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The purpose of this paper is to examine the factors affecting the academic outcome of real estate students in a specialized Federal University in Nigeria. Furthermore, this paper investigates the phenomenon of publication bias in the extant literature as such evidence poses severe threats to the validity of empirical findings on factors affecting the degree outcome of undergraduate students.

The standard statistical approach adopted was to examine whether the reported coefficient estimates from ten empirical studies (105 observations) are independent of their standard errors by employing both ordinary least squares (OLS) and weighted least squares (WLS). In this paper, this approach enabled evidence of publication bias in the cited literature to be refuted. In addition, data were also collected on the academic measure and demographic information of 449 students who graduated between 2005 and 2011. For the purpose of analysis, the study utilized a stepwise logistic regression technique to examine the factors impacting on the degree outcome of real estate students.

The results of the OLS and WLS regression indicate that there is no significant evidence of any empirical effect of publication bias in the extant literature. The results of the logistic regression also revealed that grade point average, gender differences, prior knowledge of real estate discipline and potential difference in year of enrollment impact on students’ academic performance in terms of their ability to graduate at first attempt. In addition, factors such as age, marital status, high school grade and geopolitical/ethnic background of undergraduate real estate students do not influence their opportunities to graduate at first attempt from the university.

This paper focuses only on one specialized university of technology offering a bachelor’s program in real estate in Nigeria, so as to remove any extraneous factor(s) that could be present in the other institutional settings where students have completed such program. Extending similar study to tertiary institutions in Nigeria that share similar geographical characteristics and institutional settings can produce far-reaching generalization.

This paper contributes to the scanty literature on factors affecting the academic performance of students in an undergraduate real estate program in Nigeria. A scientific element of novelty in this paper is the evidence of the absence of the underlying effect of publication bias in the extant literature on students’ academic outcome in tertiary institutions. Findings from this study serve as the basis for university officers to monitor significant transitions in real estate students’ academic progress, so as to identify those who are unlikely to graduate at first attempt early at the entrant level. Generally, the outcome of this research could provide faculty and admission officers in tertiary institutions with complementary information in arriving at an informed decision in a non-discriminatory admission process.

The purpose of this paper is to investigate the relationship between a company’s environmental, social and governance (ESG) performance and its financial performance. This paper also investigates the relationship between ESG performance and a company’s market valuation. This paper provides convincing empirical evidence that delivering superior ESG performance pays off financially.

The financial data and ESG scores of 150 publicly traded companies listed in the Standard and Poor’s 500 index for 2017–2020, comprising 5,750 observations, were collected. STATA was used to run a fixed-effect regression and a weighted least squares model to analyze the panel data.

The results of the empirical analysis suggest that companies with superior ESG performance perform better financially and are valued higher in the market compared to their industry peers. The ESG rating score impacts both return-on-capital-employed as a proxy for financial performance and Tobin’s Q as a proxy for the market valuation of a company.

This study contributes to the existing research on ESG performance and financial performance relationship by providing empirical evidence to resolve confusion in the existing literature caused by contradictory evidence. Taking advantage of worldwide crisis caused by the COVID-19 pandemic, this study shows that a positive relationship between ESG performance and a company’s market valuation holds even during times of unexpected crises. Further, this study contributes to business practitioners’ knowledge by showing that ESG aspects constitute highly relevant non-financial information that impact the market’s perception of a company and that investing in sustainability positively impacts a company’s bottom line.

This study aims to examine the information transfer effects of customers’ credit rating downgrades on supplier firms.

In this study, the authors use suppliers’ cumulative abnormal returns around customers’ credit rating downgrade events to identify how shocks to customer credit impact supplier equity prices. The authors also incorporate ordinary least squares and weighted least squares regressions regression analysis of the determinants of supplier market response to customer downgrades.

The authors find that customer credit rating downgrades present significant negative shocks to the stock prices of supplier firms. Moreover, the authors show that the information transfer effects are determined by both firm- and industry-level factors, including the market anticipation of downgrades, the strength of the customer–supplier linkage, the industry rivals’ reactions to the downgrades and investor attention. The authors also find that the likelihood that a supplier will receive a rating downgrade is significantly higher following its primary customer firm’s downgrade.

