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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.

Brazil’s regional inequality is an important topic due to the large and persistent differences in development between states and the high levels of inequality in the country. These variations in development can potentially render survey data inaccurate since the significance of capital income varies across the states. Besides, previous studies incorporating tax and national accounts data globally have mainly focused on measuring the income distribution at the country-level. This approach can limit the understanding of inequality, especially when considering large countries such as Brazil.

The methodology used to construct these estimates follows the guidelines of the Distributional National Accounts, whose core goal is to provide income distribution measures consistent with macroeconomic aggregates and harmonized across countries and time. The procedure has three main steps: first, it corrects the survey’s underrepresentation of top incomes using tax data. Then, it accounts for national income items not included in the survey or tax data, such as imputed rents and undistributed profits. Finally, it ensures that all components match the national income.

Compared to survey-based estimations, the results reveal a new angle on the state-level inequality. This study indicates that Amazonas, Rio de Janeiro and São Paulo have a more concentrated income distribution. The top 1\% of earners in these states receives around 28\% of total pre-tax income, while the top 10\% receive nearly 60\%. On the other end, Amapá (AP), Acre (AC), Rondônia (RO) and Santa Catarina (SC) are the states where the income distribution is less concentrated. There were no significant changes in the income distribution across the states during the period analyzed.

This study combines survey, tax and national accounts data to construct new estimates of Brazil’s state-level income distribution from 2006 to 2019. Previous results only considered income captured in surveys, which usually misses a significant part of capital incomes. This limitation may bias comparisons as capital income has different importance across the states. The new estimates represent the income of top groups more accurately, account for the entire national income and enable to compare regional inequality levels consistently with other countries.

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.

The purpose of this study is to examine the influence of conscientiousness on entrepreneurship over and above the impact of other factors that are associated with entrepreneurship in the literature.

The design uses household responses from the Panel Study of Income Dynamics (PSID) biennial survey that follows the same heads of households over time to measure their conscientiousness, businesses owned and other demographic and financial characteristics. Ordinary least squares (OLS), Probit and Poisson regression techniques are applied at the head of household and state level to examine the relationship.

The results show heads of households’ conscientiousness positively relating to the average number of businesses owned, beyond other Big Five traits and the impact of other characteristics. A one-standard deviation increase in conscientiousness is significantly associated with a 0.012 increase in the number of businesses owned. This association is robust to alternative regression specifications and variable measurements.

The results are original to the finance literature, complementing studies by linking intrinsic head of household-level traits to entrepreneurship while controlling for external financial and demographic factors. The study also attempts to externally validate previous findings using aggregate-level outcomes. The data and setting used to measure personality traits as well as entrepreneurial outcomes are original to the entrepreneurship literature, validating previous findings.

The paper investigates whether political geography, as measured by the degree of alignment of state politicians with the party of the USA President, has an impact on corporate fraud convictions.

Prior research shows that the degree of alignment between state politicians and the president's political party is positively correlated with measures of earnings management for firms headquartered in the state. Political alignment is conducive to earnings management because it affects a firm's information and enforcement environment by increasing policy risk and promoting lenient regulatory oversight. The paper posits that this environment is also conducive to corporate fraud and tests this hypothesis using pooled ordinary least squares (OLS) and panel regressions with annual state-level data for 2003–2018.

The paper documents a positive and statistically significant relationship between political alignment and corporate fraud conviction rates by state.

The conclusions are tempered by data limitations. First, the conviction data are available at the state level only. Second, the true level of fraud is inherently unobservable and the conviction data may not reflect the actual number of frauds that are committed.

Fraud examiners might benefit from considering the role of political connectedness in determining fraud risk. Although additional research is needed before making concrete recommendations, the initial indications clearly point to political connections as a potential concern.

The findings build on evidence that political connections influence earnings management. Rather than focusing on direct measures of connectedness, such as lobbying expenditures, the paper examines a plausibly exogenous measure: political geography.

