mike bader

In the commotion of moving and starting my new job, I neglected to post about two articles that came out last month that I worked on for quite a while. The first, Reassessing Residential Preferences for Redevelopment, was published in City & Community last month in a special issue on gentrification. My paper argues that much of our public policy and debate regarding changing residential preferences for gentrification occurs without actually measuring preferences in the population. Using the 2004-5 Chicago Area Study, I do just that to show that preferences break down along groups defined by home ownership. Home owners in the city of Chicago, regardless of race, are much more likely than their suburban counterparts to consider a redeveloped neighborhood. Meanwhile, race tends to unify preferences among renters in that blacks -- regardless of whether they live in Chicago or suburban Cook County -- would consider redeveloped neighborhood much more than their white renting counterparts, with Latino renters in between. I also find that traditional reasons middle-class people prefer redeveloped neighborhoods touted by gentrification and creative class proponents only really apply among whites while black home owners prefer access to city services and Latinos prioritize access to employment.

To the extent that cities hold developers accountable to mixed-income plans, these results suggest that redevelopment might help integrate communities economically and racially. Of course, this means actually holding developers accountable, which is sometimes difficult to do. Overall, the debate regarding who would prefer to live in redeveloped neighborhoods needs to be more nuanced and not based on where people do live.

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I am moving with my family this week so that I can start my new position at American University in the Department of Sociology and Center on Health, Risk and Society. I am very excited to be starting this new position being with great new colleagues, being in a wonderful academic and policy environment, and moving back to the area where my wife and I grew up. In addition to a much needed, if only possibly well deserved, vacation last week posts have necessarily been a little slow.

As a scholar of residential mobility, it is very strange to be going through a move myself. Throughout the process, I attempted to remain as reflexive as possible to learn from my own experience and how that might help inform my work. Although nothing I did would constitute "real" research that could be written up and published in a journal, I felt that I was a true participant-observer in the process. The process certainly informed my understanding of the ways that the housing search is an intensely sociological as well as economic process given the bounded rationality of deciding what we could afford, the importance of social networks in overcoming information externalities, and the cultural component of purchasing a home that is often neglected. I have no doubt that these insights will inform my work on the topic, particularly as it highlights the need to understand the social and cultural context of the housing search in addition to traditional economic models.

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I often joke that corporations want to hurt puppies, kittens, or children for profits. Unfortunately, a recent press release by the National Grocers Association (NGA) makes this a little less funny by actually arguing that profits should be valued over children's health. The press release responds to an inter-agency request for comments regarding marketing food to children.

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While writing scripts is one of the most important skills for reproducible quantitative sociology, the typical convention is to pick up the skills through more experienced colleagues in graduate school or at the workplace. Below are a few tips that I have learned from others, picked up on my own, or otherwise accumulated in my arsenal of tricks that I thought that I would pass along. There are great resources out there, but I thought it would be helpful to pass along what I think are the most important and helpful tips.

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Rolf Pendall posted a short, interesting piece on the suburbanization of poverty at the Urban Institute's new Metro Trends Blog. In it, he questions the basis of determining cities from suburbs in the service of understanding the "suburbanization of poverty."

His criticism stems from the ambiguity of defining suburbs and cities based on their urban design and physical infrastructure. He demonstrates this ambiguity through examples of Houson, Texas (a city with extensive sprawl); Fremont, California (a suburb with its own employment base and denser development than Houston); and Silver Spring, Maryland (an inner-ring suburb with all of the accoutrements of urban living).

His question is valid and one we face often in our work on New York: how relevant is work on New York City for the rest of the country. Just to provide an example of my own, the picture below that looks much like the Silver Spring neighborhood my family is preparing to move into is actually in New York City.

View Larger Map

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I believe firmly in the importance of research to inform policy based on observed facts. But, sometimes art expresses truth better than research ever could. I present the following as evidence of art capturing the essence of the Whole Foods habitus.

The video is of course meant to parody the cultural conventions of Whole Foods; but, I think that it really speaks to larger truths about culture, food, and inequality. While the video pokes fun at the Whole Foods consumer, I think that it accurately reflects how out of touch a vast swath of relatively privileged Americans are regarding the real struggles of poorer and many minority residents face when attempting to eat a healthy diet. Forget the fact that kombucha isn't on the shelves, many can't find produce or unspoiled meat as Dan Rose, my friend from graduate school documents in this piece Detroit neighborhoods where they are even lucky if they have grocery stores.

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After much hard work on the part of very talented people, the website for the Chicago Community Adult Health Study launched this week! For those who do not know about the project, it is an excellent dataset to examine influences of neighborhood environments on health outcomes among adults. The sample comes from all 343 Neighborhood Clusters in the city of Chicago, which allows a wide range of analyses across neighborhood environments. In addition to the survey of informants, there is also very rich data on the physical aspects of the neighborhood environment based on systematic social observations in all of the 343 Neighborhood Clusters.

I took advantage of this in part of my dissertation to explore how small-scale predictors of neighborhood disorder could be constructed from the sample of systematic social observations. Combining the insights from Steve Raudenbush and Rob Sampson regarding the benefit of using multiple items to rate characteristics of the neighborhood environment like disorder and the benefits of the geostatistical method of kriging to measure small-scale changes in the environment, I created a smooth surface of physical disorder across Chicago. Based on this method, one is not limited to pre-defined definitions of neighborhoods because block-level estimates can be reassembled into any configuration desired by the researcher. I then used this to show that observed physical disorder has a strong effect on residents' perceptions of neighborhood safety when we measure physical disorder at very small scales around a respondent, but almost no influence when we measure physical disorder at larger scales.

