Are U.S. Planning Programs “Diverse”? (Part II)

My previous blog post discussed the racial and ethnic diversity of graduate students in U.S. urban planning programs (see Are U.S. Planning Programs “Diverse”?). I received some very insightful feedback, mostly supporting the idea that we need to better define what “diversity” means if planning academics and planning professionals consider this a priority. I wasn’t accusing PAB of intentionally focusing on “whiteness” in their description of “student diversity,” rather, I was pointing out how diversity ends up being perceived relative to the racial/ethnic categories that are used to report student and faculty composition. The fact of the matter is that skin color dominates the “diversity” conversation.

The second part of my descriptive analysis of program racial/ethnic diversity focuses on faculty, as well as a comparison of faculty to students. It’s no surprise that faculty members of urban planning programs are predominantly white. Data from PAB’s Annual Report Online Database (AROD) show the breakdown of full-time, part-time, and adjunct faculty for planning programs.[1] Out of 1,806 total faculty, nearly 80% are white. This figure is composed of 71% white full-time faculty, 77% white part-time faculty, and 85% white adjunct faculty. Figure 1 illustrates a stark gender imbalance as well as the large numbers of white adjuncts hired by planning programs. The predominance of white adjuncts may be a reflection of the planning profession (i.e., the available pool) or who we choose to hire. While the proportions of white full-time faculty and graduate planning students are quite close (71% and 70% respectively), the gap widens with respect to part-time and adjunct faculty.

Figure 1

Figure1a

Figure 2 shows a relatively flat distribution with about half of planning programs having 80% or greater white faculty members and less than one-fifth having under 70% white faculty members.

Figure 2

Figure2a

Comparing program proportions of white faculty and students shows a significant positive correlation between the two groups (see Figure 3). It would be interesting to assess the role of recruiting resources and other institutional factors that may be influencing this relationship. It would also be interesting to see how/if this relationship has changed over time.

Figure 3

Figure3a

As shown in the post on student racial and ethnic diversity, I also looked at the numbers of race/ethnicity categories represented by planning program faculty members. This is a crude measure of variation because it doesn’t account for the evenness of numbers between categories, however, it is used here for illustrative purposes. The numbers of categories are slightly different for the faculty data from the AROD and the data from the ACSP Guide. The AROD includes 9 race categories for faculty:

  • White
  • Black or African American
  • American Indian or Alaska Native
  • Asian
  • Native Hawaiian and Other Pacific Islander
  • Some Other Race Alone
  • Two or More Races
  • Unknown
  • Foreign

The ACSP Guide includes 8 race/ethnicity categories for students:

  • Hispanics of Any Race
  • White
  • African American
  • Native American/Pacific Islander
  • Asian American
  • Mixed
  • Other/Don’t Know
  • Non-US Citizens

Figure 4 shows the relationship between the numbers of faculty and student categories represented by planning programs. Similar to Figure 3 there is an observable positive correlation between the numbers of groups represented by planning faculty and the students within programs. The upper right-hand corner highlights 13 “diverse” programs that have at least 5 groups represented by both faculty and students. Table 1 shows the list of these schools.

Figure 4

Figure4a

Table 1 – Racial/Ethnic categories represented by faculty and students

Table1a

Your comments are appreciated.

[1] I combined these data with data from the ACSP Guide used in the student analysis. This resulted in complete data for 65 U.S. planning programs.

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Are U.S. Planning Programs “Diverse”?

For some time the planning profession and planning educators have shown concern about being too “white”. Our country continues to experience significant demographic changes, especially in terms of race and ethnicity, most notably becoming less “white”. The Planning Accreditation Board (PAB) has emphasized that planning programs should be racially diverse, which translates into being less “white” and/or more “non-white” – for the purposes of better representing the populations we serve. Regarding student diversity PAB language states:

“Student diversity: The Program shall adopt appropriate recruitment and retention strategies, including curricular strategies, to achieve its aspirations for a diverse student body, and shall document actual progress in implementing those strategies. The Program shall foster a climate of inclusivity that appreciates and celebrates cultural difference through its recruitment and retention of students. Students shall possess, in the aggregate, characteristics of diversity (e.g., racial and ethnic background) that reflect the practice settings where graduates work or where professional needs exist in the Program’s region of recruitment and placement. Notwithstanding, the demographic mix is not a static concept, and all planning programs should seek to be in the forefront of a diverse society.”

It can be argued that “racial and ethnic background” is a very narrow indicator of student diversity and should be reconsidered in light of the broader concept of diversity. Why don’t we include sexual orientation, political perspectives, socio-economic background, musical tastes, etc? Fostering inclusion involves self-identification which may be seen by some as potential for discrimination. As planning programs consider their relative student diversity (by PAB criteria), I thought it would be interesting to look at program-level metrics on race and ethnicity. According to PAB, of the nearly 5,000 students in accredited programs, 69.5% (full and part-time) are non-Hispanic white, which compares to 62.6% for the U.S.[1] In addition, these programs are 53% male and 47% female. So planning programs have some work to do. The aggregate numbers tell part of the story, and the question then becomes whether the unit of analysis is the discipline or individual programs.

