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This study aims to examine the relationship between medication for opioid use disorder (MOUD) among pregnant individuals, referral source, mental health, political affiliation and substance use policies in all 50 states in the USA.

This study describes MOUD receipt among pregnant people with an opioid use disorder (OUD) in 2018. The authors explored sociodemographic differences in MOUD receipt, referrals and co-occurring mental health disorders. The authors included a comparison of MOUD receipt among states that have varying substance use policies and examined the impact of these policies and the political affiliation on MOUD. The authors used multilevel binary logistic regression to examine effects of individual and state-level characteristics on MOUD.

Among 8,790 pregnant admissions with OUD, the majority who received MOUD occurred in the Northeast region (71.52%), and 14.99% were referred by the criminal justice system (n = 1,318). Of those who were self-referred, 66.39% received MOUD, while only 30.8% of referrals from the criminal justice system received MOUD. Those referred from the criminal justice system or who had a co-occurring mental health disorder were least likely to receive MOUD. The multilevel model showed that while policies were not a significant predictor, a state’s political affiliation was a significant predictor of MOUD.

The study has some methodological limitations; a state-level analysis, even when considering the individual factors, may not provide sufficient description of community-level or other social factors that may influence MOUD receipt. This study adds to the growing literature on the ineffectiveness of prenatal substance use policies designed specifically to increase the use of MOUD. If such policies are consistently assessed as not contributing to substantial increase in MOUD among pregnant women over time, it is imperative to investigate potential mechanisms in these policies that may not facilitate MOUD access the way they are intended to.

Findings from this study aid in understanding the impact that a political affiliation may have on treatment access; states that leaned more Democratic were more likely to have higher rates of MOUD, and this finding can lead to research that focuses on how and why this contributes to greater treatment utilization. This study provides estimates of underutilization at a state level and the mechanisms that act as barriers, which is a stronger assessment of how state-specific policies and practices are performing in addressing prenatal substance use and a necessary step in implementing changes that can improve the links between pregnant women and MOUD.

To the best of the authors’ knowledge, this is the first study to explore individual-level factors that include mental health and referral sources to treatment that lead to MOUD use in the context of state-level policy and political environments. Most studies estimate national-level rates of treatment use only, which can be useful, but what is necessary is to understand what mechanisms are at work that vary by state. This study also found that while substance use policies were designed to increase MOUD for pregnant women, this was not as prominent a predictor as other factors, like mental health, being referred from the criminal justice system, and living in a state with more Democratic-leaning affiliations.

The study responds to the calls for multilevel approaches in entrepreneurship research and seeks to answer whether the relationships between personal values and entrepreneurship remain stable across different economic conditions, using the unemployment rate as a moderator. It pays attention to the solo self-employed and women, as these groups are particularly vulnerable when crises occur.

We use Schwartz's theory of human values, which has been understudied in entrepreneurship and follow a correlational research design with micro and macro variables. Multilevel logistic regression is applied to the data from the large sample of 151,032 individuals participating in six waves of the European Social Survey. Solo self-employed are distinguished from those employing others, and analyses are run separately for men and women to understand gender differences.

The findings show that self-direction and achievement are positively, and benevolence and security negatively related to entrepreneurship. The high unemployment rate lowers the positive relationships with self-direction and achievement and mitigates the negative relationship with security, but only for the solo self-employed and not for employers. Results mostly hold for both genders.

The study suggests that security-related values should not be omitted from entrepreneurship research focused on entrepreneurs' values. It also emphasizes the need to distinguish between various subgroups of entrepreneurs and their motivation, which is important for efficient active labor market policies.

The study utilizes multilevel analyses that account for individual- and country-level influences on entrepreneurial activity. It contributes to understanding how economic context influences value salience and supports the applicability of Schwartz's theory of human values in entrepreneurship.

This article aims to explore whether and how economic, political and demographic municipal conditions shape citizens' attitudes regarding decentralised social policies.

The authors analysed the 2018 wave of the Dutch Local Election Studies, which includes a novel survey item asking respondents whether they prefer local social policies to be primarily: (1) protection-based, (2) cohesion-building or (3) activation-based. The authors appended context indicators to that survey and performed multilevel logistic regression analyses (1,913 respondents nested in 336 municipalities).

