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Book part
Publication date: 15 January 2010

Cristian Angelo Guevara and Moshe Ben-Akiva

Endogeneity or nonorthogonality in discrete choice models occurs when the systematic part of the utility is correlated with the error term. Under this misspecification…

Abstract

Endogeneity or nonorthogonality in discrete choice models occurs when the systematic part of the utility is correlated with the error term. Under this misspecification, the model's estimators are inconsistent. When endogeneity occurs at the level of each observation, the principal technique used to treat for it is the control-function method, where a function that accounts for the endogenous part of the error term is constructed and is then included as an additional variable in the choice model. Alternatively, the latent-variable method can also address endogeneity. In this case, the omitted quality attribute is considered as a latent variable and modeled as a function of observed variables and/or measured through indicators. The link between the controlfunction and the latent-variable methods in the correction for endogeneity has not been established in previous work. This paper analyzes the similarities and differences between a set of variations of both methods, establishes the formal link between them in the correction for endogeneity, and illustrates their properties using a Monte Carlo experiment. The paper concludes with suggestions for future lines of research in this area.

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Choice Modelling: The State-of-the-art and The State-of-practice
Type: Book
ISBN: 978-1-84950-773-8

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Article
Publication date: 9 May 2016

Ruey-Jer "Bryan" Jean, Ziliang Deng, Daekwan Kim and Xiaohui Yuan

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…

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Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Research limitations/implications

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.

Originality/value

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.

Details

International Marketing Review, vol. 33 no. 3
Type: Research Article
ISSN: 0265-1335

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Article
Publication date: 11 November 2014

Rick L. Andrews and Peter Ebbes

This paper aims to investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models. Endogeneity problems in demand models…

Abstract

Purpose

This paper aims to investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models. Endogeneity problems in demand models occur when certain factors, unobserved by the researcher, affect both demand and the values of a marketing mix variable set by managers. For example, unobserved factors such as style, prestige or reputation might result in higher prices for a product and higher demand for that product. If not addressed properly, endogeneity can bias the elasticities of the endogenous variable and subsequent optimization of the marketing mix. In practice, instrumental variables (IV) estimation techniques are often used to remedy an endogeneity problem. It is well-known that, for linear regression models, the use of IV techniques with poor-quality instruments can produce very poor parameter estimates, in some circumstances even worse than those that result from ignoring the endogeneity problem altogether. The literature has not addressed the consequences of using poor-quality instruments to remedy endogeneity problems in non-linear models, such as logit-based demand models.

Design/methodology/approach

Using simulation methods, the authors investigate the effects of using poor-quality instruments to remedy endogeneity in logit-based demand models applied to finite-sample data sets. The results show that, even when the conditions for lack of parameter identification due to poor-quality instruments do not hold exactly, estimates of price elasticities can still be quite poor. That being the case, the authors investigate the relative performance of several non-linear IV estimation procedures utilizing readily available instruments in finite samples.

Findings

The study highlights the attractiveness of the control function approach (Petrin and Train, 2010) and readily available instruments, which together reduce the mean squared elasticity errors substantially for experimental conditions in which the theory-backed instruments are poor in quality. The authors find important effects for sample size, in particular for the number of brands, for which it is shown that endogeneity problems are exacerbated with increases in the number of brands, especially when poor-quality instruments are used. In addition, the number of stores is found to be important for likelihood ratio testing. The results of the simulation are shown to generalize to situations under Nash pricing in oligopolistic markets, to conditions in which cross-sectional preference heterogeneity exists and to nested logit and probit-based demand specifications as well. Based on the results of the simulation, the authors suggest a procedure for managing a potential endogeneity problem in logit-based demand models.

Originality/value

The literature on demand modeling has focused on deriving analytical results on the consequences of using poor-quality instruments to remedy endogeneity problems in linear models. Despite the widespread use of non-linear demand models such as logit, this study is the first to address the consequences of using poor-quality instruments in these models and to make practical recommendations on how to avoid poor outcomes.

Details

Journal of Modelling in Management, vol. 9 no. 3
Type: Research Article
ISSN: 1746-5664

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Article
Publication date: 13 July 2020

A. George Assaf and Mike Tsionas

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

Abstract

Purpose

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

Design/methodology/approach

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

Findings

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.

Research limitations/implications

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.

Originality/value

The paper provides practical recommendations for more rigorous regression estimation.

