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This paper assesses the impact of this program on the rural women's employment opportunities using data from the 2015 round of the household's living standard survey (HLSS) of Côte d'Ivoire.

In 2013, in order to improve the living conditions of the rural population, the Ivorian government launched the National Program for rural electrification (PRONER) to electrify all localities with more than 500 inhabitants.

The results show that PRONER, while reducing the time allocated to performing household chores, increases women's employment through the reallocation of time to full-time paid work in the agricultural and non-agricultural sectors. The authors also find that the allocation of men's time is not affected by this programme. A possible mechanism that would explain such a pro-women effect is the labour-saving technology introduced to home production as an effect of the reform.

As a limitation, it is important to note that these results were obtained in the specific context of PRONER in Côte d’Ivoire and are not necessarily applicable to rural electrification programmes in other contexts. Furthermore, the choice of other indicators to measure women's empowerment is limited by the quality of the data available. It would be interesting for future research to extend this analysis to include other aspects of women's empowerment and household welfare.

This paper is the first to the author’s knowledge to apply a robust econometric method by combining an inverse probability weighted regression adjustment model with Heckman sample selection method to access a robust causal effect of the PRONER in Côte d'Ivoire.

This research assesses the economic impact of biomass plant installations on Brazilian municipalities, focusing on (1) labor income, (2) sectoral labor income and (3) income inequality.

Municipal data from the Annual Social Information Report, the National Electric Energy Agency and the National Institute of Meteorology spanning 2002 to 2020 are utilized. The Synthetic Difference-in-Differences methodology is employed for empirical analysis, and robustness checks are conducted using the Doubly Robust Difference in Differences and the Double/Debiased Machine Learning methods.

The findings reveal that biomass plant installations lead to an average annual increase of approximately R$688.00 in formal workers' wages and reduce formal income inequality, with notable benefits observed for workers in the industry and agriculture sectors. The robustness tests support and validate the primary results, highlighting the positive implications of renewable energy integration on economic development in the studied municipalities.

This article represents a groundbreaking contribution to the existing literature as it pioneers the identification of the impact of biomass plant installation on formal employment income and local economic development in Brazil. To the best of our knowledge, this study is the first to uncover such effects. Moreover, the authors comprehensively examine sectoral implications and formal income inequality.

A brief review of earlier studies on the productivity scenario of Indian industry shows that most of the studies analysed are confined to either parametric approach or growth accounting approach of measuring productivity. At the same time, the few studies based on the non-parametric [namely, Malmquist productivity index (MPI)] overlook the returns to scale conditions as well as the bias involved in the estimation of distance functions. Given this backdrop, this study aims to provide a robust measure of productivity, which considers the returns to scale assumptions and correct for the bias involved in the estimation of productivity.

This study empirically tests for the returns to scale that exists in the chemical and chemical products industry in India. The test result suggests that Ray and Desli (1997) approach of MPI is the appropriate one for the present context. Initially, the conventional Ray and Desli (1997) estimation and decomposition of MPI for the period 2001 to 2017 is being used. Subsequently, to correct for the bias in the estimation of efficiency scores used for the estimation of MPI, the bootstrapping algorithm of Simar and Wilson (2007) has been extended into the context of MPI estimation.

The results from the conventional Malmquist productivity estimates testifies to an improvement of total factor productivity (TFP) in seven out of 16 years under consideration. On the contrary, TFP growth is recorded only in the four years throughout the period after the bias correction. A greater discrepancy between the two measures has been found in the case of scale change factor component of MPI.

The technical change (TC) component positively influences TFP, whereas scale change factor (SCF) deteriorates the TFP condition of this industry. It will be appropriate for these firms to identify and operate under an optimal scale of operation, along with reaping the benefits of technological change. From a methodological perspective, researchers should consider the potential bias that arise in estimation of TFP and use a larger sample whenever possible.

This paper brings in a new perspective to the existing literature on industrial productivity. As against earlier studies, this study empirically tests the returns to scale of the sector under consideration and uses the most appropriate approach to measure productivity. The effect of sampling bias on TFP and its components is analysed.