Logistics sector is recognized as one of the core enablers of the economic development of a nation. However, inefficiency in logistics operations impedes the achievement of intended targets by increasing the cost of doing business. Also, it is difficult to improve the efficiency of a country’s logistics operations without a metric for evaluating and understanding logistics capabilities and efficiency. Therefore, the present study has developed In-country Logistics Performance Index (ILP Index) to propose a benchmarking tool to measure the in-country logistics competitiveness, particularly in the setting of emerging economies, i.e. India.

This study has developed a unified index using principal component analysis and quintile approach. In addition, the proposed index relies on several dimensions that are developed and illustrated using quantitative secondary panel data.

The findings of this study reveal that the quality of infrastructure, economy, and telecommunications are the three most important dimensions that may significantly support the growth of the transportation and logistics sector. The results reveal that Gujarat, Tamil Nadu, and Maharashtra are the top performers whereas, Bihar, Jharkhand, and Jammu and Kashmir scores the least due to the insufficient logistics infrastructure as compared to other Indian states.

Given the extensive focus on international-level logistics index (like World Bank’s LPI) in the existing literature, this study intends to develop in-country logistics index to evaluate the logistics capabilities at the regional and state level. In addition, unlike prior studies, this study utilizes quantitative secondary data to eliminate cognitive and opinion bias. Moreover, this benchmarking tool would assist decision-makers in idealizing standard practices toward sustainable logistics operations. Additionally, the ILP index could serve the international investors in crucial decision-making, as it provides valuable insights into a country’s logistics readiness, influencing their investment choices and trade preferences. Finally, the proposed approach is adaptable to measuring the overall performance of any other industry/economy.

This paper aims to investigate how a US firm’s political landscape affects the integration of environmental, social and governance (hereafter ESG) measures in CEO compensation contracts, thereby affecting the firm’s ESG performance and credit rating.

Based on the results of state senatorial and presidential elections and the location of a US firm’s headquarters, the authors categorize whether a firm has a political environment that is predominantly Democratic (blue) or Republican (red). The empirical analyses are based on a sample of US firms in the period 2014–2021.

The authors find that firms in blue states are more likely to link CEO compensation to ESG performance measures. Further, the results show that firms in blue states with ESG-linked compensation contracts have better ESG performance. Lastly, the authors find evidence that a firm’s ESG performance has a positive impact on its credit rating, but the impact is weakened if firms in red states link ESG performance to executive compensation.

To the best of the authors’ knowledge, this is the first research that explores how a firm’s political environment affects the use of ESG performance measures in CEO compensation contracts. Furthermore, the authors contribute to the literature by showing evidence that the political environment interacts with the impact of ESG-linked compensation incentives on the firm’s ESG performance and, thus, its credit rating.

Introduction: The government has taken many initiatives for the overall growth of India after liberalisation and remarkably performed to make India an emerging economy. Due to changes in macroeconomic conditions, investment in companys’ shares includes the possibility of bearing high risk, which cannot be eliminated but, to some extent, minimised. The persistence of risks motivates investors to invest in different available options of investment. Gearing measures, a company’s financial leverage, represent the risk afforded within the company’s capital structure.

Purpose: The research aims to identify the risk-return analysis of financial geared stocks of Nifty 50 companies in India, which have debt equity ratios of more than 1.

Methodology: Convenience and cluster sampling techniques were used to identify companies with debt equity ratios of more than 1. The considered time period is 2010–2019.

Findings: This research found capital structure ratios, debt equity ratio, and total debt ratio. The total equity ratio does not have any visible effect on any of the dependent variables, i.e., Return on equity (ROE), Return on Assets (ROA), Earnings per share (EPS), Return on capital employed (ROCE). It explains the impact of high-levered firms’ performance on profitability and functioning. The study highlights that highly geared companies do not significantly impact the ROA, proving Modigliani and Miller’s (1958) irrelevant theory.