To the best of the authors’ knowledge, this paper is the first to explore the information transfer effects of credit rating downgrades on primary stakeholders within the supply chain. The authors document that customer–supplier networks have valuable implications for the spillover effect across debt and equity holders. Information about customers’ financial stress is incorporated into suppliers’ equity prices outside of the context of customer bankruptcy.

The lack of transaction data has been identified as one of the major obstacles for the empirical evaluation of real option. Quigg’s study in 1993 was one of the first to empirically estimate the premium for the option of waiting to develop using data from 2,700 land sales in Seattle. This study modified Quigg’s methodology and applied it to estimate the premium for the option of waiting to develop based on a sample of data from 2,286 property transactions in the UK collected over a 14‐year sample period from 1984 to 1997. Based on a one‐factor contingent claim valuation model, we found that the average premiums for the timing options were 28.78 percent for office sector, 25.75 percent for industrial sector and 16.06 percent for retail sector. We also tested the robustness of the theoretical‐based land value estimates in explaining the market‐based land values. The regression results showed a statistically significant relationship in logarithm form between the market‐based residual land value and the model‐based land values (with embedded timing option), with R² of 0.75, 0.79 and 0.82 for office, industrial and the retail sectors respectively.

This study empirically documents the effect of ambiguity on stock returns in a major emerging market along with the ambiguity attitudes under various market conditions.

Ambiguity is measured as the volatility of return probability distributions extracted from high frequency intraday data via a method developed by Brenner and Izhakian (2018). The impact of ambiguity is then tested on stock market returns.

The results show that ambiguity is a priced factor in Turkish stock market with a positive premium that is distinct from risk premium. In contrast with the findings in the US market, the investors in Turkey show an increasing level of ambiguity aversion as expected probability of favorable returns deviate from the mean value. The investors are effectively ambiguity neutral in lateral markets. The results are robust to testing with higher moments, sentiment measures and under recession conditions.

This study contributes to empirically documenting ambiguity and ambiguity aversion in a major emerging market along with the opportunity to observe international differences in ambiguity attitudes.

In the finance literature, fitting a cross-sectional regression with (estimated) abnormal returns as the dependent variable and firm-specific variables (e.g. financial ratios) as independent variables has become de rigueur for a publishable event study. In the absence of skewness and/or kurtosis the explanatory variable, the regression design does not exhibit leverage – an issue that has been addressed in the econometrics literature on the finite sample properties of heteroskedastic-consistent (HC) standard errors, but not in the finance literature on event studies. The paper aims to discuss this issue.

In this paper, simulations are designed to evaluate the potential bias in the standard error of the regression coefficient when the regression design includes “points of high leverage” (Chesher and Jewitt, 1987) and heteroskedasticity. The empirical distributions of test statistics are tabulated from ordinary least squares, weighted least squares, and HC standard errors.

None of the test statistics examined in these simulations are uniformly robust with regard to conditional heteroskedasticity when the regression includes “points of high leverage.” In some cases the bias can be quite large: an empirical rejection rate as high as 25 percent for a 5 percent nominal significance level. Further, the bias in OLS HC standard errors may be attenuated but not fully corrected with a “wild bootstrap.”

If the researcher suspects an event-induced increase in return variances, tests for conditional heteroskedasticity should be conducted and the regressor matrix should be evaluated for observations that exhibit a high degree of leverage.

This paper is a modest step toward filling a gap on the finite sample properties of HC standard errors in the event methodology literature.

Telework is an alternative work relationship with demonstrated positive benefits for individuals and society, yet it has not been implemented with enthusiasm by most organizations. This could be due to the lacking, consolidated evidence for management regarding whether or not telework is a good thing for the firm. The purpose of this paper is to integrate multidisciplinary literature that reports effects of telework on organizational outcomes with the aim of providing a clearer answer to the question: is telework effective for organizations?

Meta‐analytical methods were used, beginning with an interdisciplinary search for effect sizes in eight databases. Limited to scholarly journals and dissertations, results included 991 articles scanned for inclusion criteria. The independent variable is telework, measured as a dichotomous variable. Dependent variables are outcomes of interest to organizations: productivity, retention, turnover intention, commitment, and performance. In total, 22 studies were double coded and meta‐analyzed using Hunter and Schmidt's approach, followed by five exploratory moderator analyses: level of analysis, level of the employee, response rate, proportion of females, and country of the study. Significant results are discussed.