The main purpose of this paper is to determine the bias-corrected efficiencies and rankings of the selected hotels and restaurants (H&Rs) in India.

The data for the Indian H&R sector are collected from the Prowess database. The bootstrap data envelopment analysis (DEA) based on a constant return to scale (CRS), variable return to scale-input oriented (VRS-IP) and variable return to scale-output oriented (VRS-OP) are applied on H&Rs to obtain the bias-corrected efficiencies.

It is found that relative efficiencies using basic DEA methods of all the 45 H&Rs of India are overestimated. These efficiencies are corrected using bias correction through bootstrap DEA methods. The bounds for the efficiencies of each H&R are computed using all the adopted methods. All H&Rs are ranked using bias-corrected efficiencies, and the linear trend between ranks suggests that the H&Rs are ranked almost similarly by all the adopted methods.

To improve efficiency, Indian H&R companies must rethink their personnel needs by enhancing their workforce management capabilities. The government needs to extend more support to this sector by introducing a liberal legislation framework and supporting infrastructure policies.

There is a paucity of studies on H&Rs in India. The current study focused on measuring bias-corrected efficiencies of the selected H&Rs of India. This study is one of the few initiatives to explore bias-corrected efficiencies extensively using the bootstrap DEA method.

The purpose of this study is to address a highly heterogeneous rift margin environment and exhibit considerable spatiotemporal hydro-climatic variations. In spite of limited, random and inaccurate data retrieved from rainfall gauging stations, the recent advancement of satellite rainfall estimate (SRE) has provided promising alternatives over such remote areas. The aim of this research is to take advantage of the technologies through performance evaluation of the SREs against ground-based-gauge rainfall data sets by incorporating its applicability in calibrating hydrological models.

Selected multi satellite-based rainfall estimates were primarily compared statistically with rain gauge observations using a point-to-pixel approach at different time scales (daily and seasonal). The continuous and categorical indices are used to evaluate the performance of SRE. The simple scaling time-variant bias correction method was further applied to remove the systematic error in satellite rainfall estimates before being used as input for a semi-distributed hydrologic engineering center's hydraulic modeling system (HEC-HMS). Runoff calibration and validation were conducted for consecutive periods ranging from 1999–2010 to 2011–2015, respectively.

The spatial patterns retrieved from climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP) and tropical rainfall measuring mission (TRMM) rainfall estimates are more or less comparably underestimate the ground-based gauge observation at daily and seasonal scales. In comparison to the others, MSWEP has the best probability of detection followed by TRMM at all observation stations whereas CHIRPS performs the least in the study area. Accordingly, the relative calibration performance of the hydrological model (HEC-HMS) using ground-based gauge observation (Nash and Sutcliffe efficiency criteria [NSE] = 0.71; R² = 0.72) is better as compared to MSWEP (NSE = 0.69; R² = 0.7), TRMM (NSE = 0.67, R² = 0.68) and CHIRPS (NSE = 0.58 and R² = 0.62).

Calibration of hydrological model using the satellite rainfall estimate products have promising results. The results also suggest that products can be a potential alternative source of data sparse complex rift margin having heterogeneous characteristics for various water resource related applications in the study area.

This research is an original work that focuses on all three satellite rainfall estimates forced simulations displaying substantially improved performance after bias correction and recalibration.

The purpose of this paper is to prepare flood hazard map and show the extent of flood hazard under climate change scenarios in Woybo River catchment. The hydraulic model, Hydrologic Engineering Center - River Analysis System (HEC-RAS) was used to simulate the floods under future climate scenarios. The impact of climate changes on severity of flooding was evaluated for the mid-term (2041–2070) and long-term (2071–2100) with relative to a baseline period (1971–2000).

Future climate scenarios were constructed from the bias corrected outputs of five regional climate models and the inflow hydrographs for 10, 25, 50 and 100 years design floods were derived from the flow which generated from HEC-hydrological modeling system; that was an input for the HEC-RAS model to generate the flood hazard maps in the catchment.

