A fascinating, age-old U.S. public policy question surrounds the relationship between educational outcomes and segregation (e.g., income, racial). Over the years, growing economic literature continues to add nuanced perspectives to the issue of the interaction between school finances and the makeup of local communities. Social science researchers emphasize the importance of studying residential segregation, whether by income or race-ethnic groups, because of potential neighborhood effects on the long run educational outlook for young students. Since public education in the U.S. is largely financed by local property taxes, there is a large disparity between funding for schools in different communities.
Thomas Nechyba, “School finance, spatial income segregation, and the nature of communities”:
Although many education policy workers focus almost solely on the impacts of disparate per pupil expenditures across schools, a large body of economic research shows strong evidence that there are broader factors affecting educational inequities, and vice versa. For instance, since public school systems are based in local districts, residential segregation by income is more likely to occur—and over time this greater residential segregation feeds even larger inequalities, thus leading to a relentless cycle. Differences in education quality are capitalized into housing prices (i.e., property values often further decline in poor areas with inadequate schools).
Nechyba (2003) questions why the contemporary educational inequalities discussion is so restricted to per pupil spending gaps; rather, he calls for a more general examination of different school finance institutions and their various equilibrium effects. Thus, the paper sets up a structural model representing a decentralized economy in which households select their place of residence, where to send kids for education, and the degree of support to provide public schools; the model includes the most causal potential factors leading to income segregation. Using observations from New Jersey districts, Nechyba (2003) adjusts the underlying structural parameters until his model simulates realistic features from the data. Then, holding these parameters constant, policy simulations can be conducted. The framework accounts for a couple main sources of residential segregation by income: 1) different neighborhoods are historically endowed with disparate housing quality and neighborhood characteristics; 2) places of residence affect a child’s school quality since public education systems require households to live within exogenously outlined boundaries. However, the inclusion of private schools into this model complicates matters because households that send their children to private educations care less about public school quality near their homes; alternatively, they would be even motivated to live in a subpar public school district with lower housing costs. Since private school households are often wealthier, this counterintuitive phenomenon pushes the local economy toward income desegregation.
Subsequently, Nechyba (2003) use the structural model to find that state financing of school districts indeed dampens residential income segregation in an area.  This effect is expected because school systems that are purely locally financed give wealthier households motivation to segregate by income and thus shape better schools. However, two less intuitive findings are that the existence of a private school market leads to considerable residential income desegregation, and that in the presence of private schools, the existence of public schools actually tends to lower residential segregation—even though, by themselves, public school systems are associated with higher spatial segregation. However, a notable caveat to this study is that the model equilibrium overestimates the movement of private school households into poorer neighborhoods simply because the structural model does not account for non-school characteristics of such communities.
Raquel Fernandez and Richard Rogerson, “Keeping people out: income distribution, zoning, and the quality of public education”:
Meanwhile, Fernandez and Rogerson (1993) investigate the effects of community zoning regulations on per pupil spending, particularly with respect to property taxes and the formation of communities with average income differences. The analysis relies on simulations using a two-community model, where each community determines tax rates by majority vote, and households can choose their place of residence. Without zoning, the equilibrium of this model results in a poor neighborhood with a low tax rate (i.e., low per pupil expenditure) and a wealthier community with a higher tax rate. The study found that the existence of zoning regulations, meaning that households have to purchase a minimum level of housing in order to live in a predetermined area, is associated with the wealthy community decreasing in size and becoming richer, while the poorer neighborhood grows larger. The increased exclusivity of the rich neighborhood causes the lowest income households of that neighborhood to enter a poorer one, thus raising average income in both areas. As a result, the poorer community sees greater per pupil spending, but the change in education quality across the two areas is ambiguous.
Sarah Reber, “School desegregation and educational attainment for blacks”:
Although the first two studies mentioned did not delve into racial segregation, it is important to gauge the relationship between segregation and educational attainment for certain race-ethnic groups. Reber (2007) looks into the effects of the desegregation process after the Supreme Court’s 1954 Brown v. Board of Education decision, specifically focusing on schools in Louisiana. Previous studies showed that the desegregation policy had two effects: 1) increasing black students’ exposure to white peers in school and 2) raising the level of federal and state funding such that the average spending in predominantly black schools increased. Given that desegregation essentially eliminated disparities in student-teacher ratios for black and white students within previously segregated districts, Reber (2007) sought to determine whether greater educational attainment by blacks following desegregation resulted more from greater exposure to white peers or increased funding.
The simplest specification in this paper is a univariate regression that looks for an association between a county’s initial share of black enrollment and change in educational attainment from before and after desegregation; in this model, the dependent variable of interest is mean attainment for 1970-1975, minus the average attainment for 1960-1965. Later, the paper considers metrics such as the 12th grade continuation rate and adds controls for other characteristics such as change in county’s employment. These regressions on high school grade continuation and graduation rates indicate that greater educational attainment increased more for black students in districts with higher rates of black enrollment following desegregation—thus implying that, following desegregation, increased funding played a larger role than higher exposure to white students in raising attainment.
Growing literature contribute to U.S. policies designed to match educational opportunities of students coming from vastly different racial and socio-economic backgrounds. Whether through investigating the means by which certain communities are able to make use of taxes and zoning as instruments to keep specific segments of the population out of a school district, or by evaluating the effect of private schooling on income segregation, valuable insights can be drawn from these types of analyses. A thorough look at the institutional set-up of education can reveal the role that segregation and various school finance mechanisms play in long-run inequality.
Raquel Fernandez and Richard Rogerson, 1997, “Keeping people out: income distribution, zoning, and the quality of public education,” International Economic Review 38(1): 23-42.
Sarah Reber, 2007, “School desegregation and educational attainment for blacks,” Cambridge, MA: NBER working paper 13193.
Thomas Nechyba, 2003. “School Finance, Spatial Income Segregation and the Nature of Communities,” Journal of Urban Economics 54(1), 61-88, July.
 Thomas Nechyba, School finance, spatial income segregation, and the nature of communities, 66: It is notable that although this analysis focuses on income segregation, it can apply to problems involving racial segregation as well, “only to the extent that such segregation is driven by income differences.”
 Nechyba, 65
 Nechyba, 74: “While it might be expected that state financing will lead to less segregation than local financing, the relatively small magnitude of this effect compared to the huge effect of private schools is surprising, as is the different effect of public schools in a world with and without private school markets.”
