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This paper aims to discuss a new source of data detailing state level occupational licensing requirements for 50 professions.

This study's research team gathered state level licensing requirements for 50 profession in all 50 states and DC from 2022 to 2023. The authors include the type of regulation, entry requirements like fees, education, training, good moral character provisions and renewal requirements. The authors include Standard Occupational Classification industry codes to allow researchers to merge it with other publicly available data sources. Finally, the authors present descriptive statistics and provide a comparison of licensing requirements for audiologists, an occupation with variation in entry requirements.

The mean number of the 50 professions licensed in states is 36. On average, these professions require a bachelor's degree, $271 in licensing fees and 26 h of continuing education to renew. For the audiologist profession, there is considerable variation between states in entry requirements like fees and education.

Despite a large body of work on occupational licensing, data limitations still exist. Most analysis focuses on whether a profession is licensed or not. However, there is considerable variation between states for the same profession, providing an avenue for work estimating the effects of specific licensing requirements. A new source of data is introduced and discussed for researchers to use in future analyses of occupational licensing.

Criminal history has been conceptualised as a socially stigmatised identity. From this perspective, we can understand criminal history as invisible, concealable and ‘not readily apparent to others’ (Chaudoir & Fisher, 2010, p. 236). Although previous periods of incarceration cannot be detected per se, during this chapter, we present several elements, such as embodiment, appearance-based inferences (i.e. assumptions of what a criminal history looks like), and information as proxy (e.g. résumé gaps, credit history), which may contribute to individual assessments and interpretations of the appearance of a criminal history. Once perceived, these elements may contribute an array of unique career experiences as individuals with a criminal history seek to navigate their employment experience. Therefore, this chapter offers insight into how the appearance of criminal history information, particularly when presented without a thorough explanation, may be left to interpretation and bias throughout the employment experience.

The purpose of this study is to examine whether variation in franchising across US states can be explained by differences in state regulatory burdens.

Three years of US state-level panel data is used on measures of franchising activity published by the International Franchise Association. The authors measured variation in regulatory burdens across state governments using the regulatory freedom index, developed by the Cato Institute. Multiple regression analysis was the statistical technique used.

Controlling for state-level per capita personal income, educational attainment, unemployment and share of population identifying as non-white, the authors find states with fewer regulatory burdens for business owners have more franchises and franchise jobs per 100,000 residents, higher franchise output per capita and a larger share of small businesses are franchises. These results were robust to alternative econometric specifications. The results support our hypothesis that states with lower regulatory burdens will have more franchising activity.

Only three years of data are currently available; however, our research provides some practical avenues for managers and policy makers to explore when considering new franchise opportunities or developing policies that impact regulatory burdens for small businesses.

This study contributes to the literature by providing supporting evidence for the relationship between US state institutional factors and franchised small businesses, and it adds a cross-state study to the existing literature using cross-country and cross-city data.

A quick search of the headlines of major newspapers reveals a treasure trove of technology procurement gone wrong. While the private sector seems to adopt and implement new technology seamlessly and quickly to deliver for customers, the government struggles to accomplish technology purchases and integrations with the same ease. As governments in the United States are looking to retain their current workforce and attract the next generation of workers, the technological capabilities and ethos of governments will be paramount. With nearly every industry being transformed by technology and Generation T being the first generation to have an ingrained “technology first” mindset, the ability of governments to attract these workers depends, in large part, on the ability to transform their government technology culture, policies, and practices.

In this chapter, the authors examine the administrative branch and observe two key components at the root of most technology failures: poor organizational structure in the bureaucracy and the lack of an empowered Chief Information/Technology Officer. Building upon case studies from Massachusetts and California, this chapter looks at the factors related to failure or success to understand the technology procurement culture. The chapter concludes by presenting four key “best practice” principles of public policy and administration that can be implemented by almost any governmental entity to improve their acquisition and implementation of technology.

This paper aims to demonstrate how skills taxonomies can be used in combination with machine learning to integrate diverse online datasets and reveal skills gaps. The purpose of this study is then to show how the skills gaps revealed by the integrated datasets can be used to achieve better labour market alignment, keep educational offerings up to date and assist graduates to communicate the value of their qualifications.

Using the ESCO taxonomy and natural language processing, this study captures skills data from three types of online data (job ads, course descriptions and resumes), allowing us to compare demand for skills and supply of skills for three different occupations.

This study illustrates three practical applications for the integrated data, showing how they can be used to help workers who are disrupted by technology to identify alternative career pathways, assist educators to identify gaps in their course offerings and support students to communicate the value of their training to employers.

