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In this chapter, we explicate two related techniques that help quantify the sensitivity of a given causal inference to potential omitted variables and/or other sources of unexplained heterogeneity. In particular, we describe the Impact Threshold of a Confounding Variable (ITCV) and the Robustness of Inference to Replacement (RIR). The ITCV describes the minimum correlation necessary between an omitted variable and the focal parameters of a study to have created a spurious or invalid statistical inference. The RIR is a technique that quantifies the percentage of observations with nonzero effects in a sample that would need to be replaced with zero effects in order to overturn a given causal inference at any desired threshold. The RIR also measures the percentage of a given parameter estimate that would need to be biased in order to overturn an inference. Each of these procedures is critical to help establish causal inference, perhaps especially for research urgently studying the COVID-19 pandemic when scholars are not afforded the luxury of extended time periods to determine precise magnitudes of relationships between variables. Over the course of this chapter, we define each technique, illustrate how they are applied in the context of seminal strategic management research, offer guidelines for interpreting corresponding results, and delineate further considerations.

While consumers did not previously have information on detailed housing features via traditional media, such as magazines, nowadays, due to the progress in information technology, they can access detailed information on various housing features via housing information websites. Therefore, detailed housing features may affect current rents to some extent. This paper aims to identify the effects of detailed housing features on rent and on omitted variable bias in Tokyo, Japan.

This paper applies the hedonic approach. To identify the effects of features which are not observed previously, we use a unique data set that contains various housing features and over 200,000 housing units. This data set enables to simulate the situations when the researcher cannot get some variables, and this simulation shows which variables cause omitted variable bias.

The analysis shows that housing features significantly influence housing rent. If significant housing feature variables are not included in the hedonic model, the estimated coefficients show omitted variable bias. Additionally, unit-specific features such auto-locking door can cause omitted variable bias on location-specific features such accessibility to downtown.

This paper shows empirical evidence that detailed housing features can cause omitted variable bias on other features including variables which are often used in previous searches. The result from our unique data set can be a guide for variable selection to reduce omitted variable bias.

Endogeneity is a potential threat to the validity of international marketing (IM) research. The purpose of this paper is to draw the attention of IM researchers to issues of endogeneity, to provide a comprehensive overview of the sources of endogeneity, and to discuss the statistical solutions.

The authors conduct the research in two steps. In the first step, the authors review the nature and sources of endogeneity specifically in IM research. In the second step, the authors review 60 IM papers on endogeneity published in the period 1995-2014 and assess the current practice of addressing endogeneity in the IM literature.

Sample selection bias and simultaneity are prevalent sources of endogeneity in IM research. Internationalization-performance relationship and innovation-export nexus are the two most frequently adopted models subject to potential endogeneity. Simply lagging the main independent variable is statistically flawed in dealing with endogeneity despite its popularity in IM research.

First, a careful choice and application of methods are critical when addressing endogeneity. Second, the authors suggest the employment of multiple study methods to address endogeneity robustly. Third, to prevent or solve endogeneity in structural equation modeling, researchers may either collect data on independent and dependent variables from different respondents or employ a two-stage least squares approach. Finally, it is helpful to design dedicated models to prevent proactively potential endogeneity a priori.

The contribution of this study is twofold. First, it is the first in the literature to discuss the endogeneity issue specifically in IM research. In particular, the study elaborates the origins and consequences of the three most frequently confronted types of endogeneity in IM research. Second, the authors assess the four major methods of addressing endogeneity in IM research with a systematic discussion of the literature from the last two decades. The authors offer suggestions on how to minimize endogeneity in model design and empirical implementation for future IM research.

This chapter investigates which factors contribute to (small) shareholder attendance using a hand-collected panel data set with information about turnout rates, voting behaviour and ownership structures of companies that are listed in seven Member States. We document how ownership concentration positively affects total shareholder turnout, but has a negative effect on small shareholder turnout. Voting power also affects small shareholder turnout rates; the greater small shareholder voting power, the greater their eagerness to vote. In addition, total and small shareholder turnout is higher the more important the meeting agenda. And, small shareholders tend to free-ride on large institutional shareholders and corporate insiders, but the magnitude of the free-rider effect is larger for the latter category of blockholders. Our results provide some important insights for the debate on shareholder rights and the role of the AGM in corporate governance. The results show that, despite the criticism, the AGM still plays an important role in small shareholder monitoring. Some topics seem to clearly motivate small shareholders to attend, while others are less relevant. Policy makers can stimulate shareholder monitoring by focusing on the factors that are determined in this study, but it is important to consider possible endogeneity issues as well.

This paper aims to foster a new discussion on endogeneity in hospitality and tourism research.

This paper elaborates on some of the common sources of endogeneity and the methods available to address them.