Using the assumption that many programs draw from within and supply planners to their own states, I compared each program with the corresponding percentage of white residents in their state. Analyzing the race and ethnicity data for graduate students of ACSP member schools using the Guide to Undergraduate and Graduate Education in Planning (20th Edition). I selected the 96 member schools in the U.S. of which 84 provided complete data. This is admittedly a simplistic approach, however, I think it provides context for further discussion about program-level diversity. There is also the question whether the benchmark should be race-ethnicity at the national, state, regional, or workforce scale.

Figure 1 shows the weak correlation between state % white and program % white and Figure 2 shows the distribution of variance between state % white and program % white for the 84 programs included.

Figure 1

Figure1

Figure 2

Figure2

As mentioned, program demographics (% white only) is weakly correlated with state demographics (see Figure 1). This is because planning programs differ in the number of out of state students (including international students) enrolled, even for programs within the same state. Also evident are outliers like Historically Black Colleges and Universities (HBCUs) such as Alabama A&M, Texas Southern, and Jackson State; states with high proportions of white residents like Utah, Iowa, and Maine; and elite schools like Harvard, MIT, and Penn. The “orange zone” shown in Figure 2 includes planning programs with proportions of white students that vary from their state proportions by 30% or more. Based on “whiteness” these programs qualify as hyper-diverse. The results for this group are driven by having much lower percentages of white students compared to their state averages. Whether these 19 schools represent diversity is open to interpretation (see Table 1).

Table 1 – Orange zone programs

Table1

On the other hand, schools that fall into the “green zone” shown in Figure 2 are those within 10% absolute difference from their state proportion of white residents. These include the 23 schools shown in Table 2. It should be mentioned that the 30% and 10% thresholds were selected arbitrarily for illustrative purposes and aren’t related to any particular standards. In addition, based on the available data, only 5 of the 84 schools included had margins greater than 10% above their state averages (see Table 3).

Table 2 Green zone programs

Table2

Table 3 Below state level

Table3

The metrics are an obvious problem here. An appropriate measure would take into account representation across identified groups. Two schools, UCLA and UC Berkeley have students that represent all 8 categories used for reporting student race/ethnicity. Eight other schools represent 7 of the 8 categories. Overall, 56 of the 84 schools represent 4 of the 8 categories (see Figure 3). I’m not recommending this as a diversity measure, but instead using it for descriptive purposes.

Figure 3

Figure3

In addition, a pressing issue that will be discussed in a follow-up blog post is planning faculty diversity. Data from the ACSP Guide show significant disparities with two-thirds of faculty being male and over 80% being white. This is the product of different dynamics that are being faced by institutions that will require concerted efforts to remedy.

Your comments are welcome.

[1] Source: U.S. Bureau of the Census, County Population Estimates by Demographic Characteristics – Age, Sex, Race, and Hispanic Origin; updated annually for states and counties. http://www.census.gov/popest/counties/asrh/. 2010 Census of Population and Housing for places; updated every 10 years. http://factfinder2.census.gov.

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Diffusion of Computer Technology in California Local Government Planning Agencies

I recently dusted off my master’s thesis (typewritten) and scanned it into a PDF file.  The topic, technology adoption by urban planners, is still of interest to me but the types of technology have changed dramatically since 1985.  For my thesis I surveyed all California city and county planning agencies to ask about their adoption of computer technologies.  With a quite respectable response rate of 81% (403 out of 497) I was able to show the state of computer use by planners, which existed in only about 60% of planning offices at the time.  It was also interesting to note that nearly half of the planning offices felt computers were only having a moderate impact on the planning profession.  I have to assume that much has changed in the past 30 years.  (Click to view thesis).

citiescounties

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Mapping the Knowledge Domain of Planning by Tom Sanchez and Nader Afzalan

25modeAs we know, the field of urban planning is far reaching in breadth and depth. This is due to the complex nature of cities, regions, and associated development patterns. Referring to the ambitious field of urban planning, Aaron Wildavsky famously remarked, “If planning is everything, maybe it’s nothing” (Wildavsky, 1973). Is planning everything? And what does that mean for someone trying to understand planning? Using the recent Guide to Undergraduate and Graduate Education in Urban and Regional Planning (20th Edition, dated 2013) published by the Association of Collegiate Schools of Planning, we examined the question of “what is planning?” by analyzing the areas of expertise and interests for over 900 regular faculty listed in the Guide. These are self-reported areas of teaching and research interests that can be used to characterize contemporary aspects of planning. Rather than just reporting the frequency of topics mentioned across planning faculty, network analysis was used to illustrate the range and interconnections between topics. The results are used to report the knowledge domain of U.S. planning faculty (see the full paper here).

