In: Prast , H M , Rossi , M , Torricelli , C & Druta , C 2014 ' Do Women Prefer Pink? The Effect of a Gender Stereotypical Stock Portfolio on Investing Decisions ' NETSPAR Discussion Paper , vol. 01/2014-009 , NETSPAR , Tilburg .
We investigate whether lack of familiarity may contribute to an explanation of the gender gap in stock market participation and risk taking. We use ads in widely read women magazines to select companies that we assume to be more familiar to women than to men, and construct a "pink" portfolio. We construct a "blue" portfolio by selecting stocks from the AEX index. We ask members of the CentERpanel how they would allocate 100.000 euro of pension wealth. Half of respondents are given the choice between government bonds and a portfolio consisting of companies most traded at Amsterdam Exchanges, while the other half can choose between government bonds and our "pink" portfolio. We find that significantly more women than men choose not to respond after having seen the question and that respondents tend to allocate their hypothetical savings fifty-fifty over stocks and bonds. This could be interpreted either as going for the default choice or the 1/n heuristic. We find a pink portfolio effect among older women, and a significant of framing which is larger for women than for men. We also find that women who already own stocks allocate significantly more to the stock basket than women who do not, which may be interpreted as an effect of familiarity. We find no such effect among men. Our evidence does not show that lack of familiarity with the large companies most traded at the Amsterdam stock exchange explains the gender gap in participation and portfolio choice. What we do find, however, is that a pink portfolio reduces decision time for women, and results in women deciding quicker than men.
Abstract. We assessed societal landslide and flood risk to the population of Italy. The assessment was conducted at the national (synoptic) and at the regional scales. For the assessment, we used an improved version of the catalogue of historical landslide and flood events that have resulted in loss of life, missing persons, injuries and homelessness in Italy, from 1850 to 2008. This is the recent portion of a larger catalogue spanning the 1941-year period from 68 to 2008. We started by discussing uncertainty and completeness in the historical catalogue, and we performed an analysis of the temporal and geographical pattern of harmful landslide and flood events, in Italy. We found that sites affected by harmful landslides or floods are not distributed evenly in Italy, and we attributed the differences to different physiographical settings. To determine societal risk, we investigated the distribution of the number of landslide and flood casualties (deaths, missing persons, and injured people) in Italy, and in the 20 Italian Regions. Using order statistics, we found that the intensity of a landslide or flood event – measured by the total number of casualties in the event – follows a general negative power law trend. Next, we modelled the empirical distributions of the frequency of landslide and flood events with casualties in Italy and in each Region using a Zipf distribution. We used the scaling exponent s of the probability mass function (PMF) of the intensity of the events, which controls the proportion of small, medium, and large events, to compare societal risk levels in different geographical areas and for different periods. Lastly, to consider the frequency of the events with casualties, we scaled the PMF obtained for the individual Regions to the total number of events in each Region, in the period 1950–2008, and we used the results to rank societal landslide and flood risk in Italy. We found that in the considered period societal landslide risk is largest in Trentino-Alto Adige and Campania, and societal flood risk is highest in Piedmont and Sicily.
Abstract. We used landslide information for 13 study areas in Italy and morphometric information obtained from the 3-arcseconds shuttle radar topography mission digital elevation model (SRTM DEM) to determine areas where landslide susceptibility is expected to be negligible in Italy and in the landmasses surrounding the Mediterranean Sea. The morphometric information consisted of the local terrain slope which was computed in a square 3 × 3-cell moving window, and in the regional relative relief computed in a circular 15 × 15-cell moving window. We tested three different models to classify the "non-susceptible" landslide areas, including a linear model (LNR), a quantile linear model (QLR), and a quantile, non-linear model (QNL). We tested the performance of the three models using independent landslide information presented by the Italian Landslide Inventory (Inventario Fenomeni Franosi in Italia – IFFI). Best results were obtained using the QNL model. The corresponding zonation of non-susceptible landslide areas was intersected in a geographic information system (GIS) with geographical census data for Italy. The result determined that 57.5% of the population of Italy (in 2001) was located in areas where landslide susceptibility is expected to be negligible. We applied the QNL model to the landmasses surrounding the Mediterranean Sea, and we tested the synoptic non-susceptibility zonation using independent landslide information for three study areas in Spain. Results showed that the QNL model was capable of determining where landslide susceptibility is expected to be negligible in the validation areas in Spain. We expect our results to be applicable in similar study areas, facilitating the identification of non-susceptible landslide areas, at the synoptic scale.
