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1 – 10 of 759Emir Malikov, Shunan Zhao and Jingfang Zhang
There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework…
Abstract
There is growing empirical evidence that firm heterogeneity is technologically non-neutral. This chapter extends the Gandhi, Navarro, and Rivers (2020) proxy variable framework for structurally identifying production functions to a more general case when latent firm productivity is multi-dimensional, with both factor-neutral and (biased) factor-augmenting components. Unlike alternative methodologies, the proposed model can be identified under weaker data requirements, notably, without relying on the typically unavailable cross-sectional variation in input prices for instrumentation. When markets are perfectly competitive, point identification is achieved by leveraging the information contained in static optimality conditions, effectively adopting a system-of-equations approach. It is also shown how one can partially identify the non-neutral production technology in the traditional proxy variable framework when firms have market power.
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Taining Wang and Daniel J. Henderson
A semiparametric stochastic frontier model is proposed for panel data, incorporating several flexible features. First, a constant elasticity of substitution (CES) production…
Abstract
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.
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Haoyu Gao, Ruixiang Jiang, Junbo Wang and Xiaoguang Yang
This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence…
Abstract
This chapter investigates the cost of public debt for firms using a comprehensive sample consisting of 17,368 industrial bond issues from 1970 to 2011. The empirical evidence shows that yield spreads for seasoned bond issues are significantly lower than those for initial bond issues. This seasoning effect is robust across different sample periods, subsamples, and model specifications. On average, the yield spreads for seasoned bond issues are around 50 bps lower than those for initial bond issues. This difference cannot be explained by other bond and firm characteristics. The seasoning effect is more pronounced for firms with higher levels of uncertainty, lower information disclosure quality, and longer time intervals between the first and subsequent issues. Our empirical findings provide supportive evidence for the extant theories that aim to rationalize the information role in determining the cost of capital.
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Feng Yao, Qinling Lu, Yiguo Sun and Junsen Zhang
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…
Abstract
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.
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Sirimon Treepongkaruna and Muttanachai Suttipun
The United Nations' sustainable development goals (SDGs) put together a global framework in an attempt to address environmental, social and governance (ESG) concerns. Measuring a…
Abstract
Purpose
The United Nations' sustainable development goals (SDGs) put together a global framework in an attempt to address environmental, social and governance (ESG) concerns. Measuring a company’s contribution to the SDGs relies heavily on ESG reporting. This paper aims to examine the impact of ESG reporting on the corporate profitability of listed companies in Thailand over the period of 2019–2021.
Design/methodology/approach
Using 147 listed firms in the ESG group, content analysis was used to quantify the ESG reporting (within 11 themes), while corporate profitability was measured by return on asset and return on equity. Descriptive analysis, correlation matrix and panel regression are used to analyze the data of this study.
Findings
Consistent with the legitimacy, stakeholder and signaling theories, the authors found a statistically significant and positive impact of ESG reporting on corporate profitability in Thailand.
Originality/value
The findings highlight the importance of incorporating ESG considerations into companies’ reporting and decision-making processes, as these can enhance firm profitability and performance, attract stakeholders, improve their competitive advantage and step toward sustainability.
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Luis Orea, Inmaculada Álvarez-Ayuso and Luis Servén
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…
Abstract
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.
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On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the…
Abstract
Purpose
On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.
Design/methodology/approach
First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.
Findings
Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.
Originality/value
This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.
Highlights
The stability of electricity consumption is important to the stable operation of the grid.
An improved FP-growth algorithm is employed to explore the influencing factors.
The improved algorithm enables the mining of rules containing specific attribute labels.
Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.
The stability of electricity consumption is important to the stable operation of the grid.
An improved FP-growth algorithm is employed to explore the influencing factors.
The improved algorithm enables the mining of rules containing specific attribute labels.
Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.