At the individual level, these preferences are affected by gender, age, income, education and political inclination, as expected. However, preferences towards local social policies are not shaped by local economic, demographic or political conditions. The authors discuss the implications of these findings for future research.

By using unique data, including a newly developed survey item, this study is the first to explore whether and how municipal conditions shape preferences regarding local welfare. Understanding those preferences is increasingly important as many Western European countries have decentralised swathes of social policies from the national to the local level in recent decades.

The purpose of this paper is to investigate how active labour market policy (ALMP) training programmes and hiring subsidies increase or decrease differences in the unemployment risk between lesser and higher educated people during an economic downturn. A focus is put on potential job competition dynamics and cumulative (dis)advantages of the lesser and higher educated.

The paper uses multi-level data. The fifth wave (2010) of the European Social Survey was used and combined with macro-level data on labour market policies of the OECD. The sample consisted of 18,172 observations in 19 countries.

The results show that higher levels of participation and spending on training policies are related to a smaller difference in the unemployment risks of the educational groups. Higher training policy intensity is associated with a lower unemployment risk for the lesser educated and a higher unemployment risk for the higher educated. This implies that the lesser educated are better able to withstand downward pressure from the higher educated, thereby, reducing downward displacement during an economic downturn. Hiring subsidies do not seem to be associated with the impact of education on unemployment.

The paper adds to the discussion on ALMP training and hiring subsidies that are primarily rooted in the human capital theory and signalling theory. Both theories ignore the social context of labour market behaviour. The job competition theory and cumulative (dis)advantage theory add to these theories by focussing on the relative position of individuals and the characteristics that accompany the social position of the individual.

Caregivers are known to experience increased morbidity when compared to noncaregivers. Does an intervention targeting caregiver distress affect their health care utilization? One hundred forty-eight caregivers of allogeneic hematopoietic stem cell transplant patients were randomized to treatment as usual (TAU) or a psychoeducation, paced respiration, and relaxation (PEPRR) intervention. Assessments of caregivers' service utilization were collected at baseline and 1, 3, and 6 months post-transplant. During the first 30 days after patient transplant, caregiver medical and mental health professional service use decreased while support group attendance peaked. Mixed model regressions showed a significant decrease in mental health service use by the PEPRR group (P=0.001). At six months caregivers in TAU had predicted marginal probabilities of mental health services utilization over 10 times as high as caregivers in PEPRR (18.1% vs 1.5%). Groups failed to differ in medical service (P=0.861) or support group (P=0.067) use. We can conclude that participation in PEPRR compared to TAU was associated with reduced mental health service utilization. Caregiver psychosocial support services are critical to improve caregiver outcomes.

Ongoing automation processes may render a fair share of the existing jobs redundant or change their nature. This begs the question to what extent employees affected invest in training in order to strengthen their labour market position in times of uncertainty. Given the different national labour market regimes and institutions, there may be an important geographical dimension to the opportunities to cope with the challenges set by automation. The purpose of this study is to address both issues.

Using data from the 2016 European labour Force Survey, the authors estimate with logit and multi-level regression analyses how the automation risk of a worker's job is associated with the propensity of following non-formal education/training. The authors allow this relationship to vary across European countries.

The results show that employees in jobs vulnerable to automation invest relatively little in training. Also, there are significant differences across Europe in both the provision of training in general and the effect of automation on training provision.

While there is quite a lot of research on the structural labour market effects of automation, relatively little is known about the actions that employees take to deal with the uncertainty they are faced with. This article aims to contribute to our understanding of such mechanisms underlying the structural macro-level labour-market dynamics.

Since the early 1980s, western governments are assumed to have been either moving toward post-bureaucratic models or transforming into so-called neo-Weberian bureaucracies. As different public-sector (reform) models imply different ideal typical personality traits for civil servants, the purpose of this paper is to ask the question to what extent personality requirements that governments demand from their employees have evolved over time in line with these models.

The authors analyzed the use of big-five traits in a sample of 21,003 job advertisements for local government jobs published between 1980 and 2017, applying tools for computer-assisted text analysis.

Using multilevel regression analyses, the authors conclude that, over time, there is a significant increase in the use of personality descriptors related to all big-five factors.

The authors postulate that governments nowadays are actively looking for the “renaissance bureaucrat” in line with the neo-Weberian bureaucracy paradigm. The authors end with a discussion of both positive and negative consequences of this development.

