The CSH economist was honored for his research on how the bombing of Vienna in World War II shaped the city’s social makeup in the long run. Unlike in London, Vienna’s rebuilt neighborhoods stayed socially mixed.
Philipp Hilmbauer-Hofmarcher was recognized by the Austrian Economic Association (NOeG) at its annual conference for his “outstanding work.”
In this interview, he talks about his study, what cities can learn from Vienna’s reconstruction, and where his research is heading now.
Q: Congratulations, Philipp! In a nutshell, what is your study about?
During World War II, bombing destroyed many buildings in Vienna. That cut the housing supply and caused a shock to the city’s housing market. I used this shock to study its long-term effect on the socioeconomic makeup of the city’s population.
Q: Why is that an interesting question?
In London, for example, The London Blitz destroyed large parts of some neighborhoods. They were replaced with relatively cheap public housing. To this day, people from lower socioeconomic backgrounds live there and pay much lower rents than in the areas around them. I wanted to know whether Vienna was different.
Q: Was it?
Yes. In the areas that were bombed most heavily, both public and private housing were used to rebuild. Today, these neighborhoods have a mixed population. You can’t really tell bombed and non-bombed areas apart in terms of income or social mix. The only difference I found is a much higher density of small and medium-sized businesses. That’s because some of the rebuilt houses held more people, which means more demand for hairdressers, small shops, and so on.
Q: Did anything surprise you?
Two things. First, everyone knows the stereotype: Vienna means public housing, the famous Gemeindebau. Public housing did play a role. But many of the destroyed buildings weren’t replaced by municipal housing. Instead, the city partly gave out interest-free loans to support private rebuilding. So the same kind of people who lived there before moved back in, and there was no complete shift to a different social class. I found that very surprising, because I would have assumed the city did it all itself.
Q: And the second?
There is almost no effect on income. In London, average incomes in bombed and non-bombed areas differ by about 10 to 15 percent. In Vienna, the difference is less than one percent. Other cities that rebuilt after a disaster saw a huge impact, too. Boston, after its great fire in the 1870s, ended up with many more wealthy residents, and London ended up with many more poor ones. In Vienna there was hardly any effect: the same society, the same groups as before. That’s what makes it so interesting.
Q: Why does this matter today?
Many cities are facing the question of how best to rebuild, whether after war and destruction or as part of urban renewal and modernization. The idea behind Vienna’s reconstruction was to subsidize private builders while also building publicly, and to keep neighborhoods as mixed as they had been before. They didn’t rebuild everything according to one standard template. The subsidies were conditional on upgrading the apartments with features such as central heating and running water to make them more livable.
The result is that people from the same milieu don’t all end up living in the same areas. That’s a long-term development, and a desirable one.
Q: So the lesson is: build mixed.
Exactly.
Q: Your background is in economics. What drew you to complexity science?
Mostly the interdisciplinarity. I studied economics throughout my bachelor’s, master’s, and PhD, but I missed the broader perspective. In urban economics especially, you need insights from fields like sociology, economic geography, and urban planning to see the full picture. So when Frank Neffke was looking for someone for his project, joining CSH felt like a natural fit.
Q: How has your research changed since then?
The common thread is still economic geography and labor market effects. My first two PhD papers were partly about migration too, although historical migration. Our project at CSH is again about the labor market and about how migrants integrate into it. What’s new, and very exciting, is the network side. Many of the outcomes we look at are about how your network has changed and whether it helps you find a new job.
Q: What exactly are you currently studying?
We look at how the Austrian labor market differs for immigrants and for Austrians. My part asks: if you lose your job through a mass layoff or because your company closes, how quickly do you find a new one? How good is that job, and how much do you earn? And how does this differ for Austrians and non-Austrians?
Q: Where do networks come in?
If your network is mostly made up of the migrant community, the people who can help you find a job have very different jobs themselves than they would if your network were mostly Austrians. It also makes a difference whether you live in a big city or in the countryside. We want to understand these dynamics.
Q: How do you measure that?
I use microdata just as I did in my previous research, but we do something completely different with it. Before, the questions were how much you earn, which company you worked for, and which one you work for currently. Now we ask: Who did you work with? How many of your colleagues moved with you to a new company? And how many former colleagues had already moved on and can now tell you, “I’m at company XY, you could start with us, we’re looking for someone with your skills”? We look at who you’ve known over the past 15 years and whether they can help you get a job. That’s completely new for me.
Last Q: What would you like to have found out or achieved five years from now?
First, from this project: I’d like us to know better how to support people who are new to the Austrian labor market and don’t have many contacts yet. Where should they go, and what helps them most to integrate? Second, and this is the broader focus of our research group: how will technological and demographic change affect Austria’s labor market, from migration to AI? I think that will keep us busy for at least the next ten years.