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Zhichao Wang and Valentin Zelenyuk
Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were…
Abstract
Estimation of (in)efficiency became a popular practice that witnessed applications in virtually any sector of the economy over the last few decades. Many different models were deployed for such endeavors, with Stochastic Frontier Analysis (SFA) models dominating the econometric literature. Among the most popular variants of SFA are Aigner, Lovell, and Schmidt (1977), which launched the literature, and Kumbhakar, Ghosh, and McGuckin (1991), which pioneered the branch taking account of the (in)efficiency term via the so-called environmental variables or determinants of inefficiency. Focusing on these two prominent approaches in SFA, the goal of this chapter is to try to understand the production inefficiency of public hospitals in Queensland. While doing so, a recognized yet often overlooked phenomenon emerges where possible dramatic differences (and consequently very different policy implications) can be derived from different models, even within one paradigm of SFA models. This emphasizes the importance of exploring many alternative models, and scrutinizing their assumptions, before drawing policy implications, especially when such implications may substantially affect people’s lives, as is the case in the hospital sector.
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Xavier Parent-Rocheleau, Kathleen Bentein, Gilles Simard and Michel Tremblay
This study sought to test two competing sets of hypotheses derived from two different theoretical perspectives regarding (1) the effects of leader–follower similarity and…
Abstract
Purpose
This study sought to test two competing sets of hypotheses derived from two different theoretical perspectives regarding (1) the effects of leader–follower similarity and dissimilarity in psychological resilience on the follower's absenteeism in times of organizational crisis and (2) the moderating effect of relational demography (gender and age similarity) in these relationships.
Design/methodology/approach
Polynomial regression and response surface analysis were performed using data from 510 followers and 149 supervisors in a financial firm in Canada.
Findings
The results overall support the similarity–attraction perspective, but not the resource complementarity perspective. Dissimilarity in resilience was predictive of followers' absenteeism, and similarity in surface-level conditions (gender and age) attenuates the relational burdens triggered by resilience discrepancy.
Practical implications
The findings reiterate the importance of developing employees' resilience, while shedding light on the importance for managers of being aware of their potential misalignment with subordinates resilience.
Originality/value
The results (1) suggest that it is the actual (di)similarity with the leader, rather than leader's degree of resilience, that shapes followers' absenteeism and (2) add nuance to the resilience literature.
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Pengkun Liu, Zhewen Yang, Jing Huang and Ting-Kwei Wang
The purpose of this study is to scrutinize the influence of individual learning styles on the effectiveness of augmented reality (AR)-based learning in structural engineering…
Abstract
Purpose
The purpose of this study is to scrutinize the influence of individual learning styles on the effectiveness of augmented reality (AR)-based learning in structural engineering. There has been a lack of research examining the correlation between learning efficiency and learning style, particularly in the context of quantitatively assessing the efficacy of AR in structural engineering education.
Design/methodology/approach
Using Kolb’s experiential learning theory (ELT), a model that emphasizes learning through experience, students from the construction management department are assigned four learning styles (converging, assimilating, diverging and accommodating). Performance data were gathered, appraised, and compared through the three dimensions from the Knowledge, Attitude and Practices (KAP) survey model across four categories of Kolb’s learning styles in both text-graph (TG)-based and AR-based learning settings.
Findings
The findings indicate that AR-based materials positively impact structural engineering education by enhancing overall learning performance more than TG-based materials. It is also found that the learning style has a profound influence on learning effectiveness, with AR technology markedly improving the information retrieval processes, particularly for converging and assimilating learners, then diverging learners, with a less significant impact on accommodating learners.
Originality/value
These results corroborate prior research analyzing learners' outcomes with hypermedia and informational learning systems. It was found that learners with an “abstract” approach (convergers and assimilators) outperform those with a “concrete” approach (divergers and accommodators). This research emphasizes the importance of considering learning styles before integrating technologies into civil engineering education, thereby assisting software developers and educational institutions in creating more effective teaching materials tailored to specific learning styles.
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