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The TechniCity MOOC: An Interview with Tom Sanchez by Philip Young

Last summer I wrote a brief post about access to scholarship in a MOOC co-taught by Tom Sanchez, Professor in Urban Affairs and Planning, shortly after the course’s initial offering. After TechniCity was offered again this past spring, I thought I would ask Tom more questions about the course. Read more…

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TechniCity MindMixer Data Available

Several participants of the TechniCity MOOC have asked for the raw data of comments and other activity from the MindMixer discussion forum.  An Excel file of all activity is now available for download here.

Please be sure to share the results of your analyses with the rest of us and post on Twitter using #technicity or on the TechniCity LinkedIn page.

 

 

 

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2014 Urban Planning Citation Analysis

At the end of 2013, I posted an initial citation analysis for urban planning academics using Google Scholar citations (see: bit.ly/1lNyHR8). I also emailed the individual data to each of the faculty included in the analysis to get their feedback. I would like to thank everyone who responded confirming or correcting their information, as well as those providing comments and suggestions. The results below summarize a more complete analysis that is being prepared for publication. There are a few things I would like to mention, each being discussed in much more detail in a forthcoming article.

  1. The previous analysis was based on the 2011 ACSP Guide to Undergraduate and Graduate Education in Urban and Regional Planning.  This analysis uses the 2013 Guide (19th Edition). All schools and regular faculty (i.e., tenure track) in the Guide are included.
  2. Citation analysis is one of several ways to gauge faculty productivity and impact. Teaching, service, funded research, etc. are other facets of what we do, but are not accounted for. Therefore scholarly publication is the focus here.
  3. Google Scholar citation counts (as of March – May 2014) were used for the analysis. There is some debate about the accuracy of Google Scholar versus Scopus or Web of Science, about which I will provide a complete discussion in an upcoming publication.
  4. Traditional citation analysis includes books, chapters, and journal articles – materials commonly controlled by publishers. The nature of “citations” is changing and Google Scholar reflects this by including some non-traditional citation types.  I argue Google Scholar is quite appropriate for the field of planning. (See an earlier discussion in my paper from the Journal of the World Universities Forum).
  5. School/program rankings are based on median values (instead of mean) to control for outliers. For instance, Arizona State University has the highest mean number of faculty citations (one of their faculty members has the highest number of citations among planning programs), but is ranked 12th using the median number of citations.
  6. While I am quite confident in the results, there are inevitable errors in the data attributable to the following:
    1. Citation data are dynamic and change daily. What is presented below is a snapshot at a particular point in time.
    2. The uniqueness of author names influences the accuracy of citation counts. Common names, name changes, non-use of middle initials or middle names (by authors or publishers), misspellings, and other parsing errors can lead to improperly attributed publications.
    3. The number of citations per year is used to control for the age of faculty members. Programs with older faculty are expected to have greater numbers of citations – just by virtue of having more time. Unfortunately, the exact year that a faculty member started their academic career is not known, so the year they obtained their terminal degree is used as a proxy. There is also no data available on leaves taken or time off.
    4. I use data directly from Google Scholar Citations where faculty have existing profiles. These are assumed to be correct and contain only publications authored or co-authored by them. There were 202 profiles out of the 923 faculty included in the analysis.

I welcome your questions, comments, and suggestions.

Table 1 list the top 25 planning schools based on the median  number of GS citations per faculty member. Table 2 lists the top 25 in terms of citations per year of service (or year since degree) to account for faculty age or experience.

table1

The citation data can also be compared by the school where each faculty member received their terminal degree (usually a PhD). Table 3 shows the top 25 universities (not necessarily planning degrees) in terms of median total citation output.

table2

Although there is a significant amount of variation among individual planning faculty citations that effect department-level performance, there are distinct trends based on seniority. Figures 1 and 2 show both increasing mean and median citation totals by years of experience and rank.

table3

Figure 1.

years

Figure 2.

rank

Finally, there are many planning faculty with citation totals far exceeding the average levels discussed (the top 25 are shown in Table 4). All of the previous summary information for planning schools is based on GS citation totals for individual planning faculty. These totals will change over time as the data are corrected and updated as previously mentioned.  Please direct your questions or comments to me at: tom.sanchez@vt.edu.

Table 4. Top 25 cited planning faculty

Rank Name Citations
1 Luc Anselin 35,470
2 Michael Storper 23,431
3 James W. Varni 20,209
4 AnnaLee Saxenian 17,771
5 Robert Cervero 14,987
6 Harry W. Richardson 12,370
7 Neil Brenner 11,318
8 Martha Feldman 10,472
9 John Forester 10,125
10 Reid Ewing 9,386
11 Lawrence D. Frank 8,954
12 Michael K. Lindell 8,546
13 John M. Bryson 8,219
14 George Galster 8,006
15 Michael Dear 7,729
16 Meric Gertler 7,162
17 Susan Fainstein 6,927
18 Nik Theodore 6,414
19 Alan Murray 6,086
20 Lawrence Susskind 5,988
21 John R. Pucher 5,619
22 Jennifer Wolch 5,329
23 Marlon Boarnet 5,321
24 Geoffrey Hewings 5,316
25 Rob Shields 5,163

*This table was updated on 10/9/2014 due to an error detected in Google Scholar citation counts.

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