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This report presents a methodology to classify skill requirements in online job postings into a pre-existing expert-driven taxonomy of broader skill categories. The proposed approach uses a semi-supervised Machine Learning algorithm and relies on the actual meaning and definition of the skills. It allows for the classification of more than 17 000 unique skill keywords contained in the Burning Glass dataset into 61 categories. The outcome of the classification exercise is validated using O*NET information on skills by occupations, and by benchmarking the results of some empirical descriptive exercises against the existing literature. Compared to a manual classification, the proposed approach organises large amounts of skills information in an analytically tractable form, and with considerable savings in time and human resources.

This article provides experimental support for the hypothesis that insurance can be a motive for religious donations. We randomize enrollment of members of a Pentecostal church in Ghana into a commercial funeral insurance policy. Then church members allocate money between themselves and a set of religious goods in a series of dictator games with significant stakes. Members enrolled in insurance give significantly less money to their own church compared with members who only receive information about the insurance. Enrollment also reduces giving toward other spiritual goods. We set up a model exploring different channels of religiously based insurance. The implications of the model and the results from the dictator games suggest that adherents perceive the church as a source of insurance and that this insurance is derived from beliefs in an interventionist God. Survey results suggest that material insurance from the church community is also important and we hypothesize that these two insurance channels exist in parallel.

This paper provides a first assessment of the degree to which public research contributes to innovative entrepreneurship, using data on start-ups and venture capital (VC). It looks at academic start-ups founded by recent undergraduates and doctorate students or researchers. It shows that academic start-ups represent 15% of all start-ups in the specific sample under scrutiny. Their share is higher in science-based technological fields such as biotechnology (23%). Across the majority of countries and technology fields, start-ups created by undergraduate students represent the highest share of all academic start-ups. As to their performance, start-ups founded by researchers are more likely to patent and those founded by students introduce innovations that are more radical compared to other start-ups. While start-ups founded by undergraduate students receive less VC funding and are less likely to exit via IPO or acquisition, those created by researchers are as successful as their non-academic counterparts.

Using data on the universe of elected politicians in French municipalities, this paper studies the impact of a gender quota law on the political representation of women and on the composition of municipal councils. The empirical strategy, a Difference-in-Discontinuities design, takes advantage of the fact that the policy applies to cities above a population threshold, and that this threshold has been modified over time. I find that the quota policy has a substantial impact on the share of female candidates and elected politicians, but fails to promote female mayors and list leaders, even in cities that have been exposed to the policy for 13 years. Women do not reach leadership positions because they are more likely to resign than male politicians. This higher propensity of women to leave politics is correlated with local gender norms concerning the place of women in society, and also varies with individual characteristics such as age and professional background. In a second part, I show that quotas have little effect on the composition of municipal councils in terms of socio-economic background, age, and political experience.

The report presents new cross-country descriptive evidence on innovative start-ups and related venture capital investments drawing upon Crunchbase, a new dataset that is unprecedented in terms of scope and comprehensiveness. The analysis employs a mix of different statistical techniques (descriptive graphics, econometric analysis, and machine learning) to highlight a number of findings. First, there are significant cross-country differences in the professional and educational background of start-ups’ founders, notably the share of founders with previous academic experience and in the share of “serial entrepreneurs”. Conversely, the founders’ average age is rather constant across countries, but shows a fair degree of variability across sectors. Second, IP assets, and in particular the presence of an inventor in the team of founders, are strongly associated with start-ups’ success. Finally, female founders are less likely to receive funding, receive lower amounts when they do receive financing, and have a lower probability of successful exit, when other factors are controlled for.