Details

International Journal of Contemporary Hospitality Management, vol. 32 no. 8
Type: Research Article
ISSN: 0959-6119

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Book part
Publication date: 21 February 2008

Xavier de Luna and Per Johansson

We show that in sorting cross-sectional data, the endogeneity of a variable may be successfully detected by graphically examining the cumulative sum of the recursive…

Abstract

We show that in sorting cross-sectional data, the endogeneity of a variable may be successfully detected by graphically examining the cumulative sum of the recursive residuals. Moreover, the sign of the bias implied by the endogeneity may be deducible through such graphs. In general, instrumental variables are needed to implement the graphical test. However, when a continuous or ordered (e.g. years of schooling) variable is suspected to be endogenous, a graphical test for misspecification due to endogeneity (e.g. self-selection) can be obtained without instrumental variables.

Details

Modelling and Evaluating Treatment Effects in Econometrics
Type: Book
ISBN: 978-0-7623-1380-8

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Article
Publication date: 1 July 1995

Masudul Alam Choudhury

Argues that ethics and values are systemic realities and can be scientifically programmed in cybernetically oriented socio‐scientific systems. The case taken is of…

Abstract

Argues that ethics and values are systemic realities and can be scientifically programmed in cybernetically oriented socio‐scientific systems. The case taken is of economic general equilibrium with possibilities of multiple equilibria. The treatment of ethics and values in this sense in economic theory makes them endogenous phenomena of socio‐economic reality. This substantive idea of ethics and values as endogenous phenomena in socio‐scientific systems is termed the principle of ethical endogeneity. Its social cybernetical possibilities are developed mathematically. While the mathematical treatment uses bilinear algebra for the formulation, greater importance may be seen in the scientific essence of the principle of ethical endogeneity applicable universally. This is particularly true of systems which need to be epistemologically unified.

Details

Kybernetes, vol. 24 no. 5
Type: Research Article
ISSN: 0368-492X

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Book part
Publication date: 19 November 2014

Angela Vossmeyer

An important but often overlooked obstacle in multivariate discrete data models is the specification of endogenous covariates. Endogeneity can be modeled as latent or…

Abstract

An important but often overlooked obstacle in multivariate discrete data models is the specification of endogenous covariates. Endogeneity can be modeled as latent or observed, representing competing hypotheses about the outcomes being considered. However, little attention has been applied to deciphering which specification is best supported by the data. This paper highlights the use of existing Bayesian model comparison techniques to investigate the proper specification for endogenous covariates and to understand the nature of endogeneity. Consideration of both observed and latent modeling approaches is emphasized in two empirical applications. The first application examines linkages for banking contagion and the second application evaluates the impact of education on socioeconomic outcomes.

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Book part
Publication date: 7 October 2019

Xiqian Liu and Victor Borden

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…

Abstract

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.

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Article
Publication date: 1 December 2020

Carolina Pasciaroni and Andrea Barbero

This paper aims to analyse the influence of cooperation on the degree of novelty of technological innovations introduced by industrial firms in Argentina. This influence…

Abstract

Purpose

This paper aims to analyse the influence of cooperation on the degree of novelty of technological innovations introduced by industrial firms in Argentina. This influence is analysed from three perspectives: cooperation by partner type [business partners or scientific and technological centres (S&T) partners]; cooperation by number of partner types, from no cooperation to cooperation with two partner types; and cooperation by goals pursued by firms.

Design/methodology/approach

The data come from one of the last national innovation surveys conducted in Argentina. The study controls for endogeneity, using instrumental variable procedures within the conditional mixed-process (CMP) framework.

Findings

The main result is the influence of cooperation with universities and S&T centres on the introduction of more novel innovations, which was found both in estimations with and without endogeneity correction. This influence was verified for more complex goals (R&D, technology transfer and industrial design and engineering) as well as for less complex ones (tests and trials, human resources training, quality management and certification). Business cooperation seems to impact only on a lower degree of novelty for more complex goals. The increase in the number of partners that the firm cooperates with, from no cooperation to joint cooperation with two partner types, influences more novel innovations.

Research limitations/implications

Limitations and proposals for future research are discussed at the end of the study.

Practical implications

The results of this study contrast with the high propensity to cooperate with business partners shown by firms in Argentina and other Latin American countries. Therefore, this paper may help formulate more effective policies to promote cooperation conducive to firm innovation performance. Limitations and proposals for future research are discussed at the end of the study.

Originality/value

Although there is empirical evidence on this topic for developed countries, firm-level studies on cooperation and degree of novelty are scarce for Latin America. In addition, this paper analyses cooperation not only by type of partner but also by type of goal. This study attempted to control for endogeneity by using instrumental variables within the CMP framework.

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Article
Publication date: 29 October 2020

Lixin Cai

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…

Abstract

Purpose

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.

Design/methodology/approach

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.

Findings

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.

Originality/value

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.

Details

International Journal of Manpower, vol. 42 no. 5
Type: Research Article
ISSN: 0143-7720

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