Review and meta analysis of 32 correlations from empirical studies find that there is a small but positive relationship between telework and organizational outcomes. Telework is perceived to increase productivity, secure retention, strengthen organizational commitment, and to improve performance within the organization. In other words, it is indeed beneficial for organizations. All five hypotheses are supported. H1 (productivity), rc=0.23 (k=5, n=620), (95% CI=0.13−0.33). H2 (retention), r=0.10 (k=6, n=1652), (95% CI=0.04−0.16). H3 (commitment), r=0.11 (k=8, n=3144), (95% CI=0.03−0.18); moderator analysis shows sample age is significant (F(1,4)=4.715, p<0.05, R2=0.80). H4 (performance), r=0.16 (k=10, n=2522). H5 (organizational outcomes), r=0.17 (k=19, n=5502), (95% CI=0.1−0.20).

To the authors' knowledge, this is the first meta‐analysis of telework research at the organizational level, providing a unique contribution to the field in filling the gap between research on effects to the individual and society. Additional contributions resulted from the moderator analyses: first, in finding that the relationship between telework and performance is moderated by whether or not the sample was one individual per firm, or many individuals from one; and second, in finding that the relationship between telework and organizational commitment is moderated by age. Thus, the paper provides unique contributions with both scholarly and practical implications.

The purpose of this paper is to investigate convergence or divergence trends at global scale.

The paper questions the methodology and findings of the conventional convergence literature using linear OLS models. It introduces polynomial (quadratic) weighted least square (WLS) regression analysis to explore whether a number of economic performance indicators follow a non‐linear pattern of change.

The results indicate the formation of two groups in the world: a convergence one, including countries with low to medium‐high development levels, and a divergence one including countries with medium‐high to very high development levels.

Data availability after 1990 (for the composite indicators).

The findings shed light on important issues, such as the decrease of economic disparities between countries, the prospects for global economic convergence, and the development of a more equal world. Apart from obvious policy implication such findings are also of theoretical significance, providing a basis to check (indirectly) the validity of alternative growth theories.

This is the first paper (to the authors' knowledge) that explores world convergence/divergence employing quadratic WLS regression analysis with a number of economic indicators. WLS regressions enable the removal of the impact of country size on results, whereas non‐linear modelling allows the possibility of multiple equilibria and different development trajectories to be taken into account. Finally, the employment of various economic‐performance indicators (simple and composite) works as a cross‐check of validity for the results provided.

Using a meta-regression analysis, we quantitatively review the empirical literature on the relation between effective tax rate (ETR) and firm size. Accounting literature offers two competing theories on this relation: The political cost theory, suggesting a positive size-ETR relation, and the political power theory, suggesting a negative size-ETR relation. Using a unique data set of 56 studies that do not show a clear tendency towards either of the two theories, we contribute to the discussion on the size-ETR relation in three ways: First, applying meta-regression analysis on a US meta-data set, we provide evidence supporting the political cost theory. Second, our analysis reveals factors that are possible sources of variation and bias in previous empirical studies; these findings can improve future empirical and analytical models. Third, we extend our analysis to a cross-country meta-data set; this extension enables us to investigate explanations for the two competing theories in more detail. We find that Hofstede’s cultural dimensions theory, a transparency index and a corruption index explain variation in the size-ETR relation. Independent of the two theories, we also find that tax planning aspects potentially affect the size-ETR relation. To our knowledge, these explanations have not yet been investigated in our research context.

Applied econometric analysis is often performed using data collected from large-scale surveys. These surveys use complex sampling plans in order to reduce costs and increase the estimation efficiency for subgroups of the population. These sampling plans result in unequal inclusion probabilities across units in the population. The purpose of this paper is to derive the asymptotic properties of a design-based nonparametric regression estimator under a combined inference framework. The nonparametric regression estimator considered is the local constant estimator. This work contributes to the literature in two ways. First, it derives the asymptotic properties for the multivariate mixed-data case, including the asymptotic normality of the estimator. Second, I use least squares cross-validation for selecting the bandwidths for both continuous and discrete variables. I run Monte Carlo simulations designed to assess the finite-sample performance of the design-based local constant estimator versus the traditional local constant estimator for three sampling methods, namely, simple random sampling, exogenous stratification and endogenous stratification. Simulation results show that the estimator is consistent and that efficiency gains can be achieved by weighting observations by the inverse of their inclusion probabilities if the sampling is endogenous.