 Raquel Fernandez and Richard Rogerson, “Keeping people out: Income distribution, zoning, and the quality of public education,” 1
 Fernandez and Rogerson, 32
 Sarah J. Reber, “School desegregation and educational attainment for blacks,” 3
 Reber, 6: To prevent white flight, schools with larger proportions of black students received more funding to “level up to the levels previously experienced only in the white schools”
This paper investigates the association between apartment rental prices in Durham and their linear distance to Duke University. Considering the substantial role Duke plays in the economic activity and housing demand, in particular that of apartments, in the city of Durham, one would expect a positive relationship between proximity to campus and the rental price of apartments. This paper aims to quantitatively examine whether or not such a relationship exists.
While not impossible, a comprehensive quality-adjusted approach to apartment prices for Durham apartments involves the matching of several datasets and is not utilized in this paper. An alternative is found in online ratings of apartments provided by review sites. Ideally, these reviews are proxies for the relative quality of accommodations and services of each given apartment. Once matched for distributions of ratings on different websites, average scores can be seen as directly comparable between apartments. Potential correlation caused by a bias towards higher quality closer to Duke’s campus because of the higher endowed wealth of Duke students, or lower quality caused by their bad behavior, can be accounted for using an interaction term.
An initial model is established using only apartment distance to Duke University as the dependent variable. Apartment listing prices are collected from Apartmentguide.com and separated by room class: one, two and three-bedroom offerings are counted separately, with maximum, minimum and average listing price for each room type provided. A mean price for each room type is calculated by taking the average of the maximum and minimum prices – note that this may not be an accurate estimate, since the composition of apartments by price for each room type is unknown. Distance values are estimated using a linear point-to-point approach. Apartment locations are obtained using the Bing Map geocoding API, and the East/West campus bus stops are used as point proxies for the two campuses, respectively. A third estimate uses the shorter of the two distances to the campuses. This allows for the possibility that because of the extensive public transit options between East and West, individuals may not have a particular preference for one campus but simply desire to live closer to one of the C1 bus stops.
For the regression model, natural log transformations are applied both to listing prices and distance estimates. A fairly strong association has been found between the prices of most of the room types and all three distance estimates. The strongest association occurs between the price of double apartments and their distance to West Campus, with a doubling of distance translating into a price decrease of 5.1%. All three regressions using the price of double apartments reports significance over 95% when regressed against distance estimates. The association between price of single apartments and distance is significant at the 90% level for both the distance to West Campus and the minimum distance estimate to both East and West Campus. One could speculate that single rooms, being not only more expensive but also less conductive to a social lifestyle, are not as preferable to Duke students as double rooms, leading to the weaker price association.
Coefficients for single-variable regressions between distance estimates and average price
Interestingly, the link between price of triple apartments and distance to campus is much weaker than that of double and single apartments. No distance estimate seems to be even slightly correlated with the price of triple apartments. Part of this could be explained by the relatively few number of apartment buildings that offer triple rooms. However, it is also possible that since triple room represent an inferior good compared to single or double rooms, Duke students with high average spending power would typically not choose to live in them. Some students might move off campus to escape a triple or double dormitory and have little interest in similar living conditions. It could also be possible that local families unaffiliated with Duke University are more likely to occupy the large units, in particular three-bedroom apartments.
A possible way to indirectly check the validity of this explanation is to see if triple room-offering apartments are, on the average, further away from Duke than single or double apartments. If students don’t care for triple-room apartment, they ought to be distributed more randomly in Durham and be, on average, further away from the Campuses. However, this does not seem to be the case for the apartment sample in the dataset. The average distance to West Campus for all apartments offering triple rooms is 9.01 kilometers, closer than the figures for apartments offering single and double rooms (9.07 and 9.35 kilometers, respectively). The average distance to East Campus displays a similar trend, at 8.33 kilometers on average for apartments that offer triple rooms, 8.58 kilometers for apartments that offer single rooms, and 8.87 kilometers for apartments that offer double rooms.
It is also possible that the price response of distance is not only non-linear but also somewhat binary in nature. Intuitively, apartments beyond a reasonable walking range will only be marginally affected by extra distance: the extra mile should matter very little if one has to drive to school anyways. To test this, dummy variables indicator certain levels of proximity to West Campus, starting from closer than two kilometers, were regressed against prices of double apartments. Indicators at the 2,3 and 4-kilometer level report significance at greater than 95% levels, while all coefficients of indicators until 14 kilometers show significance at greater than 90% levels. Double apartments that are closer than 2 kilometers to campus show a substantial, 28.9% price premium over those that are further than 2 kilometers away from campus. Double apartments that are closer than 3 kilometers to campus have a 16.3% premium. However, there is also a premium of 4-7% (6.2% on average) of apartments closer to campus than a range of distances from 5km to 15km.
Price difference between apartments within and outside radii 2-16 kilometers, 1 = 100%
A potential explanation for this behavior is that apartments very far away from campus are so grossly inferior in quality terms that everything else offered seems better in comparison. This does not seem likely, but a positive association between quality and distance might exist simply because the presence of Duke raises surrounding land prices and makes low-quality, low-margin apartments less profitable. Being far from Duke may also mean being far away from downtown Durham, which in itself implies a number of other negative influences on price. A third explanation is that even if an apartment is beyond walking distance to Duke’s campus, there can still be indirect benefits from being somewhat close to Duke. Better policing, the coverage of Duke transit systems, short driving distances to the Duke Hospital might all be factors that play a role in prices of apartments beyond the walkable range.
One issue to consider is that positive price effects of larger radii are much weaker with regard to single apartments and virtually nonexistent for triple apartments. Single apartments closer than 2km and 3km to West Campus enjoy a 22% and 14% respective price premium, which is comparable in absolute terms with those of double apartments. However, when the indicator search radius is expanded the positive price effect quickly disappears. Using search radii of 11 and 12 kilometers, double apartments within the radius enjoy a 7% and 5.1% price premium compared to those that are outside, both significant at the 90% level. Single apartments within the same radii, on the other hand, only enjoy a 3.3% and 0.8% price premium. Neither of these relationships are statistically significant. No regression model with triple apartment prices reports statistical significance at the 90% level, although the 2-kilometer indicator represents a 10.4% price premium (P=0.47) for triple apartments.
Concluding the results thus far, all three apartment types show some level of response to difference distance gradients. Prices of double apartments are the most responsive, while evidence for price responses of triple apartments is somewhat weak. If we take the average price premium level for double apartments between 5-16 kilometers as reasonable expectations for price of a resident not living within walking distance to campus, then the model suggests that compared to not being able to walk to school at all, living closer than two kilometers to west campus comes with a price premium of 22.7%. Living closer than three kilometers has a relative premium of 10.1%. The same respective rates for single apartments are 18.5% and 10.5%, and for triples 9.5% and 6.2%. However, the triple apartment figures are not statistically significant.
