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The paper examines the impact of market power on wages within the context of a developing country, focusing on Brazil.

With access to matched employer–employee data from Brazil, we first characterized the evolution of the local labor market concentration (Municipality Herfindahl–Hirschman Index [HHI]). Then, we built a fixed-effect model with instrumental variables to verify the association between the local labor market concentration and wages. Finally, a difference-in-difference (DiD) was implemented to verify whether a merger transaction impacted the workers’ earnings in the Brazilian banking sector.

The paper’s findings suggest that there may be a negative relationship between market power and workers’ earnings.

This research conducted an in-depth investigation of the labor market power in a developing country. As far as we know, our work is the first to evaluate the extension of local concentration in Brazilian formal labor markets and to illustrate its evolution over the last decades. Additionally, when going through the effects of market concentration on wages, we use a new identification strategy that explores changes in the HHI that are caused by national trends in an industry as a source of exogenous variation. Finally, the last part of the paper assesses the effects of antitrust policy on the labor market, a kind of investigation that is still scarce.

This study aims to develop and apply a process model for prioritizing and selecting basic research projects in developing countries.

Basic research is mainly funded by governments and since, unlike technological research, it does not have clear business goals, its prioritization is one of the complicated issues in formulating science and technology policy. Adopting a design science research methodology, the authors chose a general framework for project portfolio selection as an appropriate artifact for solving this problem. By customizing it for two specific features of this study, i.e. national scale of the problem and the basic nature of research proposals, the authors developed the proposed framework for solving the problem of priority setting.

The process for selecting basic research proposals consists of several steps, which can be categorized into eight steps including strategic decisions, preparation, pre-screening, evaluating individual proposals, screening, portfolio selection and monitoring. This study emphasizes the necessity of defining goals that can be evaluated for the national basic research portfolio, as a key strategic decision. Evaluating individual proposals is a peer-review-based process. In contrast, portfolio selection is done through a zero-one linear programming model. The validity of the proposed framework has been confirmed based on the data obtained from the Iran National Science Foundation.

To the best of the authors’ knowledge, in this research, for the first time, a mathematical model for prioritizing basic research at the national level has been presented, which effectively contributes to policymaking regarding the development of an optimum national research portfolio.

Pakistan ranks third globally in terms of newborn deaths occuring within the first 24 hours of life. With a neonatal mortality rate of 42.0%, it carries the highest burden compared to neighboring countries such as Bangladesh (17%), India (22.7%) and Afghanistan (37%). While there has been a decline in neonatal mortality rates in Pakistan, the pace of this decline is slower than that of other countries in the region. Hence, it is crucial to conduct a comprehensive examination of the risk factors contributing to neonatal mortality in Pakistan over an extended period. This study aims to analyze the trends and determinants of neonatal mortality in Pakistan over three decades, providing valuable insights into this persistent issue.

The study focused on neonatal mortality as the response variable, which is defined as the death of a live-born child within 28 days of birth. Neonates who passed away during this period were categorized as “cases,” while those who survived beyond a specific timeframe were referred to as “noncases.” To conduct a pooled analysis of neonatal mortality, birth records of 39,976 children born in the five years preceding the survey were extracted from four waves (1990–2018) of the Pakistan Demographic and Household Survey. The relationship between risk factors and the response variable was examined using the Cox Proportional Hazard Model. Neonatal mortality rates were calculated through the direct method using the “syncmrates” package in Stata 15.

During the extended period in Pakistan, several critical protective factors against neonatal mortality were identified, including a large family size, improved toilet facilities, middle-aged and educated mothers, female children, singleton live births, large size at birth and longer birth intervals. These factors were found to reduce the risk of neonatal mortality significantly.

This study makes the first attempt to analyze the trends and patterns of potential risk factors associated with neonatal mortality in Pakistan. By examining a large dataset spanning several years, the study provides valuable insights into the factors influencing neonatal mortality.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0604

The purpose of this study is threefold: Determine recent trends in several mental health problems in the USA, identify risk factors that may be responsible for the trends and evaluate intervention policies to reduce the consequences of these problems.

