Don’t venture too far on the internet: bad neighborhoods were located! Internet bad neighborhoods are those geographical areas where the majority of spam and phishing mails originate from. Interestingly, some regions specialize in spam, while others focus on phishing for your bank account.

Having successfully defended his dissertation at the University of Twente (the Netherlands), Giovane Moura now attracts huge crowds of attention with his research. First his research was discussed on slashdot (link), next on BBC news (link).

From the press release:

Of the 42,000 Internet Service Providers (ISPs) surveyed, just 20 were found to be responsible for nearly half of all the internet addresses that send spam. That just is one of the striking results of an extensive study by the University of Twente’s Centre for Telematics and Information Technology (CTIT). This study focused on “Bad Neighbourhoods” on the internet (which sometimes correspond to certain geographical areas) that are the source of a great deal of spam, phishing or other undesirable activity. In his thesis, Giovane Moura describes this situation in detail.

The results have practical implications. BBC writes:

The large-scale study was carried out to help fine-tune computer security tools that scrutinise the net addresses of email and other messages to help them work out if they are junk or legitimate. Such tools could make better choices if they were armed with historical information about the types of traffic that emerge from particular networks.

Giovane C. M. Moura, Anna Sperotto, Ramin Sadre, & Aiko Pras (2013). Evaluating Third-Party Bad Neighborhood Blacklists for Spam Detection IFIP/IEEE International Symposium on Integrated Network Management

Institutional and Demographic Explanations of Women’s Employment
Family policies have stimulated women’s employment in OECD countries in recent decades. Women also gained a stronger position on the labour market with higher status positions and higher wages. As a result of this stronger position of women on the labour market, earnings inequality between men and women, and within households, decreased. Of course, the strong increase of women’s employment was not only explained by the implementation of family policies, but for instance also by women’s rising educational levels and decreasing fertility. In my dissertation (to be completed this year) I address the interplay between these institutional and demographic explanations of women’s employment.

First, I will outline the background of my dissertation. I argue that studies solely using macro-level data to study the outcomes of family policies on women’s employment are limited in two ways. First, they run the risk of committing an ecological fallacy, which is best illustrated by the observation that after 1985, the cross-country correlation between fertility and women’s employment turned positive. Second, I argue that studies using only macro-level data – despite the advantages of such data – are limited in the type of questions they can answer and cannot properly account for demographic changes. In other words: using solely macro-level data, studies cannot account for the combination of institutional and demographic explanations of women’s employment. I will illustrate my argument based on two chapters in my dissertation.

Secondly, I will detail a third chapter, which addresses the question whether the increased availability of family policies have the unexpected result of increased the earnings inequalities between households. I present a (very) preliminary analysis of a decomposition of earnings inequalities between households, in 17 OECD countries from 1975 to 2005. The results suggest that in societies with extensive reconciliation policies, women’s earnings have a stronger attenuating effect on the inequality between households. In contrast, in societies with extensive financial support policies, the attenuating effects of women’s earnings on household inequalities is weaker. It is well known that earnings inequalities between households have been increasing in recent decades. This analysis suggests that there is no ground to the concern that family policies have contributed to this trend. On the contrary: societies that provide an institutional context facilitating small inequalities within households, also tend to attenuate the inequalities between households.

From the press release:

Good qualifications, a career with good prospects, a full-time job in a mainly male environment and irregular hours are all factors that cause women to postpone having their first child. These are some of the findings from research carried out by Katia Begall in four separate studies into the relationship between working conditions and fertility. She discovered that highly qualified women working in sectors that employ relatively few women are much more likely to postpone having their first child. Women working in sectors with a relatively large female workforce, such as healthcare and education, are less likely to postpone the birth of their first child. Begall has come up with two explanations: ‘Having children appears to be “catching” in sectors employing mainly women. What’s more, it is easier to have children in these sectors as employees often qualify for paid parental leave.’ The partner’s qualifications and type of work appear to have little impact on the timing of the first child. ‘The woman’s job is the deciding factor, although we did note a delay among highly qualified men, which we put down to the fact that many of them are in relationships with highly qualified women.’

The press release is to be found here, and the complete dissertation can be found online as well.

The Work and Family Researchers Network is seeking applicants for the 2013-2014 Early Career Work and Family Scholars Program. Fifteen scholars will be selected for the program. 

They advertise:

To be eligible, candidates must have received their doctorate in 2010 or later, and have yet to progress into tenured or secure senior level positions.  Those anticipating receipt of their doctoral degree by June 2013 are eligible to apply.  Application is not restricted on the basis of location. Recipients of the award will be expected to become members of the WFRN.   To apply for the Early Career Scholars Program, go here.

The goal of the Early Career Work and Family Scholars Program is to provide supports for recent doctoral recipients to facilitate their teaching and research scholarship.  By offering resources and consultation, the program is designed to help promising young scholars move into tenured appointments and secure senior level positions, as well as connect them to the broad work and family community by enhancing their professional networks.  To date, 50 scholars in their early career stages have benefitted from resources provided through this program.

