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The purpose of this paper is to investigate the effect of managerial ability on tax avoidance in Tehran Stock Exchange (TSE) and OTC by classifying the income smoother and non-income smoother companies based on the theoretical approaches.

To measure the managerial ability the data envelopment analysis is applied based on the accounting data, company and industry characteristics. In this research, hypotheses are tested for the first time by three statistical methods, namely classical regression models, mixed effect multilevel models, and Bayesian multilevel models, which have never been addressed in Persian accounting research studies. The hypotheses are estimated during a 12-year period from 2004 to 2015 in TSE and OTC. In this research, according to Lucas’s critique, structural change test is used in order to control macroeconomic and political variables affecting the results of the study.

The results of hypothesis testing by employing three statistical methods suggest that only one hypothesis of this investigation is significant, which shows the significant association of type of market’s impact (exchange of OTC) on the relationship between managerial ability and tax avoidance.

Each company’s performance is affected by various factors. The study intends to mention that the performance of listed companies in the stock market depends heavily on its financial reports. And investors with perceived perception by the reports, can indirectly squeeze their stock indexes with their sudden sale of stocks, and question the company’s performance with losses to the company.

Each ethnic group has its own peculiar cultural practices that may widen inequalities in child health and survival among ethnic groups. This study estimated ethnic disparities in mortality of under‐five‐year‐olds, controlling for individual and community level characteristics. Using multilevel multivariable regression analysis on a nationally representative sample drawn from 7,864 households in the 2003 Nigeria Demographic and Health Survey, we estimated the risks of deaths under‐five‐year‐olds for 6,029 children nested within 2,735 mothers aged 15‐49 years old, who were in turn nested within 365 communities. Results were expressed as odds ratios with 95% confidence intervals. The observed risk of under‐five death was highest among children of Hausa/Fulani/Kanuri mothers and lowest among children of Yoruba mothers. The mother's affiliation to the Yoruba ethnic group, compared to Hausa/Fulani/Kanuri, was still significantly associated with decreased under‐five mortality (OR = 0.66, 95% CI = 0.45 ‐ 0.96) after adjustment for individual and community level factors. Under‐five mortality was significantly related to socio‐economic and demographic factors (birth order/birth interval, mother's age, and mother's education), which explained much but not all of the ethnic disparities. Findings underscore the need for measures aimed at improving female education and the socio‐economic standard of women, changing short birth spacing norms and reducing inequitable distribution of maternal and child health services.

This paper aims to discuss multilevel modeling for longitudinal data, clarifying the circumstances in which they can be used.

The authors estimate three-level models with repeated measures, offering conditions for their correct interpretation.

From the concepts and techniques presented, the authors can propose models, in which it is possible to identify the fixed and random effects on the dependent variable, understand the variance decomposition of multilevel random effects, test alternative covariance structures to account for heteroskedasticity and calculate and interpret the intraclass correlations of each analysis level.

Understanding how nested data structures and data with repeated measures work enables researchers and managers to define several types of constructs from which multilevel models can be used.

This paper aims to examine how influences at home and school interact to predict smoking among adolescents.

Data were collected from 15‐year‐old pupils from Norway (n=1,404 in 73 Grade 10 school classes). Multilevel logistic regression analysis was used to determine how family and school influences interact to predict adolescent smoking behaviour.

A total of 14 schools strictly enforced the ban on smoking among pupils, and 60 schools strictly enforced existing restrictions on teachers' smoking. Pupils from all schools were exposed to smoking by both their families and teachers. Although the main effects of school enforcement and family smoking were not associated with adolescent smoking behaviour, a cross‐level interaction term between these variables was significantly associated with school level variation in regular smoking (odds ratio [OR] 5.57, 95 percent confidence interval 1.47‐21.12). Individual rather than school's perception of exposure to teachers' smoking, and parental norms were associated with adolescent smoking behaviour, irrespective of the school's level of enforcement.

The effect of school smoking restrictions seems to be dependent on smoking norms at home. Thus, in addition to school‐based anti‐smoking policies, those directed at parents are needed as well.

The present study used a multilevel statistical approach to study the association between the independent factors and adolescent smoking behaviour, an approach that takes into account the cluster effect in hierarchically structured data. The use of this approach is important in studies that involve data with hierarchies as a means to avoid false conclusions.

This paper aims to address the issues of Asian countries toward why females are discouraged and more likely to be removed from the formal financial system than males. Further, whether there is any connection between religion and women’s financial inclusion is also addressed.

This paper explores gender disparities in the use of structured financial services through multilevel models tailored to the individual. The data from 2004 to 2017 have been used for eight South Asian countries, including Bangladesh, India, Pakistan, Sri Lanka, Afghanistan, Maldives, Nepal and Bhutan. We used a multilevel modeling methodology to estimate the impact of the socio-economic climate on women’s financial inclusion while controlling for individual-level features, with all control variables included, the two-level logistic regression model used for this study.

The results of this study demonstrate that sex appears to be strongly correlated with the usage of financial services. The study also found that in nations where religious restrictions limit women’s willingness to work for a living, they are less likely than males to own a bank account. However, through legislation and regulations, countries that encourage gender equality in the labor market and have effective regulatory mechanisms to maintain these initiatives appear to have more financially active women.

This research advises that government authorities strengthen women’s empowerment in South Asian countries.

To the best of the authors’ knowledge, this is the first paper that explains the linkage between financial inclusion and women empowerment and will contribute to existing knowledge.