The results of this research show that 25.68% of the study area can be classified as very high hazard class while 28.56% of the area is under high hazard. It was also found that 20.20% is under moderate hazard and about 25.56% is under low hazard class in future under high emission scenario. The projected area to be flooded in far future relative to the baseline period is 66.3 ha of land which accounts for 62.82% from the total area. This study suggested that agricultural/crop land located at the right side of the Woybo River near the flood plain would be affected more with the 25, 50 and 100 years design floods.

Multiple climate models were assessed properly and the ensemble mean was used to prepare flood hazard map using HEC-RAS modeling.

This study aims to provide a response to the commentary by Yuan on the paper “Marketing or Methodology” in this issue of EJM.

Conceptual argument and statistical discussion.

The authors find that some of Yuan’s arguments are incorrect, or unclear. Further, rather than contradicting the authors’ conclusions, the material provided by Yuan in his commentary actually provides additional reasons to avoid partial least squares (PLS) in marketing research. As such, Yuan’s commentary is best understood as additional evidence speaking against the use of PLS in real-world research.

This rejoinder, coupled with Yuan’s comment, continues to support the strong implication that researchers should avoid using PLS in marketing and related research.

Marketing researchers should avoid using PLS in their work.

This rejoinder supports the earlier conclusions of “Marketing or Methodology,” with additional argumentation and evidence.

Groups once marginalized by culture, ethnicity, class, sexuality, age, and physical ability have entered and impacted business, service, and educational institutions. To unify their widening communities, leaders must pursue inclusivity, which demands more than equitable demographics. Inclusivity integrates each individual’s perspective, regardless of group – the tougher goal of equitable belonging. Most diversity, equity, inclusion, and belonging programs agree that inclusivity starts with leaders’ acknowledging their own biases and committing to organizational reform. Yet few apply leadership principles to gain crucial team collaboration in the project. This chapter explicitly shows public- and private-sector executives and instructors how to guide staffers and students to understand and welcome unfamiliar cultural, social, and personal variances so they themselves create an inclusive cohort. Experiential activities, games, performance arts, and focused, reflective debriefings help make inclusivity the norm by playfully but persistently uncovering even unconscious exclusionist assumptions and replacing them with informed, diversity-positive interactions. These emotionally engaging exercises reveal that exclusionism emerges most bluntly in casual conversation, which both displays and perpetuates preconceptions. Fortunately, self-corrected speech can become the avatar and instrument of inclusivity. So the gentle unearthing and disproving of biases about cultural, social, and personal differences allow participants to construct a diversity-enhanced unity deeper than uniformity. Albeit temporary and simulated, such visceral learning experiences dramatically immerse players in the hurtful disregard caused by microaggressions of privilege and prejudice about cultures, ethnicities, classes, sexualities, ages, and abilities. These exercises and leaders’ modeling grow collegiality despite – indeed, through – human variety, letting all celebrate their individuality while greeting new views and voices.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

The paper proposes a research method to verify the perception bias of consumers on the freshness preservation effects of vacuum packaging (VP) and modified atmosphere packaging (MAP) chilled pork packages, the influence of “sensory experience” on correcting consumers' perception bias of packaging performance and willingness-to-pay (WTP) enhancement channels.

Using data from 458 and 188 participants who completed the contingent valuation method (CVM) and auction experiment, respectively, the study aimed to uncover consumers' packing quality perception bias and WTP, and investigated the societal factors that contribute to variations in WTP.

The CVM experiment revealed that although consumers' high perception bias rate toward MAP to maintain freshness, as compared to lab test results, came along with low WTP premium to cost rate with sensory experience in the auction experiment, the proportion of consumers with quality perception bias decreased from 49.85% to 34.46%, while the WTP premium to cost rate for MAP increased largely by 36.7%. Perceptive embedding has a positive effect on chilled pork packaging WTP, while normative embedding decreases WTP.

The findings emphasize the need of public policies to promote positive consumption attitudes, while whittling the negative consumption norms, to increase the WTP for packaged child pork and promote the chilled pork market formation.