The most recent job market seminar that I attended was from Alix Duhaime-Ross (who happens to have the perfect initials for a PhD student: A DR). Duhaime-Ross works on empirical microeconomics, an area of economics that has been popularised by books such as Freakonomics. This approach to economics is heavily data driven, and Duhaime-Ross focuses on labour economics and the economics of education.

Duhaime-Ross’ presentation focused on the effects of educating school-aged children in the dominant language (instead of a minority language). The context here is Bill 101, a law that was passed in Quebec in 1977 that required all children of immigrants to be educated in French language schools (previously a majority of immigrant children were educated in English). An exception was allowed for children of immigrants who had at least one parent who had been educated in English in Canada. This law is still in place today, so that if, for example, I moved to Quebec and had children they would be required to attend school in French. Any children who had already started school in English were allowed to finish their schooling in English, so the law only affected new students.

What effect did this law have on the children who would be educated in English without the law, but are not being educated in French? It might improve the student’s future employment prospects because they will have better French skills in a French speaking province, or it might harm them because they might do worse in a French school than they would have in an English school (perhaps leading to them not going to university, for example).

Normally a question like this would be very hard to answer because parent’s will usually try and make the best choices for their kids, creating a selection bias. This means that Duhaime-Ross can’t just compare English educated children in 1976 with French educated children in 1977, for example, because the characteristics of these children will differ (and other changes might confound things as well). What we really need to do is exploit the mandatory nature of the law, and create a natural control group to compare outcomes with.

Duhaime-Ross does exactly this. She begins by looking at a control group – those children with only one foreign born parent. These children usually have one parent who was educated in English in Canada, so are exempt from the law. The change in outcomes over the period of time when the law was enacted amongst this group of children forms a control group. The change in outcomes for children affected by the law can be compared to the change of outcomes in the control group to isolate the effect of the law.

But, there still might be some other changes that are not accounted for with this process. What if the changes in the law caused French school class sizes to become larger and therefore reduce the quality of French language education? This would bias the estimates from the previous paragraph. Duhaime-Ross therefore used a second control group consisting of native French speaking who would always be educated in French regardless of the law.

There are, therefore, three groups. Children who were educated in English both before and after the law change, children educated in French both before and after the law change, and a group of children who would have been educated in English before the law change but were forced to be educated in French after the law change. By comparing the outcomes of these three groups Duhaime-Ross can isolate the effects of the law change on the outcomes of the children affected by the law.

The outcomes that Duhaime-Ross used were taken from the 2006 Canadian census. What this means is that the outcome data is more than 20 years after these children finished school, so that we are looking at the long term effects of schooling language.

Duhaime-Ross found that the law was unambiguously good for the children of immigrants. Children whose education was in French (instead of English purely because of the law change) earned higher incomes, were more likely to be employed, and were more likely to have gone to university. The law was not, however, unambiguously good for the province of Quebec. The law caused some immigrant families to leave Quebec and move to other parts of Canada so that their children could be educated in English. The families that left tended to be better educated and earn higher incomes than those that stayed.

None of these effects were intended effects of Bill 101. The purpose of the bill was simply to protect the French language in Quebec (the bill also had a bunch of other provisions to protect French as well). Nevertheless, the bill improved the long-term outcomes of immigrant children but also cost Quebec some extra `high value’ immigrants. The lesson here is that new laws can have unintended consequences that can last decades. Understanding these effects is interesting but also extremely important, particularly for policy makers who might be considering similar laws in other jurisdictions.

So far I have attended two practice job market seminars; those by Zhengfei Yu and Jacob Cosman. Although their work is in rather different fields I will address them both in this single post because there are some interesting juxtapositions between their work.

Yu’s job market paper is a classic job market paper: it is ambitious and attacks a “big” problem in theoretical econometrics (the study of statistical techniques as applied to economic problems). Econometricians often use large data sets that have been collected by national statistical agencies (containing, say, quantities and prices and, if you are lucky, production costs for an industry). One of the assumptions that is usually made to facilitate data analysis is that the industry is in an equilibrium, and this equilibrium is unique.

Economic theory tells us, however, that often there are multiple equilibria that can exist. If there are multiple equilibria, but we assume that the equilibria is unique, then our conclusions will be wrong. Yu asks the question: can we tell, just from looking at the data, whether the assumption of a unique equilibrium is correct?

Yu finds that the answer to this question is yes, and he proposes a method to test whether there is a unique equilibrium or not. This is a great example of big picture econometric research.

Cosman, on the other hand, takes an approach at the other extreme. Instead of attacking a big problem in the abstract, Cosman attacks a very specific problem. In some sense, the answer to Cosman’s question is secondary: just as important are the advances in the techniques that Cosman uses.

Cosman works in urban economics and industrial organisation (the study of firm and industry level behaviour and outcomes). One of the hallmarks of empirical industrial organisation studies is getting a whole lot of mileage out of a paucity of data, usually by making a series of strong assumptions. Empirical industrial organisation advances by scholars pushing the limits and trying to find the minimal level of assumptions needed to get the most answers from a given data set.

Cosman studies night life in Chicago. Using a data set which is built chiefly from liquor licence applications and cancellations (which can be seen as entry of new bars, or old bars closing down) Cosman is able to estimate the costs of starting a bar in Chicago and how much consumers value new bars. If you are interested in social planning, or in Chicago in particular, then you are probably very interested in Cosman’s estimates.

For others though, the methodological innovations that Cosman introduces are more interesting. The main innovation is the introduction of a dynamic estimation technique that both simplifies the computational burden of solving the model and allows (realistically) for sequential decision making by potential bar owners.

Both Yu and Cosman have very interesting papers, and they showcase the broad range of techniques that can be applied to modern economic research. It doesn’t matter if you are answering broad abstract questions, or using novel techniques to  estimate demand functions for a single industry in a single city; it’s all economics, and it’s all pushing forwards the frontier of knowledge. But best of all, it’s all fun!

Economics is, as far as I am aware, the only academic field which has developed the infrastructure to facilitate a large scale coordinated market for hiring new PhDs into academic positions. Every year, in November, over a thousand PhD graduates apply to over a thousand jobs. In January all the North American universities, and most top international universities, conduct interviews in parallel with the annual American Economics Association meetings. Second round interviews run from January until March (or even April) and  usually involve flying the candidates out to visit the hiring university.

This year’s candidates from UBC are listed here. In November, the candidates give presentations to the department outlining their job market papers (generally the best piece of research that the candidate has produced during their PhD).

What is so interesting about all of this? Well, if you want to get a good handle on where economics is heading, looking at the research that the next wave of young researchers are producing is a pretty good place to start.

Throughout the rest of the month I will post up brief summaries of the job market presentations that I attend. I most likely won’t be able to attend all of them, but I will attend as many as I can.

So, check back throughout the month to get a taste of modern economic research across a broad range of fields and from a great group of young researchers.