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Month: October 2012

Ethnic job discrimination in the Netherlands

Published by demetriodor on October 29, 2012

I have more than one reason to care about job discrimination based on ethnicity in the Netherlands. A new study shows that there is plenty to worry about. In short, the researchers sent identical job applications varying only the name – Dutch vs. ethnic (Antillean, Surinamese, Turkish, Moroccan). The ‘Dutch’ applicants had a higher chance of being invited for a job interview. The effect is rather small in size (5-8 percentage points), but is robust and statistically significant. Furthermore, discrimination is greater for ethnic males (20 percentage points), and for the lower-educated. This study investigates ethnic discrimination in the Dutch labor market, using field experiments. Two thousand eighty applications were sent to 1340 job vacancies; one applicant had a Dutch-sounding name, the other a name that signaled immigrant descent. Our aims were (a) to test for the persistence of discrimination in the Dutch labor market; (b) to study the interactions of ethnic background with job characteristics; (c) to study the complexity of discrimination against a background of multiple group membership. Results indicate that discrimination continues to be a problem in selection procedures. Interactions with job characteristics and multiple group membership are discussed. [full text (gated) here] Andriessen, Iris, Eline Nievers, Jaco Dagevos, and Laila Faulk. “Ethnic Discrimination in the Dutch Labor Market: Its Relationship with Job Characteristics and Multiple Group Membership.” Work and Occupations 39, no. 3 (2012): 237-69.  

The hidden structure of (academic) organizations

Published by demetriodor on October 23, 2012

All organizations have a ‘deep’ hidden structure based on the social interactions among its members which might or might not coincide with the official formal one. University departments are no exception – if anything, the informal alliances, affinities, and allegiances within academic departments are only too visible and salient. Network analysis provides one way of visualizing and exploring the ‘deep’ organizational structure. In order to learn how to visualize small networks with R, I collected data on the social interactions within my own department and plugged the dataset in R (igraph package) to get the plot below. The figure shows the social network of my institute based on the co-supervision of student dissertations (each Master thesis has a supervisor who selects a so-called ‘second’ reader who reviews the draft and the two supervisors examine the student during the defence). So each link between nodes (people) is based on one joint supervision of a student. The total number of links (edges) is 264 which covers (approximately) all dissertations defended over the last year. In this version of the graph, the people are represented only by numbers but in the full version the actual names of people are plotted, the links are directional, and additional info (like the grade of the thesis) can be incorporated. Altogether, the organization appears surprisingly well-integrated. Most ‘outsiders’ and most weakly-connected ‘islands’ are either occasional external readers, or new colleagues being ‘socialized’ into the organization. Obviously, some people are more ‘central’ in the sense of connecting to a more diverse set of people, while others serve as boundary-spanners reaching…

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Correlation does not imply causation. Then what does it imply?

Published by demetriodor on October 9, 2012

‘Correlation does not imply causation’ is an adage students from all social sciences are made to recite from a very early age. What is less often systematically discussed is what could be actually going on so that two phenomena are correlated but not causally related. Let’s try to make a list: 1) The correlation might be due to chance. T-tests and p-values are generally used to guard against this possibility. 1a) The correlation might be due to coincidence. This is essentially a variant of the previous point but with focus on time series. It is especially easy to mistake pure noise (randomness) for patterns (relationships) when one looks at two variables over time. If you look at the numerous ‘correlation is not causation’ jokes and cartoons on the internet, you will note that most concern the spurious correlation between two variables over time (e.g. number of pirates and global warming): it is just easier to find such examples in time series than in cross-sectional data. 1b) Another reason to distrust correlations is the so-called ‘ecological inference‘ problem. The problem arises when data is available at several levels of observation (e.g. people nested in municipalities nested in states). Correlation of two variables aggregated at a higher level (e.g. states) cannot be used to imply correlation of these variables at the lower (e.g. people). Hence, the higher-level correlation is a statistical artifact, although not necessarily due to mistaking ‘noise’ for ‘signal’. 2) The correlation might be due to a third variable being causally related to the two correlated variables we observe. This is the well-known omitted…

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Solve for the equilibrium: Dutch higher education

Published by demetriodor on October 2, 2012

1) The number of first-year students in the Netherlands has soared from 105 000 in 2000 to 135 000 in 2011. The 30% increase is a direct result of government policy which links university funding with student numbers. In some programs in the country, student numbers have more than doubled during the last five years. Everyone is encouraged to enter the university system. 2) In the general case, there is no selection at the gate. Students cannot be refused to enter a program. 3) Now, the government’s objectives are to reduce the number of first-year drop-outs  and slash the number of students who do not graduate within four years. Both objectives are being supported by financial incentives and penalties for the universities. Something’s gotta give. I wonder what… P.S. ‘Solve for the equilibrium’ is the title of a rubric from Marginal Revolution.