All participants are required to participate in the WFRN conference to be held June 19-21, 2014 in New York City.  Participants receive up to $1,000 to defer travel expenses. At the conference, special events will be targeted to serve their interests, such as networking opportunities with senior scholars and other career development activities. In addition, they will be connected with one another in periodic encounters beyond the conference, designed to facilitate collaboration, peer-mentorship, guidance on teaching, and development of research programs. They will also receive periodic mailings of opportunities of special interest to work and family scholars who are at their early career stages and have opportunities to engage in teleconferences.

I attended the inaugural meeting of the Work and Family Researchers Network in New York (2012), and was truly impressed by the multidisciplinary goodness that was on offer. So, really, apply if you can!

Contributing to a scientific discipline means that a PhD candidate is supervised to create scientific products of the highest possible quality, and does so in an increasingly independent manner. Participating in a scientific discipline entails presenting these scientific products to others, frequently discussing these with colleagues, and collaborating with representatives of that discipline. Participation also means enculturation in the norms and values of a discipline. In addition, society increasingly expects scientists to be able to explain the relevance of their work to an audience outside their own discipline. A scientific discipline is not limited to a single department, nor to a single university or even to a single country. Phd candidates should operate in an internationally oriented community.

Doing your PhD is and should be a demanding challenge. Important skills are learned from dealing with this challenge; it is easy to make mistakes, but challenging to learn to recognize ones’ own mistakes. A good supervisor allows PhD candidates to independently develop and try their own solutions to problems, while reflecting on that process. A fine balance should be found between safeguarding the candidate from endangering progress and completion of the project in time, without predefining all the decisions that need to be made.

In addition to the academic development, which should be prioritized, PhD candidates and their supervisors should identify personal training needs to be inlcuded in the training and supervision plan. Participation in projects besides the thesis, in teaching, and in administrative tasks, invest in training young academics the skills required for their future careers. Phd candidates play a crucial role in university,

The scientific progress, as well as in teaching, and should therefore be regarded employees of a university. Integration into the scientific community contributes to the professional development of PhD candidates.

Skills that all PhD graduates have in common include research- and analytical skills, the conception, planning, and management of a large and long-term project, the organization and presentation of complex information, and perseverance. All PhDs are about scientific research, but the developed skills can be applied in future careers in academia, as well as in business. With PhD candidates working on fixed term projects, they should be stimulated to anticipate their future career already at early stages during the PhD. By realizing how to apply their general skills to future plans, PhD candidates should be fully prepared for the next step in their careers.

For the last two years, I have been actively involved with the PhD Network of the University of Twente (P-NUT), including the last year as president. Recently, I wrote down my vision on doing a good PhD, which will serve as a basis for the representative activities of P-NUT. It was written on a general level, and P-NUT combines this vision with concrete goals. Here, for discussion, only the general vision on doing a good PhD is presented.

In the end, I won the best poster prize. An updated of that poster (with a slightly modified title) is available here.

In the meantime, the research this poster was based on was published in the Journal of Marriage of Family, as:

Rense Nieuwenhuis, Ariana Need, & Henk van der Kolk (2012). Institutional and Demographic Explanations of Women’s Employment in 18 OECD Countries, 1975-1999 Journal of Marriage and Family, 74 (June), 614-630

Today, with this study, I was honored to receive the best research of 2012 award, given by the School of Management and Governance of the University of Twente. I’m very excited about the recognition our study receives, and the amount of attention it attracts.

So, please bear with me as I run you through some of our findings.

In our study we set out to describe the degree to which mothers in industrialized societies between 1975 to 1999 were less likely to be employed than women without children. We found that in no country mothers were more likely to be employed than women without children. Over time, women were increasingly likely to combine motherhood and employment in many, but not all, countries. Both mothers and women without children were more likely to be employed in societies with a large service sector and low unemployment. As you might expect, but has not been tested often, the employment of women without children was generally unaffected by family policies. Mothers were more likely to be employed in societies with extensive reconciliation policies and limited family allowances.

Interesting, right? If you want to know more (or don’t have access to the publication linked above), please don’t hesitate to contact me.

PS: this is the award!

Nevertheless, in the social sciences multilevel models are often used to analyze data on a limited number of groups with per group a large number of observations. A typical example would be the analysis of data on individuals nested within countries. By nature, only a limited number of countries exists. In practice, typical country-comparative analyses are based on about 25 countries. With such a small number of groups (e.g. countries), observations on a single group can easily be overly influential to the outcomes. This means that the conclusions based on the multilevel regression model could no longer hold when a single group is removed from the data.

In our recent publication in the R Journal, we introduce influence.ME, software that provides tools for detecting influential data in multilevel regression models (or: in mixed effects models, as these are commonly referred to in statistics). influence.ME is a publically available R package that evaluates multilevel regression models that were estimated with the lme4.0 package. It calculates standardized measures of influential data for the point estimates of generalized mixed effects models, such as DFBETAS, Cook’s distance, as well as percentile change and a test for changing levels of significance. influence.ME calculates these measures of influence while accounting for the nesting structure of the data. The package and measures of influential data are introduced, a practical example is given, and strategies for dealing with influential data are suggested.

