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The purpose of this study is to examine the effects of health on wages of Australian workers, with a focus on gender differences and the role of macroeconomic conditions in the effects.

The first 15 waves of the Household, Income and Labour Dynamics in Australia survey are used to estimate a wage model that accounts for the endogeneity of health, unobserved heterogeneity and sample selection bias.

The results show that, after accounting for the endogeneity of health, unobserved heterogeneity and sample selection bias, better health increases wages for Australian male workers, but not for female workers. The results also show that accounting for the endogeneity of health, unobserved heterogeneity and potential sample selection bias is important in estimating the effects of health on wages. In particular, a simple ordinary least squares estimator would underestimate the effect of health on wages for males, while overestimate it for females, and simply addressing the endogeneity of health using instrumental variables could overestimate the effect for both genders. It is also found that the effects of health on wages fall under depressed macroeconomic conditions, perhaps due to reduced job mobility and increased presentism during a recession.

This study adds to the international literature on the effects of health on wages by providing empirical evidence from Australia. The model applied to estimate the effects takes advantage of a panel dataset to address the bias resulting potentially from all the sources of the endogeneity of health, unobserved heterogeneity and sample selection. The results indeed show that failing to address these issues would substantially bias the estimated effects of health on wages.

Entrepreneurial learning through formal growth-oriented training programs for SME managers promises to enhance the growth competences and growth intentions of the enrolled managers. The impact of such programs, however, depends on who enrolls since initial competence and growth-intention levels vary significantly. Potential participants may suffer from limited ability to transform new knowledge into practice, absence of growth intention and too high or too low a prior competence level to be able to benefit substantially. Selection and self-selection processes therefore have a bearing on the extent to which such programs result in additionality, i.e. improved growth performance compared to non-intervention. The paper aims to discuss these issues.

Selection and self-selection processes are explored through a study of a large-scale training program for growth-oriented managers of small Danish firms. This program has, from 2012 to 2015, trained about 700 SME managers. Data are currently available for 366 of these participants. This evidence is compared with survey results from a randomly selected control group of 292 growth-oriented SME managers in the same firm-size group. The data were analyzed through descriptive statistics and logistic regression analysis.

A number of selection and self-selection biases were identified in the analysis. While some of the identified biases did not seem to conflict with the ambitions of this growth program, others potentially have consequences for the additionality of the program.

The paper is the first systematic study of the importance of who enrolls in training programs for SME managers.

The purpose of this study was to measure the bias on a binary option's effect estimate that appeared in the types of questions asked and in the placement changes of public service users.

The author designed Monte Carlo simulations with the analytical strategy of latent trait theory leveraging a probability of care-placement change. The author used difference-in-difference (DID) method to estimate the effects of care settings.

The author explained the extent of discrepancy between the estimates and the true values of care service effects in changes across time. The time trend of in-home care for the combined effect of in-home care, general maturity, and other environmental factors was estimated in a biased manner, while the bias for the estimate of the incremental effect for foster care could be negligible.

This study was designed based on individual child-unit only. Therefore, higher-level units, such as care setting or cluster, county, and state, should be considered for the simulation model.

This study contributed to illuminating an overlooked facet in causal inferences that embrace disproportionate selection biases that appear in categorical data scales in public management research.

To model the nuance of a disproportionate self-selection problem, the author constructed a scenario surrounding a caseworker's judgment of care placement in the child welfare system and investigated potential bias of the caseworker's discretion. The unfolding model has not been widely used in public management research, but it can be usefully leveraged for the estimation of a decision probability.

Online review bias research has predominantly focused on self-selection biases on the user’s side. By collecting online reviews from multiple platforms and examining their biases in the unique digital environment of “Chinanet,” this paper aims to shed new light on the multiple sources of biases embedded in online reviews and potential interactions among users, technical platforms and the broader social–cultural norms.

In the first study, online restaurant reviews were collected from Dianping.com, one of China's largest review platforms. Their distribution and underlying biases were examined via comparisons with offline reviews collected from on-site surveys. In the second study, user and platform ratings were collected from three additional major online review platforms – Koubei, Meituan and Ele.me – and compared for possible indications of biases in platform's review aggregation.