Frequency Plot of Ratings of Sample Apartments
To further develop the model, online ratings of apartments are added in as a control variable. Ratings are collected from several websites and matched by average value and standard deviation. Ratings from different websites are aligned not by absolute score but by standard deviations from the mean, capped at 0 and 100. Apartments with fewer than five ratings for all websites aggregated are not considered in the dataset to avoid misrepresentation. Apartments with ratings from multiple websites have a composite score derived by first matching scores by mean and SD and then averaging over all scores. Unfortunately, not all apartment groups in the dataset have at least five ratings. Out of the 238 total observations with price data and 119 total observations with at least five reviews, only 89 observations have both price data and the minimum number of rating scores. Scores are tallied at a maximum of 100 and minimum of 0, with an adjusted average score of 55.7 out of 100 for the 89 usable observations. The 25th percentile score is 35 and the 75th percentile score 84.3.
With the inclusion of ratings as a control, no distance gradient terms in any of the regression models report statistical significance above 90%. This is the case for all three distance estimations, three apartment types and radii 2 – 6 kilometers. Distances terms remain below the 90% level of significance after adding in an interaction term between distance and ratings. However, the coefficient of the ratings term is highly significant for all regression models. Regressing only the ratings term against price of single, double and triple rooms results in strongly positive connections. For 10 extra points in the rating score, there is a price premium of 1.82%, 1.81% and 1.13% for the three room types, respectively. Note that these effects are weaker than those associated with distance to campus.
Influence of distance estimates/indicators on apartment ratings and respective significance
The reduction of statistical significance in distance gradient coefficients after introducing ratings as a control suggests a relationship between distance to campus and ratings. This can be demonstrated by a variety of metrics. Using the full, 119-observation dataset of apartments with ratings, the correlation coefficient between average review score and log-distance to West campus and East campus is -0.206 and -0.207, respectively. Modelling distance preferences using a natural log transformation, a doubling of distance to West Campus results in an average expected rating decrease of about 8.2 points. However, it must be noted that this association is rather weak considering the large spread of apartment ratings, which reports a standard deviation of 30.6 points.
The association between distance gradients and ratings can be explained in several ways. The obvious rationale is that raters are taking distance into account when giving scores, boosting the scores of apartments closer to campus. It is also possible that there is an inherent bias in apartment quality, with higher quality apartments generally being built closer to campus. If Duke students do actually have higher spending power than the average apartment resident in Durham, developers could be selectively offering high-quality, expensive apartments at locations closer to school in response greater demand. Even if apartment quality is not inherently associated with distance, students could be on average less responsive to quality differences. This could be the case either because most students know that they will be moving out in the near future (upon graduation) and have little concern for quality factors, or because their social lives are centered on Duke’s campus regardless of how far away they live. If apartments closer to Duke have larger student populations that only view the apartment as a place to shower and sleep, scores of bad apartments close to Duke could be buoyed even if such groups do not care about distance.
It is not difficult, at least in principle, to test for these explanations. However, currently available data do not offer a straightforward way to introduce controls beyond rating scores. Several websites do offer extra information about size and amenities, but the total number of observations in the dataset that have such information is small. Future research on such issues could focus on the obtaining of apartment quality data and geospatial quality variables such as crime rates and distances to public utilities. The distance estimate itself could also be improved, for example using actual walking/driving distance estimates instead of linear distance to campus. It might also be useful to model Duke Campuses as shapes with different points of priority (bus stops, Bryan Center, etc.) instead of a single point.
In conclusion, this paper has provided evidence of distance gradient effects on apartment prices in Durham. On average, a 100% increase in linear distance to west campus results in a 3.6% price decrease for single apartments and a 5.1% price decrease for double apartments. Using distance indicators, being within 2 kilometers of the west campus bus stop translates to a substantial, 22.7% price premium for double apartments and an 18.5% price premium for single apartments. The same figures derived with a 3-kilometer radius estimate are 10.1% and 10.5%. There is also evidence of a negative association between rating scores of apartments and their distance to campus. For a doubling of distance to West Campus, ratings scores decrease by approximately 8.2 out of 100 or 0.27 standard deviations.
 * = P<0.1, ** = P<0.05
 Only 142 apartments out of the 238 listings in the dataset offered triple rooms. In contrast, 228 apartments offered double rooms and 212 apartments offered single rooms.
 The 2km-radius indicator is not used here because only 2 apartments out of the 16 observation within the 2km radius have at least five reviews.
In “Mortgage Lending in Chicago and Los Angeles: a paired-testing study of the pre-application process”, Ross et al. (2008) used paired testing to measure discrimination against African-American and Hispanic homebuyers in the mortgage lending process. Many studies have provided evidence that minority buyers are less likely to receive mortgage loans than white buyers and, if successful, receive less favorable loan amounts and terms. There is debate, however, on how much of this outcome can be attributed to discrimination. Due to differences in creditworthiness, it is not typically straightforward to isolate the effects of differences in racial and ethnic treatment. Most work done on the topic of race in lending has used HMDA data which does not contain many important lender and loan attributes such as credit history and lending ratios.
Using data from a recent paired test study of discrimination in lending, Ross et al. examine the effects of race and ethnicity on mortgage lending. Using paired testing, two individuals, one white and one minority, separately pose as homebuyers with equal qualifications for borrowing. Both members of the pair ask about the availability and terms for the same home mortgage loan. Since the two borrowers are constructed to be equal in every regard other than race or ethnicity, differences in the responses received by the two can provide direct evidence for differing treatment of minorities. It should be noted that this methodology will only focus on the first part of the lending process, the pre-application stage (which involves a loan officer that can observe the race of the applicant) rather than the approval stage (with an underwriter who typically does not).
Paired Testing Methodology
The study included approximately 250 paired tests of a representative sample of mortgage lenders in Los Angeles and Chicago. Testers posed as first-time homebuyers with limited assets making general requests for information from lenders about their mortgage loan options. The testers were given profiles that qualified them for loans targeted towards A- credit quality borrowers in their respective housing markets. Each tester was assigned sufficient income to purchase a median-priced home in the area (with a 30 year fixed-rate loan and 5% down payment) and randomly assigned one or two minor credit issues, mostly late payments. Each pair was given almost identical financial and household characteristics with the minority in the pair receiving slightly better qualifications. These pairs, it should be noted, were not permanent—a tester could be paired with multiple partners if more than one partner was available that also generally matched in gender, age, and appearance.
Table 1 below provides data on the lending institutions in the study. The study looked only at lenders that reported under the Home Mortgage Disclosure act, accepted at least 90 loan applications in 1998, and had reasonably located offices for a first-time homebuyer. 67 lenders in Los Angeles and 106 lenders in Chicago qualified under these criteria, and in order to draw a market representative sample, lenders were selected (with replacement) with a probability of selection based on loan volume. This provided 35 lenders for black-white testing in Los Angeles, 34 for Hispanic-Anglo in LA, and so on as indicated in the table.