With this publication, and of course with the software that was available for quite some time, we hope to contribute to a better usage of multilevel regression models. The provided example and guidelines were geared towards applications in the social sciences, but are applicable in all disciplines.

On a final note, the editorial of the R Journal describes how this journal is quickly ranking up in the degree of (academic) recognition it receives:

Thomson Reuters has informed us that The R Journal has been accepted for listing in the Science Citation Index-Expanded (SCIE), including the Web of Science, and the ISI Alerting Service, starting with volume 1, issue 1 (May 2009). This complements the current listings by EBSCO and the Directory of Open Access Journals (DOAJ), and completes a process started by Peter Dalgaard in 2010.

More information on our influence.ME software is available on this website.

Download the paper from the R Journal
Rense Nieuwenhuis, Manfred te Grotenhuis, & Ben Pelzer (2012). Influence.ME: tools for detecting influential data in mixed effects models R Journal, 4 (2), 38-47

In the application of multilevel models in country-comparative studies, however, it has long been overlooked that on the country-level only a limited number of observations are available. As a result, measurements on single countries can easily overly influence the regression outcomes.

Diagnostic tools for detecting influential data in multilevel regression are becoming available (including our own influence.ME), but what are your experiences with influential cases in country-comparative (multilevel) studies? How do you deal with influential cases if you encounter them?

Personally, I think doing a good PhD means:

Doing a PhD represents several years of supervised training, developing oneself to become a researcher capable of independently contributing to, and participating in, a scientific discipline.

Contributing to a scientific discipline means that a PhD candidate is supervised to create scientific products of the highest possible quality, and does so in an increasingly independent manner. Participating in a scientific discipline entails presenting these scientific products to others, frequently discussing these with colleagues, and collaborating with representatives of that discipline.

The core of that statement is being trained to independently contributing to a scientific discipline.

Based on my (clearly limited and selective) experiences, I learned that in New York (US?) PhD students really are … students. This by no means is a negative comments, but it is clear that the PhDs I met have a different set of skills. Generally, my impression is that their course load results in them being familiar with a wide overview of the literature on several topics, but (at least during the first few years) are less experienced in doing research with some level of independence. It’s a trade-off: some of the exams seem to be gruesome, with students faced with the taks of familiarizing themselves with a whole field of research. This is well beyond the relatively simple exams we have in the Netherlands, but getting your hands ‘in the dirt’ while doing some actual research yourself also is quite the experience.

Also, I noticed that it is much more of a struggle to do your PhD in the US. In New York life is expensive, and most PhDs are not employed by their universities as in the Netherlands. This means you have to find yourself some funding, but also that it really is a disappointment if your proposal is rejected. Not getting those funds really means you have to rethink your options. Many PhDs also need to have a job – besides writing their thesis, that is. Many teach or assist a professor, tasks that we do in the Netherlands as well. The difference, I think, is that the workload (again, besides writing a thesis which is a pretty decent workload by itself) is much higher in the US.

If every individual is under quite a lot of pressure, I wonder how that works out in terms of being able to form an academic group. I learned of one example of a PhD only providing to help to others when being paid on an hourly rate. I understand where that’s coming from, but also wonder how it affects the livelihood of an academic community of shared ideas, discussion, and collective interest.

The premise of my project was something that is commonly believed amongst sociologists – and something I took for granted for many years. I even remember when I first learned about it during a first-year course on inequality. Simply put: because spouses’ earnings are positively correlated, the earnings inequality between households is thought to be bigger than the earnings inequality between individuals. Intuitively, this makes sense: it is easy to imagine how the earnings gap between households with two top-earners and households with two low-income spouses is quite big. Recently, a new book by Esping-Andersen (The incomplete revolution) is getting quite some attention, and is reporting on how increased women’s earnings have contributed to income inequalities between households in several countries.

I thought it would be very interesting to delve into this issue more, and try to analyze differences between countries in the degree to which women’s earnings affects the inequality between households in these countries. Interestingly, though, once I started working seriously on the project, it quickly became clear that women’s earnings attenuate – rather than increase – inequalities between households. Virtually all authors analyzing the contribution of women’s earnings to household inequalities report this attenuating affect, and do so for various countries and for different points in time. So, the common conception that women’s earnings generally increase inequalities, turned out to be a misconception. No need to be concerned of this unintended consequence of women’s increased labour force participation.

So, I had to revise my plan. I turned my attention to a systematic review of the literature with a special focus on how it is possible that spouses’ earnings are positively correlated (which, indeed, they are), but that when women’s earnings are high the inequalities between households tend to be reduced. I won’t spoil the anticipation by disclosing my results here (but please, do contact me if you want to be informed when these results become available), but I figured it out. Also, I am still working on a systematic analysis comparing countries with very interesting preliminary results (again, no spoilers).

This concludes the most technical part of this series of blogs on my New York experiences. Tomorrow some notes on how PhD students are trained in New York.