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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.

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.

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.

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.

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.

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.

In this chapter, the author argues that Austrians are perhaps uniquely placed to be effective practitioners of causal inference techniques on observational data. This is because, while the methods are easy to implement, their validity and value lies in a detailed, “analytical/historical” narrative to accompany the findings. This is true for several reasons. (1) all the models have identifying assumptions (e.g., no spillovers and parallel trends) that are best addressed by an exposition of the institutional/economic/historical milieu in place before and after the treatment under study; (2) determination of external validity also requires detailed institutional and historical knowledge; and (3) researchers often want to know the mechanisms producing the reduced form result that comes out of most causal inference studies. Here again, institutional and historical learning is crucial. My conclusion is that Austrians should add the tools of causal inference with observational data to their arsenal of analysis. This would be good both for their publication prospects and for the profession at large.

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.

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.

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.

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.

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.

Banks, financial statement users, and accounting standard setters have long disagreed on the informativeness of banks’ statements of cash flows (SCFs) and there is a lack of relevant evidence in the literature. This paper examines the informativeness of the SCFs of U.S. commercial banks in two settings where SCFs are purported to be useful. The first analysis tests the incremental value relevance of banks’ SCFs beyond income statements and balance sheets and compares bank's SCFs with those of industrial firms. We find that banks’ SCFs have limited incremental value relevance, and are much less value relevant than industrial firms’ SCFs. The second analysis examines and finds no distress-predictive power of banks’ SCFs, especially in the presence of standard distress predictors. Overall, our results are consistent with the view that banks’ SCFs have limited informativeness.

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.

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.

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.

This study aims to examine the relationship between integrated reporting (IR) quality and corporate tax avoidance (CTA). IR is an emerging reporting mechanism, while CTA practices are considered a hindrance to inclusive and sustainable growth. The study also assesses the moderating role of firm complexity on the IR-CTA relationship. Additionally, this study also envisages that CTA practices are not static. Hence, it also analyses the IR-CTA relationship across different intensity levels of CTA practices. The study focusses on listed companies in South Africa, the only country that has mandated IR practice so far.

Ordinary least square and quantile regressions are used to analyse archival and content analysis data for firms listed on the Johannesburg Stock Exchange from 2011 to 2017.

This study finds that IR quality negatively associates firms CTA practices. It further concludes that although firms’ transparency level increases due to IR quality, firm complexity reduces the significant negative relationship between IR and CTA practices. The findings also indicate that the IR-CTA relationship is not constant but instead differs across the CTA quantiles. At aggressive levels of CTA, no relationship is established between IR quality and firms’ CTA practices.

The findings provide a useful and more detailed description of the relationship between information quality and CTA practice, focussing on IR, an emerging reporting mechanism that is considered innovative and transparent.

Considering the IR-CTA relationship found in this study, IR quality implementation may indirectly contribute to attaining sustainable development goals by reducing CTA practices.

This study examines the relationship between reporting quality and firms’ CTA practices from the perspectives of an emerging reporting mechanism, with a focus on South Africa, the only country that has mandated IR practice. Furthermore, the distributional mean effects of IR quality on firms’ CTA practices explored in this study extend beyond the usual IR-CTA relationship.