First, the authors explicitly link personality, public administration, and public management using the Abridged Big-Five-Dimensional Circumflex model of personality. Second, by linking observed trends in civil servant personality requirements to larger theories of public-sector reform models, the authors narrow the gap between public administration theories and practice. Third, the software tools that the authors use to digitalize and analyze a large number of documents (the job ads) are new to the discipline of public administration. The research can therefore serve as a guideline for scholars who want to use software tools to study large amounts of unstructured, qualitative data.

The purpose of this study is to make an exploratory analysis of the impact of the entrepreneurial ecosystem (EE) as defined by Acs et al. (2014) on opportunity-driven senior entrepreneurial activity in Latin America.

The research uses data from the Global Entrepreneurship Monitor and the Global Entrepreneurship and Development Institute of five Latin America countries (Argentina, Brazil, Chile, Colombia and Mexico), providing a total of 15,019 observations of people that are 50+ years old, between the years 2013 and 2017. A multi-level logistic regression model was used to estimate the relation between the total entrepreneurial activity by opportunity of seniors and some EE indicators. A total of three equations were estimated on the data set described.

This research confirms the relevance of some elements of EE on senior entrepreneurship in Latin America. Entrepreneurial attitudes have a positive relationship with senior entrepreneurs, generating higher levels of entrepreneurial ventures. The combination of institutions that support these attitudes on the EE enhances senior entrepreneurial activity. It also demonstrates that a higher level of entrepreneurial education at postschool stages is relevant to increasing senior entrepreneurial activity.

This research makes some interesting contributions in the field of measuring the impact of EE on senior entrepreneurship by opportunity in developing countries, filling a literature gap. It allows us to glimpse some measures that policymakers could take to improve the entrepreneurial activity of this segment in the region, such as implementing programs that facilitate networking opportunities and mentorship, along with providing training in business and financial literacy.

Protected designation of origin (PDO) and protected geographical indication (PGI) products form the core of the European Union (EU) quality food policy. Low and fragmented logo recognition perils the entire plan. This work aims to provide a “classification” of European consumers as regards logo awareness based on generic demographic and socio-economic characteristics and to test hypotheses relating PDO awareness with the purchasing behaviour of consumers.

The work utilises publicly available pan-European databases collected from Eurobarometer in four rolling surveys from 2012 to 2017. The statistical analysis exploits the spatially nested nature of the data.

The “logo aware” consumer is distinctively different from the average representative European consumer. A range of demographic, human capital and socio-economic characteristics and behavioural and attitudinal traits differentiate the consumers who are aware of the logo. Country and region effects are vital.

Benefits of large and representative samples accrue by utilising available Eurobarometer surveys. This comes at a cost. The individual researcher has no control over the questions included in the questionnaire.

Consumer classification forms the basis of awareness-raising strategies. It reveals the numerous segments of aware and non-aware consumers and opens a discussion about tools and methods to reach out to the European consumer.

This analysis holds an exact pan-European perspective and incorporates consumers' characteristics, behaviour, attitudes and country and region effects.

The advent of technology has enabled frequent innovations in the digital modes of doing business. While some users are reluctant to adopt one, others have made it their way of life. The purpose of this study is to find out the approximate time frame of a digital user in an emerging economy like India to adopt their favorite digital application. The importance of an individual’s level of internet skills is explored in the aspect of adopting digital innovations.

The study follows a quantitative approach where data collected from both primary and secondary sources are analyzed to arrive at the results. In total, 837 responses collected with the help of a structured questionnaire are entered and analyzed using SPSS. Multinomial logistic regression is the statistical tool used in the research.

Five sets of adopter categories based on Rogers (1983) are created using an adoption score and are represented in a Time Horizon to understand better. The Unaffected adopter category is used as the reference point to compare the set of internet skills against each of the other five categories. The five sets of categories comprising the internet skills depict interesting results in the case of all the four comparisons made. Social skills are not seen to have any influence on a user being an Innovator or Early Adopter as compared to an Unaffected user. It is also established that Operational and Creative skills play important role in users’ probability of being an innovator, early adopter and early majority compared to being unaffected.

The study analyzes the role of internet skills in the process of adopting digital innovations. The role of internet skill although is associated with every aspect of the digital revolution taking place all over, its application including the direct score of a digital user’s level of internet skills is not found in the literature.