The results revealed a distinct exponential-curved distribution of Chinese users’ online reviews, suggesting a deviation from previous findings based on Western user data. The lack of online “moaning” on Chinese review platforms points to the social–cultural complexity of Chinese consumer behavior and online environment that goes beyond self-selection at the individual user level. The results also documented a prevalent usage of customized aggregation methods by review service providers in China, implicating an additional layer of biases introduced by technical platforms.

Using an online–offline design and multi-platform data sets, this paper elucidates online review biases among Chinese users, the world's largest and understudied (in terms of review biases) online user group. The results provide insights into the unique social–cultural cyber norm in China's digital environment and bring to light the multilayered nature of online review biases at the intersection of users, platforms and culture.

The purpose of this paper is to determine the primary basis upon which raters make decisions in the context of selection for formal leadership positions. Specifically, this paper examines the applicant’s personality, the rater’s personality, and the congruence between the applicant’s personality and the rater’s implicit leadership theories (ILTs) as predictors of interview scores.

The hypotheses were tested via random coefficient modeling analyses using HLM software with the control variables included in Step 1 and the main effects entered in Step 2, and interaction effects in Step 3 as appropriate.

Analyses suggest that both applicant and rater personality impact interview scores, but raters do not appear to select leaders on the basis of their conceptualization of an ideal leader.

The results suggest that raters may not consider their own ILTs when attempting to identify future leaders. Given this lack of a natural tendency toward selecting individuals that match one’s perceptions of an ideal leader, future research should focus on adapting current selection methods to leader selection and the development of new selection methods that are more valid.

These findings suggest that current staffing practices may not encompass the most effective tools for selecting future leaders of the organization. These results highlight the importance of clarifying the outcome goals of the selection process in advance by giving raters a clear representation of the qualities and ideals that should be present in potential leaders.

This study is among the first to examine the relationships between personality and ILTs in the context of a formal leadership selection process and makes a significant contribution to the literature by providing insight into the influence of both rater and applicant personality differences along with rater conceptualizations of ideal leadership in the context of formal leadership selection.

The purpose of this paper is to examine if and how entrepreneurial assistance programs, through guided preparation, affect start-up success.

–This study uses Heckman's two-stage sample selection model to predict the effect of contact and interactions with entrepreneurial support programs on start-up outcomes while taking into account the entrepreneur's self-selection into obtaining support from these programs.

The results indicate that, after controlling for individual characteristics, activities undertaken during the start-up process, organizational characteristics and external factors, guided preparation contributes to a greater likelihood of achieving positive start-up outcome. This finding holds even after controlling for the entrepreneur's self-selection into contacting and using outside assistance.

Results suggest that self-selection bias remains a concern when studying the impact of assistance programs on start-up outcomes. Future research should make sure to address self-selection in their analysis.

The study's results have implications for the design of start-up programs. It highlights the importance of delivery structures that are fluid, flexible, interactive, experiential, and tailored to the individual entrepreneur's needs.

This study focusses on assistance programs broadly defined (includes many different types of programs) and provides an empirical analysis that addresses self-selection.

The purpose of this paper is to explore how hedge fund database biases developed during the 2007‐2009 financial crisis.

The sample consists of 8,935 hedge funds from the Lipper TASS Hedge Fund Database for the January 2002‐September 2010 time period. The theoretical foundation of this paper draws from Fung and Hsieh who argue that time series of funds of hedge funds should be less prone to some of the documented database biases. The paper uses a sampling technique to create hedge fund portfolios, and then compares them using fund of fund data.

The paper finds empirical evidence that fund of hedge fund data is less biased than single hedge fund data, and that the impact of the survivorship and backfilling biases has increased since the financial crisis. It also finds that the attrition rate for hedge funds has nearly doubled since the financial crisis, and that an elevated attrition rate has a negative impact on the quality and representativeness of hedge fund data due to the liquidation bias. The liquidation bias increased strongly in the aftermath of the financial crisis. It also fluctuates over time, and it can account for an overestimate of performance of over 10 percent p.a.

Given this increase and the volatile nature of hedge fund biases, we believe investors (for benchmarking) and academics (for empirical studies) should consider refraining from using single hedge fund index data.

The purpose of this study is to investigate the presence of ethnic bias in residential tenancy and the relationship between ethnic bias tendency of property managers/landlords and their demographic features.

