CSR is more pronounced at upper quantiles, followed by moderate and low regimes. These findings underscore the importance of considering CSR in assessing the SGR and enterprises market value. We also confirm that our results are robust under range of different econometrics' methods. Finally, we enlighten current literature, and our research has useful policy implications for management and investors.

Today, the sustainability and outreach of microfinance institutions (MFIs) are crucial to the success of microfinance and the sector’s potential to make a lasting impact. The ability of MFIs to operate financially well without sacrificing their social goals has come under scrutiny. This study aims to identify the kind of relationships between the two objectives of MFIs in Ethiopia.

This study investigated the association between the outreach and financial sustainability of Ethiopian MFIs from the years 2012 to 2021 using a balanced set of panel data. The study used secondary data and employed a descriptive research design and a quantitative research approach. To this end, random and fixed effects estimation models, as well as three-stage least squares, with the model of seemingly unrelated regression (SUR) are used.

According to the study, outreach performance enables MFIs to achieve sustainability/financial performance. On the other side, MFI that are financially sound improve social performance. There was therefore no trade-off between the two objectives.

As Ethiopia’s microfinance sector shifts away from government and non-government backing and toward commercialization, such research is crucial. This aspect of the Ethiopian microfinance industry has gotten little consideration in research. The SUR model was used in the study together with random and fixed effect estimators, and the most reliable estimation result was chosen based on the necessary tests.

The purpose of this study is to examine the structure of the international fossil fuel trade network (IFFTN) and assess its effects on CO₂ emissions and global trade patterns. This research integrates complex network theory with econometric analysis to explore the dynamics of fossil fuel trade and its implications for environmental quality across various countries. Specifically, the study analyses the roles of different countries within this global network, examines the relationship between trade volumes and environmental impacts and evaluates how advancements in renewable energy generation could mitigate these effects. Through this comprehensive examination, the study seeks to provide an in-depth understanding of the trade-environment nexus.

The study uses data on international fossil fuel trade from 2005 to 2020, which includes 74 countries categorized as high-income, low-income and Asian economies based on their roles in the global market. This research constructs the IFFTN, where countries are depicted as nodes and trade links as edges. The authors analyse network parameters, such as degree, density and clustering coefficient, along with trade metrics like strength and centrality. These parameters are integrated into a panel fixed effects model, with the robustness of the findings confirmed through dynamic ordinary least squares (DOLS) analysis.

The study finds that the dynamic fossil fuel trade network includes key players such as the USA, China, France, India, the Netherlands and South Korea. It demonstrates increased connectivity and dependence among these countries, directly correlating with higher CO₂ emissions. However, this correlation is mitigated by the adoption of renewable energy, particularly in Asia and high-income countries. The impact on environmental quality is mediated through scale, technique and composition effects, suggesting significant environmental improvements through enhanced industry structure, technological progress and economies of scale.

This study recognizes several limitations. First, the categorization of countries into Asian economies, low-income and high-income groups may oversimplify the intricate effects of economic status on environmental impacts. Second, focusing primarily on per capita CO₂ emissions may neglect other critical environmental indicators. Future research should consider examining regional variations and including a wider range of environmental metrics. This approach would offer a more detailed perspective on the nuanced interactions between economic development and environmental sustainability, enhancing the depth and applicability of the findings.

To address the challenges of the IFFTN and CO₂ emissions, several practical policy measures are recommended. Governments should enhance international cooperation by establishing global platforms for sharing best practices and initiating technology transfer agreements to accelerate the adoption of energy-efficient technologies. Additionally, a phased transition towards more sustainable energy sources is crucial, involving increased investment in the renewable energy sector alongside incentives for adopting green technologies. On the trade front, governments should modify trade partnerships to address congestion externalities, fostering a shift towards more sustainable and environmentally friendly trade practices.

The social implications of the IFFTN are profound. As global reliance on fossil fuels continues, communities face heightened health risks due to increased pollution. Transitioning to renewable energy can alleviate these health concerns and the creation of green technologies, enhancing social well-being. Moreover, equitable access to energy-efficient solutions can reduce energy poverty, particularly in low-income countries, fostering greater societal resilience and inclusivity.

This study offers a pioneering examination of the trade-energy nexus across 74 countries, using complex network models to analyse diverse economic settings, particularly in Asian economies dominated by non-renewable energy. It identifies key market players and assesses their impact on dynamics such as congestion and market power. Additionally, the study explores the positive effects of renewable energy capacity on these relationships, highlighting its crucial role in driving sustainable energy transitions and enhancing the understanding of indirect trade-environment interactions.

This study explored how institutional quality influences the relationship between military spending and education in Africa.

This study used data from 43 African countries spanning the years 2000–2021. Two estimation methods were employed to address various issues: Fixed Effects with Driscoll-Kraay standard errors and the Two-Step System Generalised Method of Moments. The Fixed Effects with Driscoll-Kraay standard error method was used to obtain reliable standard errors and inferences from the estimated coefficients of the fixed effects model. Meanwhile, the problem of endogeneity between military spending and education was addressed using the Two-Step System Generalized Method of Moments (GMM).

The results indicated that military spending negatively impacts both the quality and quantity of education. However, both institutional quality and the interaction term (institutional quality*military spending) have positive effects on both measures of education, suggesting that better institutional quality mitigates the negative effect of military spending on education outcomes.

This study shows that institutional quality dampens the negative effect of military spending on education, especially the quality of education. Hence, African countries should prioritize strengthening their institutions to ensure optimal allocation and utilization of government funds for the benefit of their citizens.

This is the first study to examine the moderating role of institutional quality in the relationship between military spending and education, focusing on both the quantity and quality of education.

Our study was designed to investigate the longitudinal trajectories of student leader development capacities in a sample of students enrolled in multiple leadership-focused courses across several semesters. Our goal was to assess the degree to which course enrollment was associated with growth over the time that students engage as undergraduates in academic leadership programs, and if so, to assess the shape and speed of capacity change.

We utilized a multilevel intra-individual modeling approach assessing students’ motivation to lead, leader self-efficacy, and leadership skills across multiple data collection points for students in a campus major or minor focused on leadership studies. We compared an unconditional model, a fixed effect model, a random intercept model, a random slope model, and a random slope and intercept model to determine the shape of score trajectories. Our approach was not to collect traditional pre-test and post-test data – choosing to collect data only at the beginning of each semester – to reduce time cues typically inherent within pre-test and post-test collections.

Our results strongly suggested that individual students differ greatly in the degree to which they report the capacity to lead when initially enrolling in their first class. Surprisingly, the various models were unable to predict a pattern of longitudinal leader development through repeated course enrollment in our sample.

Our investigation employed statistical methods that are not often utilized in leadership education quantitative research, and also included a data collection effort designed to avoid a linear pre-test/post-test score comparison.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.