The basic testing protocol involved five steps:
- Obtain an appointment – testers called to arrange in person visits with lenders
- Make the initial request – testers requested help in figuring out a price range of housing they could afford and an estimated loan amount that they would qualify for
- Exchange personal/financial information – testers provided all requested information on income, debts, assets, credit history, etc.
- Record information on recommendations – testers noted suggested home price range, estimated loan amount, and financing options recommended
- End the visit – testers thanked the lender and allowed them to suggest follow-up contact
The testers then completed a test report form which allowed the study to gather information on the following six questions:
- Did the testers receive the information they requested about loan amounts and house prices they could afford
- How much were testers told they could afford to borrow and/or buy?
- How many specific products were discussed with the tester?
- How much “coaching”, such as offers of advice on paying down debts, down payment assistance, or a prequalification letter, did testers receive to help them qualify for a loan?
- Did testers receive follow-up calls from lenders?
- Were testers encouraged to consider FHA loans as an option?
Statistical Analysis Methodology
The paired tests each generate a series of treatments t for the white and minority testers, designated as Wit and Mit respectively. An incidence measure i is derived by comparing the experiences of the two testers and classifying the test as majority favored, equal treatment, or minority favored. For loan amounts or house prices, a test is considered favored one way or the other if a tester receives an estimate that is 5% higher than their counterpart. Gross majority favored treatment is defined as the fraction of tests classified as majority favored, and likewise for gross minority favored treatment. The net measure of adverse treatment, Nt is then defined as
which is gross majority favored treatment minus gross minority favored treatment. Probability (Pr) in this case is solely a measure of sample frequency. Also, a severity measure for a treatment is defined as the difference in the treatment experienced by the two testers
where the expected value (E) is captured by the sample mean of the difference of the two series of treatments. These two measures, Nt and St, are commonly used estimates of systematic discrimination towards minorities. Statistical tests are performed on these two variables to determine if they differ significantly from zero using a two sided test. While it would be very unlikely to find unfavorable treatment for whites based on past studies, the authors decided to use the two-sided test as it was more conservative.
To address the potential issue of bias arising from using the normal distribution for small sample sizes, the authors use Fisher’s exact (permutation) tests, writing the null hypothesis for Nt as
For St the null hypothesis is
Table 5 below summarizes the patterns of findings. Significant differences between the white favored and minority favored are indicated, with * representing significance at the 5% level and ** for the 1% level. The last row of the table shows that in Chicago, Hispanics and blacks received significant differential treatment from whites in three and four of the six categories, respectively. For both minority groups in Chicago, this leads to a rejection of the null hypothesis of equal treatment for whites and minorities at the 0.01 level. In Los Angeles, the data taken as a whole is consistent with the null hypothesis of equal treatment.
In summary, the paper finds strong evidence of adverse treatment of Hispanics and blacks compared to whites in Chicago in the pre-application stages of the mortgage lending process. In the study, Hispanics were quoted lower loan amounts and house prices, were given less information about products, and received less coaching. African Americans were provided less information, received information about fewer products, received less coaching, and were less likely to experience follow-up contact. Los Angeles, on the other hand, showed no statistically significant differences in overall treatment of its white and minority borrowers. While minorities received worse treatment in some specific categories, this was not indicative of an overall pattern in LA.
Discriminatory treatment at this early stage in the mortgage lending process, though subtle,can have effects on the rest of the mortgage application. Minority homeseekers may be discouraged from applying for a mortgage due to their treatment by a lender, either abandoning their search completely or applying through the costlier subprime mortgage market instead. Also, loan officers provide more support and information to white applicants in certain circumstances which gives them a better chance of acceptance than a similarly qualified minority applicant.
Federal law, through the Equal Credit Opportunity Act (ECOA) and Fair Housing Act (FHA), forbids credit discrimination and real-estate related discrimination. The results from this study show that discrimination in these aspects is an unfortunate reality for minorities seeking home mortgage loans. Further study could be done on the reasons behind the different levels of discrimination found in Chicago and Los Angeles in the study. This research could then be used to help implement policies and effect change on a broader scale to help fight against unfair lending treatments and practices.
Stephen Ross, Margery Austin Turner, Erin Godfrey, and Robin Smith, 2008, “Mortgage lending in Chicago and Los Angeles: a paired-testing study of the pre-application process,” Journal of Urban Economics 63: 902-919.
Tables 3 and 4 below provide information on the proportions of each test that were favored for white or minority testers.
Durham Tour: January 18th, 2015
Cole Mill Road is a lengthy road in the northernmost section of Durham that branches off to numerous neighborhoods of various affluence levels. Neighborhoods along the road share the commonalities of a heavily wooded environment and expansive land space. However, there are some other aspects which highlight stark differences between neighborhoods. Stoneybrook for example (more on this subdivision in the next paragraph) is probably the nicest neighborhood I’ve seen in Durham. The neighborhood itself is flanked by a large golf course that looked surprisingly empty. It seemed more like a private golf course for Stoneybrook residents than anything else. On the other hand, the subdivision off of Jefferson Dr. has the look of a loose settlement in the mountains. The hilly area is dotted with wood cabin type establishments, but the houses virtually have no lawn as they are smothered by the dense forest. The houses also look worn and there are large amounts of trash lying around.
Stoneybrook Drive is a small road off Cole Mill Road which leads to very nice looking neighborhood. Having first observed the neighborhood on Jefferson Dr. off of Cole Mill Rd., I was not expecting to see anything too enticing in terms of housing. However, as we drive in to check out the real estate, the first thing we see is a flashy new BMW coming out of a driveway. The status of the car certainly did not outdo the status of the household from which it came. The uniquely constructed brick house was gated, had elaborate landscaping, and also spanned upon a rather large piece of land. The backyard was spacious and led right into the golf course. Further down the drive was a sign warning of a neighborhood security watch that would report any suspicious activity. This was one of two neighborhoods that I saw this sign. (The other is East Forest Hills Blvd.)There was another house that had two BMW’s in the driveway. The house that stood out the most is pictured below. Its appearance did not fit in with its surrounding at all. The house had modern architecture with a wild-west style. Pine trees and a western designed mailbox surrounded by cacti distinguished its landscaping. What stood out about this neighborhood was that every house seemed to have been uniquely designed by the owner as opposed to a cookie-cutter neighborhood. This aspect speaks to the upper class nature of the neighborhood as well as the relatively recent construction of the houses.
A modern, stylish house of the sort usually seen in southern California rather than in North Carolina.
