Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL Copy DOI

Deceptive (or misleading) advertising can harm advertisers’ competitors, confuse consumer choices, and reduce the confidence in the informative role of marketing communication. Many countries do not allow deceptive advertising and provide penalties in the case of infringements. In Italy, deceptive advertising has been prohibited since 1992, but penalties were only introduced in 2005. This paper examines whether the adoption of fines and their increase in 2007 led to a decline in the legal proceedings undertaken by the antitrust authority for deceptive advertising. The results show that the adoption of fines did not have a significant impact on the number of proceedings. When higher fines were introduced, there was a significant decline in this number, although this effect depends on the size of the advertising firms, the economic sectors, the media involved, and the deceptive component of the advertising message. These results can be helpful in refining and improving the enforcement of advertising regulations, given the scarce resources available to regulation agencies to combat this widespread marketing practice.

The official statistics produced by National Statistical Institutes are mainly used by policy makers to take decisions. In particular, when policy makers and decision takers would like to know the impact of a given policy, it is important to acknowledge the heterogeneity of the treatment effects for different domains. If the domain of interest is small with regard to its sample size, then the evaluator has entered the small area estimation (SAE) dilemma. Based on the modification of the Inverse Propensity Weighting estimator and the traditional small area predictors, new estimators of area specific average treatment effects are proposed for unplanned domains. A robustified version of the predictor against presence of the outliers is also developed. Analytical Mean Squared Error (MSE) estimators of the proposed predictors are derived. These methods provide a tool to map the policy impacts that can help to better target the treatment group(s). The properties of these small area estimators are illustrated by means of a design-based simulation using a real data set where the aim is to study the effects of permanent versus temporary contracts on the economic insecurity of households in different regions of Italy.

Sampling is informative when probabilities of sample inclusion depend on unknown variables that are correlated with a response variable of interest. When sample inclusion probabilities are available, inverse probability weighting can be used to account for informative sampling in such a situation, although usually at the cost of less precise inference. This paper reviews two important research contributions by Chris Skinner that modify these weights to reduce their variability while at the same time retaining consistency of the weighted estimators. In some cases, however, sample inclusion probabilities are not known, and are estimated as propensity scores. This is often the situation in causal analysis, and double robust methods that protect against the resulting misspecification of the sampling process have been the focus of much recent research. In this paper we propose two model-assisted modifications to the popular inverse propensity score weighted estimator of an average treatment effect, and then illustrate their use in a causal analysis of a rainfall enhancement experiment that was carried out in Oman between 2013 and 2018.

Media scholars raised several concerns about the digital television transition implemented at the beginning of this millennium. The impact of the transition on local TV systems is a crucial issue, because many small- and medium-sized enterprises may not be prepared to switch to a new technology and may exit the market. This article studies the survival of local TV stations in Italy during the transition to digital television. The empirical analysis shows that the adoption of the digital system is associated with a sharp decrease of the probability of survival of local TV broadcasters. While this result confirms that local media struggle to compete in the digital world, the overall assessment at the national level is not straightforward, given the imbalance between many local TV stations and scarce economic resources before the switch-off.

Scholars have long debated about the economy-environment trade-off, for which the economic conditions affect the attention of citizens and mass media towards environmental issues. This paper explores this theme within the US press and studies which factors are associated with the media coverage of electric vehicles (EVs) in four major national newspapers between 1995 and 2015. According to the empirical estimates, the media attention towards EVs shows a weak association with good economic conditions, policy initiatives in support of sustainable mobility, and tenures of Democratic presidents.

When doing impact evaluation and making causal inferences, it is important to acknowledge the heterogeneity of the treatment effects for different domains (geographic, socio-demographic, or socio-economic). If the domain of interest is small with regards to its sample size (or even zero in some cases), then the evaluator has entered the small area estimation (SAE) dilemma. Based on the modification of the Inverse Propensity Weighting estimator and the traditional small area predictors, the paper proposes a new methodology to estimate area specific average treatment effects for unplanned domains. By means of these methods we can also provide a map of policy impacts, that can help to better target the treatment group(s). We develop analytical Mean Squared Error (MSE) estimators of the proposed predictors. An extensive simulation analysis, also based on real data, shows that the proposed techniques in most cases lead to more efficient estimators.

The economic crisis and the pressure towards efficient and effective use of public money claim for a higher accountability of research expenditure, as well as for a greater proximity of research to the needs of community. While agricultural productivity represents a worldwide goal for agricultural research as a response to growing food, feed and energy demands, other objectives besides productivity are becoming central. The challenge is to take into account broader impacts that go beyond academic and economic ones, and to improve knowledge on the causal impact-generating mechanisms. In this paper, we adopt a causal perspective and estimate the impact of agricultural research expenditure on multiple dimensions. We develop a structural equation model relating research expenditure, research activity, productivity and multiple impact indicators within a dynamic impact pathway, accounting for existing domain knowledge on causal relationships and their lag structures. The model is applied on EU 15 countries over the period 1980–2014, making use of official statistics from several European databases.