This study used data from the National Survey of Children's Health (NSCH), a nationally representative survey of children under the age of 17 that was conducted between 2016 and 2022. Prevalence rates in the data take into account the probability of selection and nonresponse. Because of the possible correlation in the longitudinal responses in the data, an appropriate extension of the generalized linear models (the marginal models) was used. Marginal models, also known as population-average models, do not require distributional assumptions for the observations, only a regression model for the mean response. The avoidance of distributional assumptions leads to the use of the generalized estimating equations (GEE) method.

The author found that the odds of children and adolescents experiencing mental health problems in the USA changed over a seven-year period, from 2016 to 2022. Anxiety and depression, in particular, have both increased, with anxiety increasing faster than depression; however, behavioral issues and attention deficit disorder/attention deficit hyperactivity disoder (ADD/ADHD) remained stable until 2020 (the start of COVID-19), when they began to rise. This paper also found a link between increased social media use and increased mental health problems, and bullying has a negative impact on the mental health of children and adolescents.

The NSCH, an annual representative survey, was used in this study to assess mental health problems among children and adolescents in the USA. Marginal models, which enable the capture of potential correlations among observations of the same subject, were used in conjunction with the GEE method. This study differs from previous research, which used other surveys, pre-COVID-19 data points and logistic regressions that assumed independence in repeated observations.

The purpose of this paper is to illustrate how COVID-19 lockdowns in the USA impacted traffic safety.

The authors explored the role of vehicle, user and built environment factors on traffic fatalities in the USA, comparing results during COVID-19 lockdowns (March 19th through April 30th, 2020) to results for the same time period during the five preceding years. The authors accomplished this through proportional comparisons and negative binomial regression models.

While traffic levels were 30%–50% below normal during the COVID-19 lockdowns, all traffic fatalities decreased by 18.3%, pedestrian fatalities decreased by 19.0% and bicyclist fatalities increased by 3.6%. Fatal COVID-19 crashes were more likely single-vehicle crashes involving fixed objects or rollovers. COVID-19 traffic fatalities were most common on arterial roadways and in lower density suburban built environments. Findings suggest the importance of vulnerable road users, speed management and holistic built environment policy when pursuing safety on the streets.

The findings have road safety implications not only for future pandemics and other similar events where we would expect decreases in motor vehicle volumes (such as natural disasters and economic downturns) but also for cities that are pursuing mode shift away from personal automobiles and toward alternative modes of transportation.

Taking urban-rural disparity into account, this research examines the current service provision of preschool education at the local level in China.

Following a qualitative design, the research is based on a case study on the preschool service provision in Luochuan County in China, with data collected from semi-structured interviews with grassroots officials and residents from the county’s rural and urban regions.

This research finds that preschool services are inefficiently delivered between the county’s rural and urban areas. While services are oversupplied in rural regions, the popular demand for urban kindergartens is inadequately addressed. Moreover, the tuition subsidy offered by the local government has drastically decreased in recent years.

This research is confined to a single-case study, so its findings are not necessarily applicable to all Chinese localities. Nevertheless, it proves that these problems in preschool service provision can be a result of the central authority’s tight control.

Given China’s rapid urbanization and fertility decline, this research argues that overinvestment in rural kindergartens can be a widespread phenomenon throughout China. It also suggests a decreasing popular demand for other public services, such as childcare, in Chinese rural regions. This research calls for special attention to the persistent regional discrepancy in preschool service standards and the welfare cuts after the revenue centralization reform.

This research contributes to a clearer picture of the current preschool service delivery by Chinese local governments, which remains largely underexplored by far. It also provides updates on the country’s long-existing urban-rural discrepancy in preschool services.

International experience has long been recognized as a crucial determinant for firms’ knowledge management in the existing literature. However, within a global context, the relationship between international experience and the performance of multinational enterprises is intricate and remains ambiguous. While the impact of people mobility has been extensively studied, limited understanding exists regarding how global mobility of people and evolving external environments reshape the relationship. This study aims to integrate existing empirical evidence on this relationship and examines the contingencies posed by environmental factors.

This study conducted a multilevel meta-analysis based on a sample of 231 effect sizes collected from 167 articles to systematically examine the international experience–performance relationship, considering the moderating effect of the global mobility of people and the rise of national sentiments (including authoritarianism and protectionism). A two-stage procedure comprising Hedges-Olkin-type meta-analysis and random-effects meta-analytic regression was adopted.

