The Determinants of Child Labour and Child Schooling in Ghana

This paper investigates the main determinants of child labourand child schooling in Ghana, with special reference to theirinteraction. The study provides evidence on the impact of povertyand quality of schooling on child labour hours, taking intoaccount their potential endogeneity. The exercise distinguishesbetween cluster poverty and household poverty in the two-stageHeckman estimation procedure. In addition, it relies on a setof non-common regressors to identify the child labour hoursregression from the selection equation. Other methodologicalfeatures include simultaneous equations estimation of childlabour, child schooling and poverty, taking into account theirjoint endogeneity. The empirical results contain some evidenceof sharp rural urban differences, thus, pointing to the needto adopt region specific policies in enhancing child welfare.However, rural, semi-urban and urban Ghana agree on the effectiverole that improved school attendance can play in curbing childlabour.     

Why Do Firms Issue Equity after Splitting Stocks?

This paper examines the motivations of firms that conduct seasoned equity offerings (SEOs) after splitting stocks. We find no difference in equity announcement and issue period returns between these firms and other equity‐issuing firms, suggesting that firms do not split stocks to reveal information and reduce adverse selection costs at the subsequent SEO. However, because investors react positively to split announcements, firms that issue equity after splitting stocks sell new shares at a higher price and raise more funds. We also find that firms split stocks to make the subsequent SEO more marketable to individual investors who are attracted to low‐priced shares.     

Generalised cost scaling: A new approach to demographic variables in demand analysis

A new approach to modelling equivalence scales and household demand systems is introduced and its usefulness empirically demonstrated. The empirical results suggest that price effects on the general equivalence scale are more important than utility effect.     

Child Labor, Child Schooling, and Their Interaction with Adult Labor: Empirical Evidence for Peru and Pakistan

Using data from Peru and Pakistan, this article tests two hypotheses:there is a positive association between hours of child laborand poverty, and there is a negative association between childschooling and poverty. Both of these hypotheses are confirmedby the Pakistani data, but not by the Peruvian data. The reductionin poverty rates due to income from children's labor is greaterin Pakistan than in Peru. The nature of interaction betweenadult and child labor markets varies with the gender of thechild and the adult. In Peru rising men's wages significantlyreduce the labor hours of girls, whereas in Pakistan there isa strong complementarity between women's and girls' labor markets.Both data sets agree on the positive role that increasing adulteducation can play in improving child welfare.     

Implied volatility and future portfolio returns

Prior studies find that the CBOE volatility index (VIX) predicts returns on stock market indices, suggesting implied volatilities measured by VIX are a risk factor affecting security returns or an indicator of market inefficiency. We extend prior work in three important ways. First, we investigate the relationship between future returns and current implied volatility levels and innovations. Second, we examine portfolios sorted on book-to-market equity, size, and beta. Third, we control for the four Fama and French [Fama, E., French, K., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3–56.] and Carhart [Carhart, M., 1997. On persistence in mutual fund performance. Journal of Finance, 52, 57–82.] factors. We find that VIX-related variables have strong predictive ability.     

On the interpretation of bribery in a laboratory corruption game: moral frames and social norms

Past studies on laboratory corruption games have not been able to find consistent evidence that subjects make “immoral” decisions. A possible reason, and also a critique of laboratory corruption games, is that the experiment may fail to trigger the intended immorality frame in the minds of the participants, leading many to question the very raison d’être of laboratory corruption games. To test this idea, we compare behavior in a harassment bribery game with a strategically identical but neutrally framed ultimatum game. The results show that fewer people, both as briber and bribee, engage in corruption in the bribery frame than in the alternative and the average bribe amount is lesser in the former than in the latter. These suggest that moral costs are indeed at work. A third treatment, which relabels the bribery game in neutral language, indicates that the observed treatment effect arises not from the neutral language of the ultimatum game but from a change in the sense of entitlement between the bribery and ultimatum game frames. To provide further support that the bribery game does measure moral costs, we elicit the shared perceptions of appropriateness of the actions or social norm, under the two frames. We show that the social norm governing the bribery game frame and ultimatum game frame are indeed different and that the perceived sense of social appropriateness plays a crucial role in determining the actual behavior in the two frames. Furthermore, merely relabelling the bribery game in neutral language makes no difference to the social appropriateness norm governing it. This indicates that, just as in the case of actual behavior, the observed difference in social appropriateness norm between bribery game and ultimatum game comes from the difference in entitlement too. Finally, we comment on the external validity of behavior in lab corruption games.     

Classification of spammer and nonspammer content in online social network using genetic algorithm-based feature selection

ABSTRACT

The emergence of online social network invokes social actors to share their personal information digitally. Moreover, it provides the facility to maintain their links with people of same interest globally. Take advantage of these services; it has become a fascinating testbed to invite various threats like a spammer. Detection of spammer in OSN is one of the most critical tasks. Spammer not only spreads unwanted or bad advertisement but does certain malicious activity in others' profiles. By clearly understanding the activities of different threats, some incremental and accurate approaches are needed for detecting spammer content and profiles involved in these activities by using social network services. Therefore, the focus of this article is to detect spammer content and account, specifically on the leading microblogging platform called Twitter. We propose a hybrid approach which leverages the capabilities of various machine learning algorithms to separate spammer and nonspammer contents and account. Initially, the optimisation algorithm called genetic algorithm analyses the various features and selects the best suitable features that influence the behaviour of user account, and these features are then used to train classifiers. Our framework achieved to severalise spammer and nonspammer content in an effective way. Finally, to prove the efficiency of our proposed framework, a comparative analysis is conducted with some existing state-of-art techniques. The experimental analysis shows that our approach achieves a high detection rate of 99.6%, which is better than other state-of-art techniques.