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The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering-applied to 2017-20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Although the Chinese government has implemented a variety of measures, the gender wage gap in 21st century China has not decreased. A significant body of literature has studied this phenomenon using sector segmentation theory, but these studies have overlooked the importance of the collective economy beyond the public and private sectors. Moreover, they have lacked assessment of the gender wage gap across different wage groups, hindering an accurate estimation of the gender wage gap in China, and the formulation of appropriate recommendations. Utilizing micro-level data from 2004, 2008, and 2013, this paper examines trends in the gender wage gap within the public sector, private sector, and collective economy. Employing a selection bias correction based on the multinomial logit model, this study finds that the gender wage gap is smallest and most stable within the public sector. Furthermore, the private sector surpasses the collective economy in this period, becoming the sector with the largest gender wage gap. Meanwhile, a recentered influence function regression reveals a substantial gender wage gap among the low-wage population in all three sectors, as well as among the high-wage population in the private sector. Additionally, employing Brown wage decomposition, this study concludes that inter-sector, rather than intra-sector, differences account for the largest share of the gender wage gap, with gender discrimination in certain sectors identified as the primary cause. Finally, this paper provides policy recommendations aimed at addressing the gender wage gap among low-wage groups and within the private sector., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The impact of a chief executive officer's (CEO's) functional experience on firm performance has gained the attention of many scholars. However, the measurement of functional experience is rarely disclosed in the public database. Few studies have been conducted on the comprehensive functional experience of CEOs. This paper used the upper echelons theory and obtained deep-level curricula vitae (CVs) data through the named entity recognition technique. First, we mined 15 consecutive years of CEOs' CVs from 2006 to 2020 from Chinese listed companies. Second, we extracted information throughout their careers and automatically classified their functional hierarchy. Finally, we constructed breadth (functional breadth: functional experience richness) and depth (functional depth: average tenure and the hierarchy of function) for empirical analysis. We found that a CEO's breadth is significantly negatively related to firm performance, and the quadratic term is significantly positive. A CEO's depth is significantly positively related to firm performance, and the quadratic term is significantly negative. The research results indicate a u-shaped relationship between a CEO's breadth and firm performance and an inverted u-shaped relationship between their depth and firm performance. The study's findings extend the literature on factors influencing firm performance and CEOs' functional experience. The study expands from the horizontal macro to the vertical micro level, providing new evidence to support the recruitment and selection of high-level corporate talent., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

This paper describes the development of an RStudio (now known as Posit) dashboard derived from the Integrated Postsecondary Educational Data System, the United States Census Bureau, and the Bureau of Labor Statistics and provides the user with institutional, community, and career information of IPEDS reporting higher education institutions in the United States and its territories. With this dashboard, users can select and learn about institutions, explore enrollment trends and demographics, compare outcomes, and correlate community and institutional variables. Users can also link degrees to career projections and wages. This paper explains how the dashboard was developed with examples of R programming language., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Perkins, Carrier. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

The aim of the paper is to propose the construction of an index that captures the economic complexity of cities over the globe, as well as to explore whether it is a good predictor for a range of city-level economic outcomes. This index aspires to mitigate data scarcity for cities and to provide policy makers with the tools for monitoring the evolving role of cities in the global economy. Analytically, we implement the economic complexity methodology on data for the ownership, location and economic activities of the world's 3,000 largest firms and their subsidiaries to propose a new indicator that quantifies the network of the largest cities worldwide and the economic activities of their globalized firms. We first show that complex cities are the highly diversified cities that host non-ubiquitous economic activities of firms with global presence. Then, in a sample of EU cities, we show that complex cities tend to be more prosperous, have higher population, and are associated with more jobs, human capital, innovation, technology and transport infrastructure. Last, using OLS methodology and accounting for several other confounders, we show that a higher ECI, at the city level, enhances the resilience of cities to negative economic shocks, i.e., their ability to bounce back after a shock. Specifically, we find that the expected increase of the ratio of employment in 2012 over 2006 is 0.01 (mean: 0.992; standard deviation: 0.081) when the ECI increases by 1 unit (mean: 0.371; standard deviation: 1.094), i.e., a satisfactory pace of recovery, in terms of employment. The ability to diversify in the presence of a shock, the reallocation of factors of production to other sectors and the ability to extract rents associated with those diversified activities, uncovers the mechanics of the ECI index., Competing Interests: The authors have declared that no competing interests exist.