This study builds upon existing applications of machine learning (detecting skills from a single dataset) by using the skills taxonomy to integrate three datasets. This study shows how these complementary, big datasets can be integrated to support greater alignment between the needs and offerings of educators, employers and job seekers.

Exploring how racism continues to persist throughout public and nonprofit organizations is central to undoing persistent society-wide injustices in the United States and around the globe. The authors provide two cases for identifying and understanding the ways in which philanthropy’s whiteness does harm to K–12 students and communities of color.

In this article, the authors draw on critical race theory and critical whiteness studies, specifically Cheryl Harris' work to expose the whiteness of philanthropy, not as a racial identity, but in the way that philanthropy is performed. The authors characterize one of the property functions of whiteness, the right to exclude, as working through two mechanisms: neoliberal exclusion and overt exclusion. Drawing on this construction of the right to exclude, the authors present two cases: the Bill & Melinda Gates Foundation and the City Fund.

Whether intentional or not, the Gates Foundation and the City Fund each exclude communities of color in several ways: from changes to schools and districts, parents' experiences navigating school enrollment due to these changes, to academic assessments and political lobbying.

These cases provide a way for researchers and practitioners to see how organizations in real time reify the extant racial hierarchy so as to disrupt such organizational processes and practices for racial justice.

This study aims to explore the relation among US audit partners’ characteristics, their career advancement and audit quality.

This study uses data from Public Company Accounting Oversight Board Form AP, Auditor Reporting of Certain Audit Participants, and publicly available online data sources. The hand-collected data on audit partners’ personal characteristics include gender, work experience and educational background. The measures for audit quality include restatements and audit fees.

The authors find that audit partner characteristics matter for the time it takes an individual to reach partnership after completing a bachelor’s degree. There are significant differences in work experience and educational background between partners in the largest (Big N) audit firms and smaller (non-Big N) audit firms. Audit partner traits are related to audit quality, and the effects differ between Big N and non-Big N partners.

The literature has examined audit partners’ career paths using international data. However, little empirical academic research has examined the career advancement of US audit partners. This study provides initial insights on the career advancement of US partners on a large scale and complements the recent research that examines audit partner characteristics and audit quality in the US market.

Using social relations theory, we argue that entrepreneurship ecosystems must also include relationships such as market pricing, equality matching, authority ranking and communal sharing to be successful and thrive.

We theorize using Fiske’s typology that a successful entrepreneurial system must have certain characteristics to be successful.

In doing so, we suggest an alternative perspective of the role of exchange relationships in ecosystems which considers both the geographic context and social relationships as equally important ecosystem components. Our contributions include (1) exposing social processes as the explanatory mechanism for exchanges instead of solely market forces, (2) illustrating the role of regional cultural differences in exchanges and (3) emphasizing how entrepreneurs can better realize ecosystem benefits through understanding the methods of exchange in these ecosystems.

Social relationships include a wide variety of different types of resources and exchange mechanisms, so by their inclusion into the entrepreneurship ecosystem literature, a more complete view of ecosystems is possible.

The purpose of this study is to develop a holistic optimization model for an integrated sustainable fleet planning and closed-loop supply chain (CLSC) network design problem under uncertainty.

A novel mixed-integer programming model that is able to consider interactions between vehicle fleet planning and CLSC network design problems is first developed. Uncertainties of the product demand and return fractions of the end-of-life products are handled by a chance-constrained stochastic program. Several Pareto optimal solutions are generated for the conflicting sustainability objectives via compromise and fuzzy goal programming (FGP) approaches.

The proposed model is tested on a real-life lead/acid battery recovery system. By using the proposed model, sustainable fleet plans that provide a smaller fleet size, fewer empty vehicle repositions, minimal CO₂ emissions, maximal vehicle safety ratings and minimal injury/illness incidence rate of transport accidents are generated. Furthermore, an environmentally and socially conscious CLSC network with maximal job creation in the less developed regions, minimal lost days resulting from the work's damages during manufacturing/recycling operations and maximal collection/recovery of end-of-life products is also designed.

Unlike the classical network design models, vehicle fleet planning decisions such as fleet sizing/composition, fleet assignment, vehicle inventory control, empty repositioning, etc. are also considered while designing a sustainable CLSC network. In addition to sustainability indicators in the network design, sustainability factors in fleet management are also handled. To the best of the authors' knowledge, there is no similar paper in the literature that proposes such a holistic optimization model for integrated sustainable fleet planning and CLSC network design.