Guess Road led the tour back into a commercial area nearer the central part of Durham. Guess Road is primarily lined with cheaper food, retail, and service options. I noticed that the street also exhibited greater diversity in commercial options than most other streets I’ve driven around in Durham. For example, almost half of the stores have Spanish in their names. There is also a Chinese dim sum restaurant (this kind of Chinese food is rare) nestled between a hot dog shack and a BBQ joint. The condition and aesthetic of the buildings/plazas matches the economic state of the area. The buildings appear old and worn, and while the road itself sees relatively busy traffic, the parking lots along the road are often barren.
Northgate Mall is old and a bit rundown, and it shows. A large Sears (a true mark of any ancient mall) towers in the forefront of the strip mall. The parking lot, on a sunny Saturday afternoon, is hardly a third filled. Both the exterior and interior of the mall clearly aren’t the most inviting commercial setups, and I feel that most people come to this mall for necessities rather than retail therapy. Inside the mall is a grand carousel that must have been a major highlight back when Northgate was built, but now it seems out of place. I noticed that many of the stores are either low end brands or small cheap shops set up by independent vendors. As the number of cars in the parking lot suggested, the traffic within the mall was eerily low. Also, some members of my group were walking around with fancy cameras, and we got stopped by mall police warning us that taking any photos of the mall property is strictly prohibited. I’ve read that Northgate Mall has received publicity for the wrong reasons in the past, and I wonder if the mall considers unnecessary documentation to be threatening.
Old North Durham neighborhood was a mixed bag of old and new and seemed like one of the areas undergoing a transition. We parked in the empty lot for the Grace Baptist Church which also housed the Durham Nativity School, a non-profit effort which serves to bring higher quality education to financially underprivileged boys in the area. In the backyard of this church was a shabby playground that looked to be falling apart. Surrounding this block, however, was a surprisingly eclectic array of large and fairly well established residences. Every house seemed to be painted a different color – from green to yellow to bright red. Each house also stood out because of its starkly contrasting architectural style from the rest of its neighbors. Interestingly, I did not notice very many vehicles at all in the driveways, and cars rarely passed through this area. There were a few pedestrians, and I watched one elderly lady board a DATA bus which had a stop right next to the Old North Durham Inn. Perhaps bus transportation is especially common in this area since it is so close to downtown Durham. The juxtaposition of the old church and playground within a finer residential area suggests that this neighborhood is in a state of transition, but traces of poverty are still evident.
Abandoned toys are strewn about on a grassless playground.
One of the unique establishments around the Old North Durham neighborhood.
East Main Street was by far the most rundown of all places I visited for this tour. The scenes along the entire route of North Alston Ave. departing from Old North Durham just seemed to become more and more entrenched in poverty the closer we got to East Main Street. At first, the houses just seemed old, then they had constructional damage, and finally entire units were boarded up and entire streets seemingly unoccupied. The neighborhoods around East Main St. are the ones that are currently undergoing some serious renovations. We passed by a house that is currently being rebuilt by Habitat for Humanity. If there are often houses here being rebuilt by Habitat for Humanity, then the renovation process for this area may be terribly slow and drawn out. It does not appear that there is any institutionalized reconstruction project in place, and some houses that are boarded up may have been empty for years.
We stopped along a small street that branched off East Main to get a closer look at these boarded up houses. I was surprised to note that on one side of the street were strictly unoccupied houses while the other side of the street had cars and looked perfectly occupied. This atmosphere of this area was quite strange, and although it was broad daylight, I had the urge to get back in the car quickly. My friend was in the process of taking a photo of one of the occupied houses when the homeowner actually opened the door to see what we were doing. Although my friend successfully made small talk with the woman, I could tell that she was a bit suspicious of our activities, rightfully so. Many of the boarded up buildings had “Private Property” or “No Trespassing” signs bolted into the front, but I was surprised to see that the woman’s house also had one such sign right next to her “Life is Short, Eat Cookies” sign. Usually buildings have these signs when they are owned by someone or some entity that is not personally there to monitor the property. It just seemed odd that an occupied building would have one too.
Most of the area around East Main Street is really empty which can certainly be attributed to the lack of suitable housing. The houses are small and bland and nothing about the housing structure stood out other than its poor state. There were often mixes of single unit buildings and town homes (3 units to a building) along a single street. Lawns were unkempt and strewn with tarps and other construction related trash. Other than residential spaces, we saw a learning center that offers vocational type training for those that perhaps struggle to achieve higher education. Adjacent to this learning center is a performance learning center for young students who are in danger of dropping out of school.
A house that has every door and window boarded up. The household across the street sees this sight every time they exit their home.
Another view of the same house.
East Pettigrew Street showed us a bit of the manufacturing side of Durham. Two sets of railroad tracks run parallel alongside the street. We passed by a number of large old manufacturing plants including Delta Gypsum and Holcim that looked to be producing cement. The area surrounding East Pettigrew is very expansive and underdeveloped, and this serves the ease of production and transportation well. Having these plants situated right next to the railroad also certainly aids the efficiency of the businesses. However, certain parts along the road suggest that manufacturing industries may be slowly phasing out of Durham, or at least in this area. There were junkyards with old trucks and cars, and some factory parts were rusting and falling apart, or all together abandoned. A gem we found on this tour is the Durham Green Flea Market which happened to be right on the side of East Pettigrew Street. Relative to its small area, the activity at this market was much more bustling than at Northgate Mall. Locals come to buy cheaper fresh produce, trinkets and even electronics from a predominantly Hispanic vendor base.
A plant along East Pettigrew that is falling apart and probably no longer in use.
Hayti is a small area that is more commercial than residential. A couple of large roads cut right through the district, and newer shopping plazas with small businesses and restaurants populate the space. The centerpiece of Hayti is the large and modernly architected Hayti Heritage Center, a locus of performance and gatherings for the historically African American community. Only a few cul-de-sacs surround the Heritage Center, and from what I saw, the majority of residents still seem to be African American. I stopped at an adjacent commercial plaza (one of the older plazas), but there was not a ton of variety in the shops. Four out of six shops were some form of barber shop or hair salon. The others were a flower shop and a Southern soul food restaurant. We opted to stop for a bite at the restaurant, and the chicken & waffles were every bit as good as Dame’s, but without the recognition, it happened to be much cheaper as well! Given the relatively modern look of Hayti, I would guess that Durham is trying to establish the area to become more commercially focused.