The shifting of welfare systems to the local level may have positive or negative consequences. On the one hand, it may be argued that local governments better tailor welfare policies to the specific needs of population, given their direct knowledge of territory; on the other hand, especially in the presence of weak supervision by the central government, decentralization may cause inequalities and territorial fragmentation. The main objective of the paper is to explore the relationship between welfare state typologies – with different degrees of decentralization – and the level of monetary poverty and material deprivation of citizens. Using data from official statistics, we model individual binary outcomes (living or not under the poverty line, being or not able to make ends meet) as a function of both family- level and country-level characteristics. The empirical analysis is run on a selection of European countries for the year 2013.

In the last decades, European welfare systems have undergone continuous reforms in the light of financial pressures. Monitoring changes requires to consider several dimensions of welfare systems, such as the composition of risks and needs covered, the rules for accessing benefits or the type of social benefits delivered. Finally, it is relevant to take into account the geographical area where beneficiaries live, since in some countries local governments are assigned managing and, sometimes, legislative competencies on social protection areas. This paper aims at exploring official statistics on European welfare systems, by focusing on social benefits. The objective is assessing if available statistics allow one to compare the level and the kind of social benefits delivered across European countries both at national and sub-national levels. We focus on the Italian case to provide some examples.

People living in close geographic areas can experience different quality of life standards. This depends on many factors among which the performance of local welfare systems may play a relevant role. In this paper, we examine Italian official statistics in order to identify available information on local social protection activities. The main objective is to assess if it is possible to convey a complete view of the level and quality of social protection services delivered by local actors. Furthermore, we present some analysis of municipalities’ social expenditure at Nuts 2 and 3 levels to show evidence of disparities among territories.

This paper aims at exploring changes in welfare state in Europe in the last decade, focussing on social protection expenditure. In particular, we examine the evolution of social protection expenditures in four countries, namely United Kingdom, Denmark, Italy and France, each one representing a specific welfare state model. The objective is to point out whether the crisis is pushing countries towards more homogeneous expenditure patterns or, at the opposite, towards even more polarized systems. Furthermore, we investigate the effect of social protection policies on people economic well-being (disposable income, consumption expenditure and wealth), with a focus on social benefits. The analysis is based on macroeconomic data, namely National Accounts and ESPROSS data.

Structural Equation Modelling is a class of statistical models typically employed to analyse the dependence relationships among a set of variables. We define an extension of the class where variables are related by distributed-lag linear regression models, in order to account for temporal delays in the dependence relationships among the variables. Our proposal is applied to impact assessment of research activity on European Agriculture.

Many empirical settings involve the specification of models leading to complicated likelihood functions, for example, finite mixture models that arise in causal inference when using Principal Stratification (PS). Traditional asymptotic results cannot be trusted for the associated likelihood functions, whose logarithms are not close to being quadratic and may be multimodal even with large sample sizes. We first investigate the shape of the likelihood function with models based on PS by providing diagnostic tools for evaluating ellipsoidal approximations based on the second derivatives of the log‐likelihood at a mode. In these settings, inference based on standard approximations is inappropriate, and other forms of inference are required. We explore the use of a direct likelihood approach for parsimonious model selection and, specifically, propose comparing values of scaled maximized likelihood functions under competitive models to select preferred models. An extensive simulation study provides guidelines, for calibrating the use of scaled log‐likelihood ratio statistics, as functions of the complexity of the models being compared.

Unless strong assumptions are made, nonparametric identification of principal causal effects can only be partial and bounds (or sets) for the causal effects are established. In the presence of a secondary outcome, recent results exist to sharpen the bounds that exploit conditional independence assumptions. More general results, though not embedded in a causal framework, can be found in concentration graphical models with a latent variable. The aim of this article is to establish a link between the two settings and to show that adapting and extending results pertaining to concentration graphical models can help achieving identification of principal casual effects in studies when more than one additional outcome is available. Model selection criteria are also suggested. An empirical illustrative example is provided, using data from a real social experiment.

We analyze the impact on employment of the 2003/2005 CAP reform (decoupled payments) in Tuscany farms. We use data coming from census (2000 and 2010) and administrative archives, and apply propensity score-based methods to evaluate the average treatment effect of pay- ments on employment-related outcomes, taking into account that the assignment mechanism of payments was non-random. We further investigate impact heterogeneity using a generalized propensity-score matching methodology focussing on a subgroup of farms. Results show that there exists some effect of the payments on employment and that the impact is heterogeneous over the different amounts of payments.