The findings demonstrate a predominantly positive international experience–performance relationship that varies across studies owing to differences in research design, variable measurements and firm characteristics. The relationship is positively moderated by the global mobility of people, yet the positive effect is contingent upon the level of national sentiments within home countries. The beneficial effect of inbound mobility on this relationship is attenuated by authoritarianism and protectionism, while the effect of outbound mobility is positively influenced by authoritarianism and less affected by protectionism.

This study offers novel theoretical insights into multinationals’ knowledge accumulation in the internationalization process. It contributes to the existing literature by presenting an integrated framework elucidating the international experience–performance relationship. Building upon the knowledge-based view, it integrates environmental dynamics and national sentiments to investigate the performance implications of multinationals’ international experience, thereby providing valuable practical insights for effective global knowledge management.

The health of an economy is heavily dependent on the productivity of the economy's major industries including construction. While most macro-measures of productivity in the USA construction industry indicate a decline, corresponding studies at the individual task level indicate an increase in productivity. Therefore, this paper aims to identify areas where productivity challenges exist and thus provide recommendations for improvement in the construction industry.

A model that relates the way construction projects are executed with the sources of data that inform productivity analyses is developed and presented. This effort/value-flow model informs the data analysis that is performed to determine productivity trends for management and field labor. Further analysis for field labor productivity using field data and management productivity was separately conducted. Management productivity was particularly difficult to gauge, resulting in the use of surrogate measures.

It was observed that while both field labor and management productivities at the industry level have been decreasing, the decrease in management productivity was five times that of field labor productivity. A similar trend was observed for management productivity at the project level.

The primary contribution of this paper to the body of knowledge and industry is the introduction of a holistic analysis of USA construction productivity. Recommendations to improve management productivity include the use of technology, especially project management software.

This study examines the time, place and theme constructs from the viewpoints of their theoretical configurations and potential applications in heritage conservation. The examination taps on the relationship of time to chronology, place to geography and theme to the human agency. Time, place and theme pose challenges in delineating workable definitions and in corroborating the resolvability of the terms into real-world applications. The challenges stem from the metaphysical nature of the constructs and the conjectural interpretability of the associated connotations.

The humanistic setting of the study supports qualitative research and information assessment methods. Information to support the theoretical objective (configurations) comes from the seminal literature on the very nature of time, place and theme and on their relationship with chronology, geography and human agency, respectively. The information to support the applied objective (applications) comes mainly from the works of the US National Park Service heritage programs, particularly the historic context strategy where the members of the triad are applied.

The theoretical knowledge about time, place and theme geared towards framing the constructs' configurations and applications in heritage conservation is lacking. Mediating time into chronology, place into geography and theme into argument provides a useful framework for understanding the constructs' configurations and applications.

The study adds knowledge on time, place and theme that helps delineate the configurations and applications of these constructs to the benefit of heritage conservation planning.

This study explores the complex impact of COVID-19 on India's financial sector, moving beyond simplistic public health vs. economy views. We assess market vulnerabilities and analyze how public sentiment, measured through Google Trends, can predict stock market fluctuations. We propose a novel framework using Google Trends for financial sentiment analysis, aiming to improve understanding and preparedness for future crises.

Hybrid approach leverages Google Trends as sentiment tool, market data, and momentum indicators like Rate of Change, Average Directional Index and Stochastic Oscillator, to deliver accurate, market insights for informed investment decisions during pandemic.

Our study reveals that the pandemic significantly impacted the Indian financial sector, highlighting its vulnerabilities. Capitalizing on this insight, we built a ground-breaking predictive model with an impressive 98.95% maximum accuracy in forecasting stock market values during such events.

To the best of authors knowledge this model's originality lies in its focus on short-term impact, novel data fusion and methodology, and high accuracy.• Focus on short-term impact: Our model uniquely identifies and quantifies the fleeting effects of COVID-19 on market behavior.• Novel data fusion and framework: A novel framework of sentiment analysis was introduced in the form of Trend Popularity Index. Combining trend popularity index with momentum offers a comprehensive and dynamic approach to predicting market movements during volatile periods.• High predictive accuracy: Achieving the prediction accuracy (98.93%) sets this model apart from existing solutions, making it a valuable tool for informed decision-making.