Of course, this is only one of the ways that I have used (and continue to use) this data. If you are interested, feel free to contact me if you want to learn more about the data -- particularly the systematic social observation component.

Programming in Stata is relatively straightforward and this is partly because the programming syntax is both powerful and relatively straightforward. There are, however, a few minor annoyances in Stata's language including using the backtick and apostrophe to indicate local macros (i.e.,`localname'). Among these shortcomings, I would argue that the lack of anything like a list in Stata's language is one of the largest.

In most langauges, you can store a list of items and refer to the item in the list by some sort of index. This is particularly helpful for iterating over the same step multiple times. Lists generally come in two flavors: lists to which you can refer to an item by its position in the list or lists which you can refer to by a keyword (called hash maps in computer science lingo). Stata's matrices can be used for the first, though doing so might become complicated if you want to do something besides storing basic numbers or strings.

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In the last step, we downloaded all of our data and deposited into directories that store this source data, backed it up, and write-protected the files. Now that we have done all of that, it is time to start working with the data! There is only one problem: almost inevitably, the data do not come neat, tidy, and ready to use. Often, the data contain major problems and need to be constructed in order to be usable. In this installment, I will write about managing files for cleaning, constructing and storing datasets.

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After establishing where my root directory resides resides, it is time to actually get to work. As with any endeavor, success begins by laying a solid foundation and with academic work that begins foundation is our data.

The most fundamental skill to academic success is asking good questions and acquiring data to answer those questions. Yet, in quantitative research, that skill is useless without the ability to manipulate data into useful formats that are capable of answering the good questions. Data cleaning, construction, and manipulation constitute well over half of my work on major quantitative projects.

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In my last post, I explained the value of a directory structure: consistent file management structures a disciplined workflow that increases productivity. The magnitude of its importance was a revelation that occurred largely after graduate school as the result of starting a new job.

When I moved to start my new job, I needed to move my files to my new computer. In transferring my files, I realized that my work that followed my well-defined workflow transfered easily, while the work that didn't follow the workflow did not.

The contrast between the ease with which I started the well-structured work and difficulty getting up to speed on disorganized pieces threw in sharp relief the importance of maintaining a workflow structured by a consistent file management system. For those well-organized projects the only difference being on my new computer was that I began work from a different "root directory".

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When I say that one of the most important things that I did in graduate school was set up a directory structure and workflow for my files, I am not kidding. Reading theory, learning statistical methods, and writing literature reviews were all important. However, just as important -- though not nearly as sexy -- is setting up a file structure and working directory.

Despite how trivial it sounds, maintaining a well-designed directory structure not only provides a framework for files, it structures productive work.

Given how important it was for me, I will attempt to explain the directory structure that I developed. Let me begin by saying that I am not an expert at developing directory structures. There are experts in these matters. Though I had an interest in becoming an expert at file management, I was too busy trying to become an expert in what I was actually studying to have the time. I will lay out in an ongoing series of posts the basic intuition behind my posts, what has seemed to work (and not) with this system, and improvements I would like to make. I would, of course, be interested in feedback and or comparisons to what others do.

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I am currently preparing a proposal for submission and one piece of information that the agency suggests is the power required to distinguish effects. This is obviously a perfectly reasonable piece of information to request; however, power calculations fall into that class of things that I know that I should know but I don't. It is one of those topics that every statistics book will tell you is important, but either a) glosses over the topic, or b) provides such a deep background that it is impossible to follow what the authors are talking about. Additionally, power calculations are complicated enormously by the fact that sample designs can become very complicated.

In contrast to this traditional treatment, Andrew Gelman and Jennifer Hill's book, Data Analysis Using Regression and Multilevel/Hierarchical Models, provides a very clear description of simple power analyses, which -- thankfully -- is all that I really need for this project. To make sure that I don't forget, I record below how to find the required sample size, n, for varying levels of between-group effect differences, Δ, at 80% power. The formula is relatively easy (see pp. 437-447 for more info): (5.6σ/Δ)². Therefore, if I measure change in units of standard deviations, sd, then I can estimate the sample size n for each unit of change.

drop _all
range sd 0 1 41
gen n = (5.6/sd)^2

I can then make a graph of the expected sample size required for a standard unit change using the command twoway line n sd; or, alternatively, just print a table of numbers using list.

This week I gave two presentations on my work exploring the consequences of neighborhood change for the evolution of contemporary metropolitan racial and ethnic segregation. The first was at the University of Pennsylvania Sociology Colloquium, which focused slightly more on the substantive conclusions, and the second was presented at the Quantitative Methods in the Social Science seminar series at Columbia University and focused more on the methodological components of the work.

I did not publish the slides for these talks because I will likely be giving the talk again (no spoilers!); however, feel free to contact me if you would like more information about them.

My friend and colleague, Danny Sheehan was interviewed on WNYC's Brian Lehrer Show this week talking a map he designed that tracked the flow of residential mobility among Brian Lehrer listeners. Among 1,600 entries, his was selected as one of the 15 featured, and one of two people interviewed about his design on-air live. You can see a video his map here.

Since my research is about where people move, this is obviously more than of just passing interest to me and Danny's visualization of moves is an incredibly helpful tool to detect patterns of neighborhood change. I know this because Danny helped us with a project that I presented at ASA last month mapping where former residents of Robert Taylor Homes in Chicago moved after demolition of the project (in fact, he gives us a shout out around the 7:45 mark in the interview). Thinking about how to incorporate movement and the increasing availability of tools to do so can add a whole new dimension to residential mobility research.