Box and Cox proposed a power transformation for the response variable that yields a linear regression model with normal error and constant variance. Inference procedures for the regression coef cients and transformation parameter under this model setting have been studied extensively. In this article we propose a simple semiparametric estimation method for the Box-Cox transformation model with no speci c parametric assumption on the distribution of the error term. The resulting estimators are strongly consistent and asymptotically normal. Their covariance matrix can be estimated through a novel resampling method without involving nonparametric function estimates of the underlying unknown density function for the error term. The new proposal is illustrated with a well-known dataset in the literature of transformation, and its ef ciency and robustness are closely examined through numerical studies.

Emerging markets are fast-growing developing countries that are creating not only a rapidly expanding segment of middle class and rich consumers but also have a sizable segment of Bpoor^consumers. This paper presents an inter-disciplinary perspective integrating insights from quantitative and behavioral marketing, social psychology, industrial organization, and development economics with the purpose of generating and answering research questions on emerging markets. We organize our discussion around three themes. First, there is substantial heterogeneity in the social, cultural, economic, and institutional environments as well as rapid change in these characteristics. Coupled together, the heterogeneity and dynamics increase the scope of variables and interrelationships that have traditionally been investigated. Second, emerging markets continue to have sizeable Bpoor^and rapidly growing Bnew rich^populations, requiring marketers and researchers to understand how to market to the poor and the Bnew rich.^Exploiting these features in research can help deepen our theoretical understanding of markets and marketing. Third, from a methodological perspective, differences in types of available secondary data and the lower cost of collecting primary data create opportunities to develop new approaches for addressing research questions. We also encourage scholars to move beyond crosscountry regressions offering broadbrush exploratory insight, to country-industry-specific research that exploits unique characteristics of a particular emerging market. This article emerged out of presentations and discussions among the authors in a session titled BEmerging Markets^at the 9th Invitational Choice Symposium hosted by Erasmus University in the Netherlands in 2013.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte.Abstract. We construct a dedicated web interface and use it to conduct a laboratory experiment to study willingness to lend and preference over borrowers in micro-finance lending. We distinguish between perceptions of transaction-related factors, such as neediness and trustworthiness, and identity-related factors such as ethnicity and gender. By looking at both channels together we are able to assess the extent to which identity-related differences in lending can be attributed to differences in how transaction-related factors are perceived among people with similar and different identities. We find that (1) both financial return and philanthropic motivation affect the amount lent, with little evidence that the former crowds out the latter; (2) lenders have a statistically significant preference for borrowers with whom they share gender and ethnic similarity even after controlling for perceived riskiness, neediness, and other factors including physical attractiveness and weight; and (3) lenders are willing to trade greater risk in order to help more needy borrowers, but at a rate sensitive to their own degree of financial exposure.