The purpose of this paper is to analyze the influence of firm-, industry- and country-level determinants on real annual sales growth in the context of a cross-classified multilevel perspective.

The authors studied 11,381 firms from 17 industries in six Latin American countries based on the data collected up to 2015. Since the data are nested in two levels (level 1: firms; level 2: cross-classification of industries and countries), the authors use a cross-classified multilevel model. The significant variability in all levels of analysis confirms the option for the multilevel model.

Differences in industries account for the largest proportion of variance (77.2 percent). This finding indicates that industry-level characteristics should be explored in the sales growth literature (it seems to the authors that they were neglected). This finding also calls attention to the roles of policy-makers in facilitating firm growth. The final model indicates that the considered variables explain approximately 55 percent of the differences in real annual sales growth in the same industry and country after having accounted for the impacts of the differences in firms. After accounting for the impacts of the differences in firms’ and countries’ characteristics, 43 percent of the variation in average real annual sales growth is due to differences in industries. The obtained results indicate that while firms from countries with higher GDP growth and more effective corporate boards present higher real annual sales growth, firms that operate in commodity producer industries have worse performance in this indicator. With respect to firm’s characteristics, larger firms (contradicting Gibrat’s law) and exporters grew less. Some results could be explained by the decrease in commodities’ prices and global purchases between 2012 and 2015.

The paper fills some gaps in the firm growth literature by testing Gibrat’s law in non-developed countries (not yet done, to the best of the authors’ knowledge) and exploring variables other than size in the explanation of firm growth (rarely used, to the best of the authors’ knowledge). Moreover, the adopted model correctly estimated the origin of the variability in firm growth in its natural cross-classified distinct levels.

This paper seeks to answer the practical question whether the institutionalisation level of same‐sex relationships can affect the social acceptance of lesbian women and gay men in Europe, and highlight some of the factors that can potentially determine the incidence of homophobia in 26 European countries.

The study contributes to the literature on acceptance of lesbian women and gay men in Europe by using the European Social Survey dataset, focusing especially on a key variable measuring the agreement level with the statement that gay men and lesbians should be free to live their own life as they wish. For data analyses, explanatory models were constructed by applying multilevel mixed‐effects linear regression.

The study presented empirically tested arguments that the introduction of same‐sex partnership legislation can lead to a decrease of anti‐gay/lesbian attitudes, as has happened in the European countries examined in this study.

Future research in more societies is needed to examine the long‐term effects of the introduction of same‐sex partnership legislation on homophobia.

A key policy implication of highlighting that the provision of equal rights for gay and lesbian citizens in the form of same‐sex marriage and registered partnership can positively influence attitudes, is to urge policy‐makers to introduce these legal frameworks in order to create a more inclusive society.

The content presented in this paper is based on the authors’ own original research.

Wide differences in response rates to the Carbon Disclosure Project’s (CDP’s) climate change program between countries have been explained by legal origins and the varying extent of environmental regulation. This paper seeks to enhance the explanation by examining the relevance of two dimensions of “democratic capital” – both the influence of countries’ experiences with democratic government recruitment are considered, as well as experiences with civil liberties. In addition, it is examined whether these forms of democratic capital are mediated by environmental regulation.

The authors draw upon the literature on the relationship between political regime form and environmental policy and the environmental disclosure literature debate. Hypotheses are based on institutional and stakeholder theory. Methodologically, multilevel regression analysis is used.

Results show that the history of democratic government recruitment is a relevant factor to explain firms’ disclosure decisions. The amount of freedom in civil society seems to also matter, but results are less clear in this regard. The hypothesis concerning the mediation effects of environmental regulation could not be corroborated. Findings, thus, corroborate the claim that standards of informational transparency flourish best in countries with a pluralistic political culture.

The results imply that voluntary carbon transparency may thrive as democratization advances, but its success may also be endangered by the recent revitalization of authoritarianism.

The authors deliver the first paper which tests the hypotheses on the influence of the “democratic capital” on the countries-of-origin on the firms’ carbon disclosure decisions, based on a multilevel analysis.

The purpose of this study is to investigate the influence that national levels of social capital have on entrepreneurial activity. Specifically, we argue that national and regional level social capital positively influences the ability of entrepreneurs to mobilize and access important resources thereby positively impacting the rate of entrepreneurship within nations and regions.

We advance a multilevel and multidimensional conceptualization of social capital. Then based on a dataset of 68 nations and 665 within-nation regions, we empirically evaluate the effects of social capital at the national and regional level in explaining differences in entrepreneurial activity across nations and regions using a combination of regression analysis and multilevel hierarchical linear modeling (HLM).

Our findings emphasize the importance of formulating a multilevel conceptualization of social capital for entrepreneurship research. We discuss the results, provide implications for public policy and suggest avenues for future research.

The overwhelming majority of entrepreneurship research focused on investigating the implications of social capital reside at the individual level of analysis. Our unique inquiry is an inaugural effort to consider this important implications at the macro and meso-level of analysis by examining both regional and national-level effects.

The authors propose a framework to estimate the probability of being poor in a dynamic setting based on a large information set that includes individual characteristics and macro-economic variables. The joint inclusion of personal characteristics along with contextual factors allows separation of idiosyncratic shocks from aggregate shocks affecting poverty. The authors combine data from different cross-sectional surveys and fit a dynamic logistic hierarchical model within a Bayesian framework using standard Markov chain Monte Carlo techniques. The authors’ approach is exemplified by estimating household poverty status in Kyrgyz Republic as a function of time, regions, country, regional level variables and household level socio-demographic characteristics.