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This paper presents a data driven approach that enables one to obtain a global measure of imbalance and to test it in a multivariate way. The main idea is based on the general framework of Partial Dependence Analysis ([2]), and thus of Conditional Multiple Correspondences Analysis ([4]) as tools for investigating the dependence relationship between a set of observable categorical covariates ( X ) and an assignment-to-treatment indicator variable (T), in order to obtain a global measure of imbalance according to their dependence structure.

Prior research considers the extent to which public assistance recipients’ charitable activity differs from the habits of the general population. Although receiving public assistance is negatively associated with donating money, the relationship to volunteering is unclear. In response to challenges overcoming selection bias, we conduct a multivariate cluster-based subgroup analysis to reduce bias in our claims about the ways in which public assistance receipt affects charitable activity. This innovative approach to dealing with the problem of selection bias has implications and applications across the social sciences.

Il lavoro passa in rassegna le evidenze statistiche sulla crescita del peso dei contratti a tempo determinato in Italia e in Europa e approfondisce l'esistenza di trappole della precarietà utilizzando dati riferiti alla provincia di Bologna.

A lot of studies are showing an increased tendency to use more than one mode of administration to collect data for a particular analysis ([10];[1];[16];[9]). Thus, understand if different data collection methods influence answers becomes a concern. The problem of self selection into different interview modes demands attention especially when the assignment to one or another collection method is not randomly controlled and respondents might self select in one data collection method over another. This paper shows an empirical case concerning the evalua- tion of two different data collection method: the CAWI (Computer Assisted Web Interview) method and the CATI (Computer Assisted Telephone Interview) method. If self-selection exists both mode of data collection and characteristics of the re- spondents influence answers. Hence, the mode effect may be confounded. In order to estimate an unbiased mode effect, this paper proposes a data driven multivariate approach to monitor self-selection that allows to disentangle interview’s modes ef- fects on answers from the effect of self-selection. We will work through the use of the monitoring system with an empirical case. In particular, we will use AlmaLaurea data and compare results of our approach to PS adjustment method that AlmaLaurea usually applies to control data quality as documented in various reports and analysis conducted by the AlmaLaurea Consortium .

Prior work has analyzed the extent to which welfare recipients engage in giving money and time to charitable causes (Brooks, 2002, 2004; Author4 & Author1, 2009; Author1 & Author4, 2011), finding that public assistance is negatively associated with donating money with the relationship to volunteering being unclear. Nevertheless, sticky issues of selection bias compel more deliberate thinking about the strength of assertions about cause and effect. In response, we now conduct a multivariate cluster-based subgroup analysis approach to more confidently infer causality about the ways in which welfare receipt affects charitable activity. This approach to dealing with the problem of selection bias capitalizes on the known treatment-associated variance in the X matrix, transforming data to estimate unbiased treatment effects. We contribute to both the substantive and methodological literatures with this work.

Some basic statistical methods (PCA,cluster analisys)have been applied to the food sector to identify homogeneous merchandising categories,because new food products are introduced into the market,having such quality features(all inevitably measurable) to make difficult the task to set clear categories.The mehods have been applied to savoury and sweet bakery products,and meaningful results have been obtained by utilising in the elaborations quality factors able to achieve them. By introducing into the elboration an increasing number of factors,the homogeinity of the category increases.

In recent years we are facing a growing attention paid by governments, organizations and companies on environmental issues. Academics have studied the role of different marketing innovations in leading to positive consequences for the environment. Among the four, innovation in product design and packaging has been regarded by the marketing literature as the starting point for many companies which intend to follow a path towards sustainability in their innovation activities. However, little is known on the specific EBs that could be achieved by the product design and packaging innovation. In this respect, the present study aims to shed light on the environmental contribution driven by innovation in product design and packaging in achieving environmental benefits, through an empirical analysis of the latest available Community Innovation Survey in Portugal related to the period 2012-2014. We run four logistic regression models on manufacturing companies where environmental benefits are a function of innovation in product design and marketing. Results show that the introduction of a product design and packaging marketing innovation yields environmental benefits, specifically, product recycling benefits and extended product life. Companies are called to consider introducing a product design and packaging innovation as it could represent a first meaningful step towards sustainability

This study attempts to answer the question of whether European manufacturing firms that undertake offshoring, innovation or both benefit from higher productivity and profitability. From a methodological point of view, the driving forces that push firms to innovate and/or to offshore can be seen as self-selection mechanisms that make the estimation of their economic impact more difficult if the confounding factors affecting these mechanisms also affect the economic performance of the firms. To disentangle the effect of both offshoring and innovation on firms’ performances from the effect of firm characteristics, the propensity score matching methodology in a multi-overlapping treatment setting is used. The study targets European countries using the EU-EFIGE/Bruegel-Unicredit dataset. Decisions to offshore and innovate do not seem to have a significant effect on productivity, whereas the decision to innovate only has a significant effect on firm profitability.