East Forest Hills Boulevard along with Stoneybrook are by far the two wealthiest residential areas we visited on the tour. This neighborhood is essentially nested within the Forest Hills Park. As the name implies, the park has numerous shallow rolling hills, and many houses are built at the top or into the side of these hills. Also due to the geography of the land, houses are scattered and spaced very generously apart from one another. Like Stoneybrook, each building is strikingly unique – a large Georgian brick house is neighbored by a quaint German style house. Since East Forest Hills runs right along the edge of the park, houses look right upon the dense green landscape, and residents have easy access to the park’s trails. Kids (the only time we saw children outside on this tour) played soccer on the open fields while joggers and bikers traversed the trails. Although the housing density is low, we saw many more people outdoors than in any other area of Durham. This characteristic attests to the safety, recreational value, and affluence of the East Forest Hills neighborhood.
A cozy well maintained German style house in East Forest Hills Boulevard. It’s hard to see, but in front is a sign that alerts outsiders that the house is security alarm protected.
North Carolina Central University is a compact urban campus distinguished by the blend of classic Georgian architecture and modern glass-heavy additions. The residential halls, which are not numerous, appear to have been recently renovated and look clean and comfortable. It is probable that the majority of students reside nearby the university or live in off campus housing. The landscaping was not remarkable given the time of year, but I could imagine that in the summer time, the campus would feel quite homely. We ventured into one of the science buildings, and both exterior and interior were similar to Duke’s own French Family Science Center. NCCU of course lacks the research and laboratory facilities that Duke possesses. A busy street (Fayetteville Rd.) cuts right through a segment of the campus, and I wonder if this presents a dangerous crossing at night. The streets around NCCU actually seem really similar to the neighborhoods off Duke’s East Campus. The small houses and apartment units are modest establishments and may serve as affordable off campus housing options for NCCU students.
Weaver Street runs along the southern part of Durham isolated away from the denser quarters of downtown much like Cole Mill Rd. is in the northern part of the city. Most of the neighborhoods along East Weaver streets had large 1-story homes with spacious lawns set against a wooded terrain. The houses weren’t fancy, but were structurally sound, and lawns were clean. This space was also particularly empty as we did not see any people outside nor did we pass by any cars on this street (the only street where we could drive at 5 mph without guilt). Continuing on to West Weaver Street, we saw a completely different housing landscape. For about a third mile up until the end of Weaver Street, there were perhaps 30 identical plain town homes, each housing at least 2 units. The layout of this area as well as the institutionalized appearance of this complex suggests that it may involve publicly subsidized housing. I saw clothes hanging out on drying lines, so perhaps the housing units are so basic that they do not even include laundry utilities. The complex also had its own recreational center and seemed to function as an independent community.
One of many identical town homes that populate West Weaver Street. The housing units are very simple, but appear clean and relatively new.
By Shelley Chen
Durham Tour: January 17th, 2015
Location 1: Cole Mill Road and Stoneybrook
I started the tour of Durham at Cole Mill Road because I was the most familiar with this area. As I drove north along the road, I noticed several churches on the left side of the road that occupied a large amount of land. Smaller streets branched out from Cole Mill Road on both sides, but there were obvious differences between the two sides. The cul-de-sacs on the left side of the street seemed more closely packed together and more densely populated. These houses were visible from the street compared to houses on the right side, which included Stoneybrook Drive, Quailridge Road, E. Oak Drive, etc. There were many franchises along Cole Mill Road including Cracker Barrel and Cookout, which have thriving business.
Location 2: Stoneybrook Drive, from Cole Mill Rd. to Carver
Adjacent to some less prosperous neighborhoods was the Stoneybrook Drive neighborhood. Upon entering, I immediately noticed a metallic “neighborhood watch” sign that signaled to me this is both a tight-knit and upper-middle class neighborhood. This is because in my own middle-class neighborhood in the suburbs of Los Angeles, a similar sign had been erected in response to a burglary two blocks away from me. According to Zillow.com, the value of properties in this neighborhood ranged from $300,000 to $1,000,000, which rival the real estate prices in Los Angeles1. Soon, I drove by a well-maintained golf course where several middle aged white residents were mingling next to their golf carts.
Passing by the golf course, the houses began to come into view. The low population density of the area and wide streets provide excellent privacy to the residents. Most residents also benefited from the extra privacy given by the lush woods and gardens that surround their properties. Every house on Stoneybrook Drive was uniquely designed, each had at least 2 stories, and many had architectural designs that were magazine- worthy. Some were stately estates while others were modern and boldly colored. Many houses had multiple luxury brand cars, with one house having two BMWs and another one having 2 SUVs.
Hidden behind the garden and trees were multiple SUVs
I feasted my eyes upon the diverse architecture and soaked in the safe, relaxing atmosphere of the neighborhood. At the end of the street was a privately owned lake that was clear and well-maintained.
Location 3: Guess Rd. from Sedgefield to Duke Homestead Rd. to Carver
Guess road has always been known to me as one of the streets leading to the most confusing intersection in Durham. Many small local businesses dot the roadsides of Guess, such as a family owned tax filing company, a “Peluqueria” or barber shop, some small strip malls, and several local food favorites like Bojangles, Hong Kong Dimsum, and Hog Heaven. It is not very densely populated, and seems to be a commercial area that provides basic services and could be further developed.
Location 4: Northgate Mall
I have only been to Northgate Mall once since coming to Duke, so I was excited to explore the mall with an economic eye. As I drove into the lot, the difficulty of finding parking in this reasonably large space reflected that heavy flow of shoppers at this mall. Facing the parking lot, there was a more sprawled out version of the strip malls I’ve seen in the heavily immigrant suburbs in California. Strip malls usually gave lower to middle class immigrants a more accessible opportunity to start small businesses; it makes me wonder if there are many immigrants in the area.
The layout of the mall seems less efficient than other malls I have seen, and the façade of the mall is less well maintained than higher-end malls like Southpoint. Most of the vehicles in the lot were relatively old sedans, which match the mall’s status as a “50 year old landmark of Durham.” At the entrance of the mall, there was a symbol saying that guns are forbidden, which implies that crime is a concern in the surrounding areas. On the inside of the mall, I noticed that some stores had “Se Habla Espanol” signs on their windows, implying a large Hispanic population. The eye center inside the mall also displayed a “Medicaid accepted” sign, which implies that the mall also caters to low income populations.
Location 5: Old North Durham Neighborhood
A “Welcome to Old North Durham” stood out to me in this more humble-looking neighborhood. Most houses were mid-sized and adequately maintained, but obviously carry a lot of history given the material, color, and style of the house. Unlike the houses of the Stoneybrook Drive neighborhood, the houses of Old North Durham were not afforded much privacy from outsiders or from each other. Around the north blocks, houses were constructed closely together and directly faced the main roads. I did not observe vehicles around many of the homes. Across the street from the houses, I noticed there was DATA bus approaching a conveniently located stop, implying some dependence on public transportation in the area. At the nearby intersection of N. Mangum Street and Trinity Avenue was the Durham Nativity School, which is a middle school for boys from financially challenged families. In the playgrounds in the neighborhood, I noticed that most of the equipments was derelict. One block south of the neighborhood, there were slightly larger, two story houses with decorations on the spacious porches. The variety of houses in this area and the proximity to school shows that this is probably a neighborhood with middle socioeconomic status and diverse levels of education.