Data for the study were gathered from administration of questionnaires, designed with items measuring bias tendency and the driving factors on the perceived wave of discrimination on residential tenancy. The structured questionnaires were administered on an intact sample of licensed property managers/agents (practicing Estate Surveyors and Valuers) at annual Mandatory Continuing Professional Development Programme (MCPD) held in metropolitan Ibadan, the largest indigenous city in West Africa and one of the commercial nerve‐centres in the country. Data collected were analyzed using descriptive analysis, chi square and factor analysis.

The study confirmed a significant relationship between the ethnic status of the principal managers (managing directors) who were mainly Yoruba aliens and the tenants selected to fill vacancies, which implies that there is presence of ethnic discrimination in tenant selection in the area. It is also confirmed that landlord/property manager (agents) experience, environmental influence and landlord/property manager preference are the most driving factors for discriminating against tenants in the city. It is further revealed that there is a significant relationship between two demographic features; professional qualifications and ages of the property managers’ firms and bias tendency.

The research is limited to the metropolitan city of Ibadan, a city with expatriates and diverse ethnic groups working at different sectors of the economy. Further research and statistical tests that covers all the 36 capital cities of the country are required to examine the tenancy nature of other classes of property.

The implication of the study to the practice is reinforced by the consensus character of the study with the professional body (property managers). Insight and findings prove useful in developing a blueprint for curbing acts of discrimination which needs to be well addressed in property management practice. It implies that effective property rights that protect the rights and rental market planning policy in the developing nations require further reform.

Emphasis on tenants’ rights, campaign against racism and discrimination in the developing nations which support equal housing right for all races is the feature and uniqueness of this study.

Internet data hold many promises for demographic research, but come with severe drawbacks due to several types of bias. The purpose of this paper is to review the literature that uses internet data for demographic studies and presents a general framework for addressing the problem of selection bias in non-representative samples.

The authors propose two main approaches to reduce bias. When ground truth data are available, the authors suggest a method that relies on calibration of the online data against reliable official statistics. When no ground truth data are available, the authors propose a difference in differences approach to evaluate relative trends.

The authors offer a generalization of existing techniques. Although there is not a definite answer to the question of whether statistical inference can be made from non-representative samples, the authors show that, when certain assumptions are met, the authors can extract signal from noisy and biased data.

The methods are sensitive to a number of assumptions. These include some regularities in the way the bias changes across different locations, different demographic groups and between time steps. The assumptions that we discuss might not always hold. In particular, the scenario where bias varies in an unpredictable manner and, at the same time, there is no “ground truth” available to continuously calibrate the model, remains challenging and beyond the scope of this paper.

The paper combines a critical review of existing substantive and methodological literature with a generalization of prior techniques. It intends to provide a fresh perspective on the issue and to stimulate the methodological discussion among social scientists.

This study aims to address two research questions. First, do the agricultural extension services have an impact on the potential outcomes considered in the primary studies, and to what extent? Second, how sensitive is the reported impact to the study-specific characteristics in the primary studies?

The paper synthesizes 45 studies that assessed the causal impact of agricultural extension services published in 2004–2021, using meta-regression analysis. It considers three measures of effect sizes – Cohen’s, Hedges and principal correlation coefficient (PCC) – to standardize the reported impact of agricultural extension services in the primary studies.

The empirical results show that, on average, agricultural extension services have statistically significant and positive impacts on the potential outcomes identified in the primary studies. However, the magnitude of the impact is considered medium-sized. Other results show that the effect size estimates of agricultural extension services' impact significantly vary with the data type (cross-sectional data vs. panel data), research design (non-experimental vs. experimental design) and econometric methods employed in the primary studies.

One can argue that the medium-sized impact we estimated indicates evidence of a moderate, weak relationship between agricultural extension services and the potential outcomes considered in the primary studies. This means that agricultural extension services need to be restructured in the current form to stimulate change in the agricultural sector globally. In addition, the sensitivity of effect sizes to study attributes (i.e. data types, research design and econometric methods) shows that researchers and academicians need to pay attention to these attributes to provide more reliable estimates for policy purposes.

This is the first study that attempts to shed light on the overall performance of agricultural extension services using a meta-regression analysis approach.