InGRID Visiting in AIAS
REPORT
Camilla Gaiaschi
15-06-2015 to 26-06-2015
During the two weeks of visiting in Aias I have worked on my Ph.D. thesis. More in particular, I have
analyzed my dataset and worked on two models with the supervision of Stephanie Steinmetz: one
for the gender pay gap and one for the gender vertical segregation.
The research investigates gender inequalities in medical careers. The dataset is drawn from a survey
which has been sent to the physicians of five hospitals in the region of Lombardy, Italy. The research
is part of the European project S.T.A.G.E.S (Structural Transformation to Achieve Gender Equality in
Science) at the University of Milan. Up to now, the dataset contains the information of the first four
hospitals. The survey from the fifth hospital still need to be closed. The aim of the survey is to collect
data on human capital, work characteristics, family characteristics, organizational characteristics,
etc.
During the first day of my stay, I had a meeting with Kea Tijdens and Stephanie Steinmetz in order to
discuss on my research and set up our work for the following two weeks. Later, I had periodical
meetings with Stephanie Steinmetz in order to discuss the advancements of my work here.
During my first week, I spent a lot of time in analyzing the dataset in order to identify the variables
that I would have added into the regressions. I’ve run descriptive statistics, t-test on means by
gender in order to have an idea of my dataset. I have cleaned, renamed and recodified variables, I
also have merged the dataset of the fourth hospital (which is a private hospital) to the dataset of the
first three (public) hospitals (that were already merged before arriving in Amsterdam). Such work
required an harmonization of some variables.
After having cleaned the dataset I was able to focus on my models. The first one is an OLS model on
income. The second and the third one are, respectively, a Logit model and a Multinomial Logit model
on position. During my stay I have mainly worked on the first one.
Concerning the explanatory variables to add into the models, before arriving in Aias I had previously
worked on work characteristics and ended up with a OLS model on income where the variable
indicating the number of hours of work of the respondent was problematic: taking out the
observations with 0 hours of work, as Stephanie suggested, helped a lot. While here in Aias I’ve
mainly worked on family characteristics such as marital status, children, age of the children, working
hours of the partner, etc. I’ve run interactions (between gender and marital status, between gender
and children, between spouse and working characteristics of the spouse, etc.). I ended up with a
step-by-step model on income made of five different models: a binary model with gender as the only
explanatory variable, a multiple regression model with work characteristics (the only one that I had
already modelled, as above-mentioned, before arriving in Amsterdam), a multiple regression model
with family characteristics, a multiple regression model with work and family characteristics but
without the variable indicating the number of children and, finally, a multiple regression model with
work and family characteristics + the variable indicating the number of children. The first results
indicate that gender has a significant (negative) impact on the dependent variable income unless we
add children in the regression.
While modelling, I’ve also worked on the recategorization of quasi-cardinal variables in categorical
ones and I have done diagnostic analysis on collinearity, outliers and influential points.
After having settled the Ols model on income I’ve started working on vertical segregation. Together
with Stephanie we decided to focus on two models: a binary logit model with a binary dependent
variable (1=directors of hospital departments 0=all others physicians) and a multinomial logit model
with a multiple-categorical variable as dependent variable (2=higher positions; 1=intermediate
positions 0=lower positions). I have used pretty much the same explanatory variables used in the
OLS-income model (except, of course, for the variable indicating the position which in the Ols
income model was an explanatory variable and now is the dependent variable). Both regressions still
need to be modelled and I need to work more on the variables.
In conclusions, my stay here in AIAS has been very useful. Stephanie has been very helpful and her
suggestions were always very precious. I’m glad to have explored the variables and ended up with a
pretty good Ols model on income. Thanks for hosting me here!
Amsterdam, 26-06-2015
Camilla Gaiaschi