Two of the four swings used by the children were broken, and a third one was held up by a deteriorating chain at the playground behind the Durham Nativity School
Location 6: E. Main St. from Roxboro to Guthrie
Leaving the north parts of Durham, I drove into the southeast part of Durham. There is a stark contrast in socioeconomic status between the neighborhoods around E. Main St (Guthrie Ave, Driver Road, Briggs Road, etc.) and the Old North Durham neighborhood. Some of the first houses I saw were boarded up, which implied they might be bank owned or in bad condition on the inside.
I was familiar with several houses on Driver St. that were undergoing demolition or construction by Habitat for Humanity, which is a program that provides affordable housing to low income residents. On one of the times I worked inside a house with Habitat for Humanity, I learned that the house had sustained termite damage based on the holes that can be seen in the base of the floor. Several horizontal wooden boards joining the basement and the base of the first floor were bowing inwards due to water damage.
Other houses are in relatively good condition and had been generously decorated with an eclectic collection of trinkets like a sign that says “Life’s Short, Eat Cookies!” This was the first neighborhood in which I saw children playing outside; one was African American and the other was Hispanic. The Holton Career and Resource Center was also located nearby, which I know provides vocational training to the unemployed and enrichment classes for children who attend designated schools in low-income areas. This was one of more run-down neighborhoods I have seen in Durham, and the population seems to be mostly low-income with little socioeconomic heterogeneity.
House with water damage (white marks on the wood and bricks) and termite attack being renovated by Habitat for Humanity
Location 7: E Pettigrew, from Roxboro to Ellis
I drove over multiple train tracks to turn onto E. Pettigrew St., which ran parallel to the train tracks as well as Highway 147. The region that Pettigrew St. span seemed very poorly economically developed and bleak compared to Guess Road or E. Main St. Some of the spots I passed by included the Boys and Girls Club, electricity generators, and a cement and concrete company called Holcim Trading Inc. There were not many businesses and stores other than the Durham Flea Market, which may have only gathered because it was the weekend. Along the road, I mostly saw African American or Hispanic residents who were gathering for the Durham Flea Market.
Location 8: Hayti area
I drove into the Hayti area on Fayetteville St., turned left onto Lakewood Avenue, and then turned right onto old Fayetteville St. The area seemed to be very close to Highway 147, which may discourage residents who value quietness. On the corner of Lakewood Avenue and Old Fayetteville stood the Hayti Heritage Center housed in a beautiful red church, reflecting the strong African American heritage of the neighborhood. I noticed a limousine rental company, International Food Market, and an abundance of soul food around the block. Coincidentally, I bought a delicious chicken and waffles entrée from the soul food diner Bowick’s Arc for lunch. On Hayti Lane, a small cul-de-sac on Old Fayetteville St., I saw the first multi-family condominiums around Durham, each having 3 connected two-story units. The driveways and lawns were very well-maintained and picturesque, and the residences were more spaced out than the Old North Durham neighborhood. Most houses had recent makes of vehicles, which matched the nicely renovated condos. The neighborhood had a majority of African American residents, some of whom were playing basketball outside of the condos. The area appears to be inhabited by educated, middle class families.
Location 9: E. Forest Hills Blvd
A whimsical owner flying a Jolly Roger outside of his house on Forest Hills Blvd
I returned closer to Duke University from the Hayti area and reached E. Forest Hills Blvd. One of the first things that came to mind when I saw the E. Forest Hills neighborhood was the cabin resort area in the Blue Ridge Mountains where I had spent fall break. Even the name “Forest Hills” elicited the image of an ideal suburbia. It was very similar to the Stoneybrook Drive neighborhood, but the houses had more diversity in size. Houses were beautifully maintained and were situated at higher elevations and the only visible part of most houses from driving down the street was the extended driveway outside many houses. I drove by many signs that read “Parks this way, play more,” and observed 2 white teenage girls playing soccer in one of the many grassy park areas. At the intersection with Enterprise St, many joggers were biking and running freely along the well-designed trails in the neighborhood. Although a majority of the inhabitants were white, I also saw 3 African American children leaving one house to play. I venture to guess that this area is inhabited by the highly educated upper-class in Durham.
Location 10: NC Central University and surrounding area
Idyllic park area of the NC Central University campus
Most of the buildings on the NC Central University campus were built in the beautiful Georgian style, except for the residential and science building that bore modern reflective glass on the outside. The buildings are very well-renovated and give off a calming vibe. The campus is very connected with the surrounding residential areas, seemingly bordering the Hayti area. Inside the science building, there was an array of brochures for programs in health sciences and earth sciences targeted at minority populations, which makes me conclude that the college consists of a large minority population. As we walked past the residential buildings, we saw only African American students exiting the buildings and walking around campus. The surrounding residences consist of efficiently designed one or two story houses and are probably rented by students. The businesses surrounding this area are typical of that of a college town, including several commercial banks, restaurants, and bookstores.
Location 11: Weaver St. from Cornwallis to Theresa
On Weaver St., there were several neighborhoods consisting of very uniform housing that reminded me of public housing projects I had seen in Compton. The houses were humbly built with one door and a few windows, and housing units were spaced out very evenly in a grid pattern. Most households did not own vehicles and did not have much lawn space. To me, public housing projects usually represent an effort to revitalize the areas where the population had undergone displacement or housing crises. This region has probably undergone economic hardships in the past years, but has been recovering from it. Overall, I did not notice much business activity around the neighborhood.
1 retrieved from: www.zillow.com/homes/for_rent/Durham-NC
By Melanie Green The Impact of Airport Development on Economic Development
Cities around the world are separated by physical distance, but individuals can travel relatively easily between cities using various forms of transportation. Air travel not only connects people but it connects economies to further develop the global economy. Airport development has also been linked with economic development. Much research has been done on this relationship, with focuses on different regions and cities around the world. In particular, studies that focus specifically on Chinese and Canadian airport economics, as well as metropolitan airport development in general, provide insight into this important economic relationship and the implications it can have on new airport development.
A new research study that stems from these previously explored relationships can look into the ongoing construction of the new terminal at Raleigh-Durham International Airport (RDU) and its relationship with economic development in the surrounding urban area. The correlation between airport and economic development is important, but determining a cause-and-effect relationship can be very useful in understanding the economics of the Triangle region as well as other regions around the country and the world. Thus, it is necessary to analyze similar situations worldwide to provide a background understanding of the issues at hand, as well as to develop models that can be used to analyze the specific situation at RDU.
