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Article
Publication date: 11 September 2017

Daeseon Choi, Younho Lee, Seokhyun Kim and Pilsung Kang

As the number of users on social network services (SNSs) continues to increase at a remarkable rate, privacy and security issues are consistently arising. Although users…

Abstract

Purpose

As the number of users on social network services (SNSs) continues to increase at a remarkable rate, privacy and security issues are consistently arising. Although users may not want to disclose their private attributes, these can be inferred from their public behavior on social media. In order to investigate the severity of the leakage of private information in this manner, the purpose of this paper is to present a method to infer undisclosed personal attributes of users based only on the data available on their public profiles on Facebook.

Design/methodology/approach

Facebook profile data consisting of 32 attributes were collected for 111,123 Korean users. Inferences were made for four private attributes (gender, age, marital status, and relationship status) based on five machine learning-based classification algorithms and three regression algorithms.

Findings

Experimental results showed that users’ gender can be inferred very accurately, whereas marital status and relationship status can be predicted more accurately with the authors’ algorithms than with a random model. Moreover, the average difference between the actual and predicted ages of users was only 0.5 years. The results show that some private attributes can be easily inferred from only a few pieces of user profile information, which can jeopardize personal information and may increase the risk to dignity.

Research limitations/implications

In this paper, the authors’ only utilized each user’s own profile data, especially text information. Since users in SNSs are directly or indirectly connected, inference performance can be improved if the profile data of the friends of a given user are additionally considered. Moreover, utilizing non-text profile information, such as profile images, can help increase inference accuracy. The authors’ can also provide a more generalized inference performance if a larger data set of Facebook users is available.

Practical implications

A private attribute leakage alarm system based on the inference model would be helpful for users not desirous of the disclosure of their private attributes on SNSs. SNS service providers can measure and monitor the risk of privacy leakage in their system to protect their users and optimize the target marketing based on the inferred information if users agree to use it.

Originality/value

This paper investigates whether private attributes of SNS users can be inferred with a few pieces of publicly available information although users are not willing to disclose them. The experimental results showed that gender, age, marital status, and relationship status, can be inferred by machine-learning algorithms. Based on these results, an early warning system was designed to help both service providers and users to protect the users’ privacy.

Details

Industrial Management & Data Systems, vol. 117 no. 8
Type: Research Article
ISSN: 0263-5577

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Book part
Publication date: 13 May 2017

Abstract

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

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Book part
Publication date: 13 May 2017

Otávio Bartalotti and Quentin Brummet

Regression discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions…

Abstract

Regression discontinuity designs have become popular in empirical studies due to their attractive properties for estimating causal effects under transparent assumptions. Nonetheless, most popular procedures assume i.i.d. data, which is unreasonable in many common applications. To fill this gap, we derive the properties of traditional local polynomial estimators in a fixed- G setting that allows for cluster dependence in the error term. Simulation results demonstrate that accounting for clustering in the data while selecting bandwidths may lead to lower MSE while maintaining proper coverage. We then apply our cluster-robust procedure to an application examining the impact of Low-Income Housing Tax Credits on neighborhood characteristics and low-income housing supply.

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Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

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Article
Publication date: 3 May 2021

Ali Abbas, Imad Moosa and Vikash Ramiah

This paper is about the effect of human capital on foreign direct investment (FDI). The purpose of this paper is to find out if developing countries with high levels of…

Abstract

Purpose

This paper is about the effect of human capital on foreign direct investment (FDI). The purpose of this paper is to find out if developing countries with high levels of human capital (educated people and well-trained labour force) are more successful in attracting FDI. The underlying hypothesis has been tested repeatedly without reaching a consensus view or providing an answer to the basic question. This is to be expected because FDI is determined by a large number of factors, making the results sensitive to the selected set of explanatory variables, which forms the basis of the Leamer (1983) critique of the use of multiple regression to derive inference. Furthermore, confirmation bias and publication bias entice researchers to be selective in choosing the set of results they report.

Design/methodology/approach

The technique of extreme bounds analysis, as originally suggested by Leamer (1983) and modified by Sala-i-Martin (1997), is used to determine the importance of human capital for the ability of developing countries to attract FDI. The authors use a cross-sectional sample covering 103 developing and transition countries.

Findings

The results show no contradiction between firms seeking human capital and cheap labour. No matter what proxy is used to represent human capital, it turns out that the most important factor for attracting FDI is the variable “employee compensation”, which is the wage bill, implying that multinational firms look for cheap and also skilled labour in the host country.

Originality/value

In this paper, the authors follow the procedure prescribed by Leamer (1983), and modified by Sala-i-Martin (1997), using extreme bounds analysis to distinguish between robust and fragile determinants of FDI, with particular emphasis on human capital. Instead of deriving inference from one regression equation by determining the statistical significance of the coefficient on the variable of interest, the extreme bounds or the distribution of estimated coefficients are used to distinguish between robust and fragile variables. This means that emphasis is shifted from significance, as implied by a single regression equation, to robustness, which is based on a large number of equations. The authors conduct tests on three proxies for human capital to find out if they are robust determinants of FDI and also judge the degree of robustness relative to other determinants.