The authors present a variety of methods that can be used to mitigate the endogeneity problem. The authors provide simulation evidence regarding the risk of incorrectly selecting instrumental variables. The authors also provide several important practical recommendations for future research.

There are other issues and methods of correcting for endogeneity, that is not covered in this paper. However, the paper focuses on issues and methods that can be generalized to most contexts.

The paper provides practical recommendations for more rigorous regression estimation.

This study investigates the asymmetric effects of exchange rate and investors' sentiments simultaneously on stock market performance in the United States context. In addition, we have also considered the potential effect of the global financial crisis of 2008 on this nexus.

We have employed the NARDL (nonlinear autoregressive distributed lag) model on monthly data ranging from January-1999 to December-2018 to investigate the asymmetric (short- and long-run) effects of exchange rate and investors' sentiments on stock market performance. We have also broken down the data into two segments, pre and post-crisis periods to capture the effect of the global financial crisis of 2008.

The findings of the study reveal that exchange rate and investors' sentiments simultaneously affect stock market performance and omitting any of these variables can produce misleading results. Results also show that the effect of sentiments is stronger than the exchange rate. There is significant evidence of asymmetric short-run and long-run effects of both explanatory variables. Moreover, we have found different outcomes for pre and post-crisis periods. Specifically, the impact of macroeconomic variables on the stock market has been substantiated in the post-crisis period.

Several studies are available which separately evidence the effects of investors' sentiments and exchange rate on performance of the stock market but they can suffer from the problem of omitted variable bias. This study is conducted to test the said effect simultaneously in a single model. Moreover, this study is considering short-run and long-run asymmetry in analyzing the effects of explanatory variables along with the inclusion of the global financial crisis of 2008.

International organizations pursue multiple objectives in hiring policies including cultural diversity, reducing costs and avoiding discrimination among which there can be sharp trade‐offs. The paper has the purpose of studying how these trade‐offs are resolved in the World Bank's hiring processes.

The paper estimates that half of salary and grade differentials between men and women and staff from high‐ and low‐income countries are attributable to differences in productive characteristics. Alternative explanations for the remainder are explored, including omitted variable bias, quotas and discrimination.

The paper argues that the salary and grade differentials and differences in productive characteristics are not compelling explanations. Discrimination probably exists, though less than would be implied by a cost minimizing hiring policy.

Provides a discussion of the World Bank's hiring processes.

The aim of this paper is to develop a new analysis method, named dynamic extreme bounds analysis (DEBA), and to determine decisive determinants of foreign direct investment (FDI) by using this new method.

In econometrics, the extreme bounds analysis (EBA) method is a convincing way of examining the strength of independent variables. However, the results obtained when using the EBA method contain little information, since each variable is only either strong or fragile, and some strong variables may be omitted because their significance could be undermined by just one unreasonable regression. Therefore, in order to overcome these limitations, this paper proposes DEBA, a new analysis method.

The authors employ the DEBA method to determine the factors which impact FDI in 86 countries. The authors note that in developing countries, the level of previous FDI, a high degree of openness, large market size and development of infrastructure help to attract FDI, whereas the development of domestic industry deters it. In developed countries, FDI is lured by the level of previous FDI stock, a high degree of openness, large market size, macroeconomic instability and availability of energy.

Although this study is expected to contribute a new methodological approach and define the strong determinants of FDI, the study is not without limitations, such as the unavailability of data. Further studies should improve the DEBA method by developing DEBA packages for use in popular statistical software, enhancing methods for other types of data and more accurately determining the estimation order of variables. In addition, further research should expand the study's FDI model, providing more potential variables for an in-depth overview of this model.

This study is to contribute a new methodological approach (DEBA method) for data analysis and defining of strong determinants of FDI. The study findings are useful for governments, policy-makers and economists in formulating more attractive FDI policies.

The purpose of this study is to examine the impact of crime on apartment prices for Hamburg, Germany, for the years 2012 to 2017.

The authors use a panel data setting with fixed effects estimators and temporal lags to moderate the endogeneity concerns related to crime. The authors consider the effect of total crime, violent and property crime and some sub-categories of crime.

The estimates show that it takes two to three years for prices to react, with the longer run elasticity reaching −0.12 for total crime, −0.15 for property crime and −0.06 for violent crime. The elasticities are much larger in high-crime areas (−0.22 for total crime, −0.28 and −0.09 for property and violent crime) and elevated also in low-income areas.

The finding that property crime matters more in terms of quantitative impact for housing values than violent crime provides reasonable grounds for rethinking the resource allocation of public spending on crime clearance and prevention in Germany. Far more emphasis on preventing property crime appears in order and especially so in the lower income or higher crime areas, which are significantly more affected by crime and in particular property crime than those in high income or low crime areas.

The estimates for Hamburg provide the first detailed results of the impact of crime on real estate prices in Germany. It is also the first study for Continental Europe using panel data.