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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.

This study investigates whether or not accounting and legal decision-makers at publicly traded US firms exhibit a professional affiliation bias with respect to their selection of business service providers. Executives at NYSE or NASDAQ firms who were affiliated with the accounting profession, the legal profession, or neither profession indicated their likelihood of using one of three randomly assigned types of firms (i.e., a CPA firm, a law firm, or a firm with both CPA and attorney partners) to provide five selected business services. The five business services represent the range of accounting and legal services that firms often outsource: audit, tax representation, mergers and acquisitions, trade regulation/interstate commerce, and litigation. We find that executive level decision-makers at publicly traded US firms do exhibit a professional affiliation bias in the selection of business service providers and that this professional affiliation bias is stronger in attorneys than in CPAs. The fact that all respondents were NYSE or NASDAQ executives, rather than students or another surrogate population, provides additional relevance and generalizability to our findings. Identifying this bias can help executives avoid suboptimal initial selection decisions and/or inaccurate performance evaluations of external business service providers.

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.

Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect conclusions. However, these issues are not addressed adequately and properly in higher education research. This study reviews the essence of self-selection bias, treatment assignment endogeneity, and treatment effect estimation. We introduce three treatment effect estimators – propensity score matching analysis, doubly robust estimation (augmented inverse probability weighted approach), and endogenous treatment estimator (control-function approach) – and examine literature that applies these methods to research in higher education. We then use the three methods in a case study that estimates the effects of transfer student pre-enrollment debt on persistence and first year grades. The final discussion provides guidelines and recommendations for causal inference research studies that use such quasi-experimental methods.

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.

Despite an increase in research that examines the media's selection of protest events for coverage, two areas of study have been left undeveloped. First, the type of protest examined is limited to common forms of the demonstration (march, vigil, rally). A second drawback of this literature is its focus on mass audience newspapers. The goal of the current study is to address these two issues by comparing coverage of a previously ignored form of protest, the strike, across two different media sources, the mass audience New York Times and the Daily Labor Report, a newspaper which targets industry and labor leaders and garners its revenue from subscriptions, not advertising. Due to specific differences between the two newspapers (primarily readership and revenue base), it is expected that certain strike characteristics (industry) will play a greater role in the New York Times’ selection of strikes than in the Daily Labor Report. Using government data to construct the population of events, I find that both newspapers select strikes in a manner that resembles coverage of other forms of protest. Important variables include size, length, and disruptiveness. The main difference between the two newspapers is the New York Time's attention to strikes in industries that affect the public and consumers and its strong regional bias. These findings indicate that not only do similar media selection processes work for both protest and strikes, but also that, despite some differences, media type did not affect selection greatly.

We argue that resource-based view (RBV) researchers must take into account three interdependencies, (i) intrafirm resource complementarity, (ii) interfirm resource complementarity or rivalry, and (iii) compatibility or incompatibility of firm resources to broader socio-economic institutions, when attempting to empirically verify the RBV. However, these interdependencies lead to three potential causes of statistical bias, which can reduce the interpretability of such empirical examinations. First, omitted variable bias results from a researcher's inability to find and include in empirical analyses appropriate operationalizations of constructs. Second, selection bias can arise when a researcher samples only from one subset of the population, and not others. Bias in estimates can occur if a correlation between unobserved determinants of the outcome and factors affecting the selection process exist. Finally, joint dependence, where two explanatory variables are themselves mutual determinants, can lead to biased estimation.

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.