Details

Journal of Intellectual Capital, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1469-1930

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Book part
Publication date: 13 May 2017

Luke Keele, Scott Lorch, Molly Passarella, Dylan Small and Rocío Titiunik

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating…

Abstract

We study research designs where a binary treatment changes discontinuously at the border between administrative units such as states, counties, or municipalities, creating a treated and a control area. This type of geographically discontinuous treatment assignment can be analyzed in a standard regression discontinuity (RD) framework if the exact geographic location of each unit in the dataset is known. Such data, however, is often unavailable due to privacy considerations or measurement limitations. In the absence of geo-referenced individual-level data, two scenarios can arise depending on what kind of geographic information is available. If researchers have information about each observation’s location within aggregate but small geographic units, a modified RD framework can be applied, where the running variable is treated as discrete instead of continuous. If researchers lack this type of information and instead only have access to the location of units within coarse aggregate geographic units that are too large to be considered in an RD framework, the available coarse geographic information can be used to create a band or buffer around the border, only including in the analysis observations that fall within this band. We characterize each scenario, and also discuss several methodological challenges that are common to all research designs based on geographically discontinuous treatment assignments. We illustrate these issues with an original geographic application that studies the effect of introducing copayments for the use of the Children’s Health Insurance Program in the United States, focusing on the border between Illinois and Wisconsin.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

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Book part
Publication date: 13 May 2017

Brigham R. Frandsen

Conventional tests of the regression discontinuity design’s identifying restrictions can perform poorly when the running variable is discrete. This paper proposes a test…

Abstract

Conventional tests of the regression discontinuity design’s identifying restrictions can perform poorly when the running variable is discrete. This paper proposes a test for manipulation of the running variable that is consistent when the running variable is discrete. The test exploits the fact that if the discrete running variable’s probability mass function satisfies a certain smoothness condition, then the observed frequency at the threshold has a known conditional distribution. The proposed test is applied to vote tally distributions in union representation elections and reveals evidence of manipulation in close elections that is in favor of employers when Republicans control the NLRB and in favor of unions otherwise.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

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Book part
Publication date: 13 May 2017

Otávio Bartalotti, Gray Calhoun and Yang He

This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild…

Abstract

This chapter develops a novel bootstrap procedure to obtain robust bias-corrected confidence intervals in regression discontinuity (RD) designs. The procedure uses a wild bootstrap from a second-order local polynomial to estimate the bias of the local linear RD estimator; the bias is then subtracted from the original estimator. The bias-corrected estimator is then bootstrapped itself to generate valid confidence intervals (CIs). The CIs generated by this procedure are valid under conditions similar to Calonico, Cattaneo, and Titiunik’s (2014) analytical correction – that is, when the bias of the naive RD estimator would otherwise prevent valid inference. This chapter also provides simulation evidence that our method is as accurate as the analytical corrections and we demonstrate its use through a reanalysis of Ludwig and Miller’s (2007) Head Start dataset.

Details

Regression Discontinuity Designs
Type: Book
ISBN: 978-1-78714-390-6

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Article
Publication date: 14 August 2018

Henri Akono

This paper aims to examine whether high equity incentives motivate executives to avoid issuing convertible debt and/or to design convertible debt issues as anti-dilutive…

Abstract

Purpose

This paper aims to examine whether high equity incentives motivate executives to avoid issuing convertible debt and/or to design convertible debt issues as anti-dilutive to earnings-per-share (EPS).

Design/methodology/approach

Tests are conducted using the Heckman two-step probit model to control for potential self-selection bias between firms that issue straight debt and those that issue convertible debt. Further, analyses are conducted separately and jointly for the Chief Executive Officer (CEO) and the Chief Financial Officer (CFO) to assess the differential impact of CEOs’ and CFOs’ equity incentives on convertible debt issuance and design decisions.

Findings

Firms are more likely to design convertible debt issues as anti-dilutive to EPS when CFOs have high levels of equity incentives, but only when the firm stock price is sensitive to diluted EPS. High CEOs’ equity incentives have limited impact of convertible debt issuance and design decisions.

Research limitations/implications

The main limitation of this study is the generalizability of the findings and implications of this study due to the smaller sample size of convertible debt issues.

Originality/value

Prior research has shown that bonus incentives influence CEOs with disincentive for EPS dilution and motivate them to make anti-dilutive financing decisions. Further, there is evidence that high equity incentives motivate CEOs to manage earnings to boost short-term prices. This study extends prior literature by showing that high equity incentives provide executives with disincentive for EPS dilution and motivate CFOs to design convertible debt issues as anti-dilutive to EPS possibly to avoid reduced stock prices. Further, this study shows that CFOs have greater influence over convertible debt design choices than CEOs do.

Details

Review of Accounting and Finance, vol. 17 no. 3
Type: Research Article
ISSN: 1475-7702

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Article
Publication date: 15 May 2017

Felix Canitz, Panagiotis Ballis-Papanastasiou, Christian Fieberg, Kerstin Lopatta, Armin Varmaz and Thomas Walker

The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither…

Abstract

Purpose

The purpose of this paper is to review and evaluate the methods commonly used in accounting literature to correct for cointegrated data and data that are neither stationary nor cointegrated.

Design/methodology/approach

The authors conducted Monte Carlo simulations according to Baltagi et al. (2011), Petersen (2009) and Gow et al. (2010), to analyze how regression results are affected by the possible nonstationarity of the variables of interest.

Findings

The results of this study suggest that biases in regression estimates can be reduced and valid inferences can be obtained by using robust standard errors clustered by firm, clustered by firm and time or Fama–MacBeth t-statistics based on the mean and standard errors of the cross section of coefficients from time-series regressions.

Originality/value

The findings of this study are suited to guide future researchers regarding which estimation methods are the most reliable given the possible nonstationarity of the variables of interest.

Details

The Journal of Risk Finance, vol. 18 no. 3
Type: Research Article
ISSN: 1526-5943

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Book part
Publication date: 15 April 2020

Jianning Kong, Peter C. B. Phillips and Donggyu Sul

Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key…

Abstract

Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak σ-convergence tests, have recently been developed (Kong, Phillips, & Sul, 2019) to evaluate such trends in dispersion in panel data using simple linear trend regressions. To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present chapter examines the behavior of these convergence tests when heteroskedastic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are reflected in finite sample performance in numerical exercises. The explanation is that weak σ-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.

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