Much of the discussion on the relationship between airport and economic development surrounds four key sub-topics: public finance, economic development, transportation and agglomerate economics, and airports in general. Airports can be considered impure public goods; therefore, in order to completely understand their worth, it is necessary to determine each individual’s marginal utility that results from the presence of a runway (Green, 2007). Economic development is often linked with infrastructure development, which means that airports are expected to further the development of the economies of the surrounding regions. Transportation in general affects the development of cities, with air travel having a large stake in both short and long distance transportation. Finally, airport economics have often included pricing and congestion issues in the past, but these issues can be combined with the economic impact of airports to gain a better understanding of urban development in the context of airport development (Green, 2007).
Economists have reached a general consensus that airports do share a relationship with economic development, but the exact cause-and-effect relationship is unclear and depends on many factors. For example, Yao and Yang (2008/07) found, based on a study of the Chinese economy, that a 10% population density increase in population density causes a 1.7% increase in air passenger volume and a 1.2% increase in air cargo. This specific relationship analyzes the effect that economic development has on airport development. On the other hand, according to a study performed by Benell and Prentice (1993), in order to create a one person-year of employment, the average number of additional air-travel passengers must increase by 1126, based on a sample set of airports in Canada. Each one of these additional passengers is expected to add a monetary value of approximately $78.08 to the economy (Benell and Prentice, 1993). In contrast with the previous relationship developed in the research focused on China, this Canadian research addresses the impact of airport development on economic development. A cause-and-effect relationship between airport and economic development is observed in both directions. For example, economic expansion can increase airport demand. An increase in airport capacity then raises productivity and/or demand in other sectors of the economy.
Methods and Findings of Economic and Airport Development Regression Analyses
In Airport Development and Regional Economic Growth in China, Yao and Yang (2008/07) perform a regression analysis to determine the effects of several variables on a dependent variable that is divided into two categories: the volume of passengers by air and the volume of cargo by air. The explanatory variables include GDP, population density, openness (trade/GDP), economic structure (share of employment accounted for by the tertiary industry), ground transportation (volume of rail and road transport), location dummy variables (east, northeast and west, using central as a base), and a time dummy for 1995-2001 (Yao and Yang, 2008/07). The data used in this study was collected from various data sources such as Statistical Data on Civil Aviation of China. The regression is represented mathematically by the following equation, with all variables evaluated in natural logarithms at 1995 price levels in China:
The researchers of this study concluded, based on the regression analysis, that economic growth and openness (measured by trade/GDP ratio) are principal determinants of airport development and air transportation volume. Further results imply that airport development is positively correlated with economic growth, industrial structure, population density, and openness. Airport development is negatively correlated with ground transportation development. The negative correlation with ground transportation reflects the substitutability between ground transportation options, such as railroads and highways, and air transportation. The less developed regions of China are the west and northeast regions. Yao and Yang’s results suggest an incentive to construct airports and promote air travel in these less-developed areas because substitutable forms of travel are costly to implement there due to the presence of vast, mountainous terrain (Yao and Yang, 2008/07). Furthermore, airport development in these less-developed regions can promote economic equality across the country, as airport development is positively correlated with economic growth. While the results of this research are only directly applicable to the Chinese economy, its methods and general findings can be transformed and applied to other urban economies.
Benell and Prentice (1993) conduct a related analysis focused on the consequences of Canadian airport expansion in their study titled A Regression Model for Predicting the Economic Impacts of CanadianAirports. The purpose of this research is to conduct an econometric analysis to estimate the relationship between indicators of airport activity and their economic impacts on the Canadian economy. This differs from the Chinese study because it looks at the opposite cause-and-effect relationship. They focus specifically on direct employment and revenue impacts as economic indicators and obtained most of the transportation data from Airports Group, Transport Canada. Benell and Prentice (1993) find that passenger traffic, a city’s commercial activity, air carrier maintenance bases, air traffic control towers, flight service stations, and selected aircraft movement statistics are key variables that determine the economic impact of a single airport. They run two regressions, one with a dependent variable of “person-years of employment that is directly attributable to the airport (E)” and the other with a dependent variable of “revenue, or economic output, that is the result of airport activity in one year (R)” (Benell and Prentice, 1993). The Ordinary Least Squares (OLS) regression is represented mathematically by the following equations:
Please refer to table 1 under references for an explanation of the variables in these equations.
The researchers find that revenue elasticity and labor elasticity can be developed from commercial airport activity. Both values of elasticity can then be used to determine the direct economic impact of an airport, using data such as passenger numbers and local economic conditions. Furthermore, it is apparent that direct labor counts are more reliable economic indicators than revenue indicators. When measuring revenues, it is difficult to avoid double counting, thus estimates become inaccurate. Numerically, Benell and Prentice (1993) find that a 1% increase in annual passenger traffic at a particular airport coincides with a .75% increase in direct employment and a .49% increase in direct revenues. The difference in the elasticity of direct employment and the elasticity of revenues is important for future planning and modeling of airport and economic development relationships.
How Airport Activity Affects Economic Development of Metropolitan United States
Similar studies have been conducted on various metropolitan areas in the United States. Specifically, research conducted by Richard Green (2007) addresses the hypothesis that activity at a metropolitan airport predicts employment and population growth. Green’s regression analysis is unique because he uses panels and two instruments to attempt to control for simultaneity and develop proof of causation rather than just correlation. This study uses four measures of airport activity that include boarding volume, passenger originations per capita, whether a city has an airport that is a hub for a major carrier, and cargo activity. The two economic development indicators are population and employment growth between 1990 and 2000.
Based on a numerical regression, using both OLS and Instrumental Variable (IV) tools, Green (2007) finds that boarding per capita, passenger originations, and the presence of a major airline hub have a significantly large influence on population growth. In fact, hub cities grew between 9% and 16% faster than non-hub cities between 1990 and 2000. However, the volume of cargo activity did not prove to cause economic development, according to this study. Green (2007) also explores the concept of negative externalities. While many argue that airport development is positively correlated with economic development, there exist negative externalities in building airports. For example, residents located within close proximity to airports often complain about the noise, pollution, and overall congestion that airports bring to the local neighborhoods. According to Green, airports can only be considered beneficial if the benefits of economic development outweigh the costs of the negative externalities.
Implications For Future Research
Each of the three aforementioned studies shares similar and dissimilar dependent and explanatory variables in completing regression analyses. This demonstrates the limitations of each study, as they define indicators such as economic activity or airport activity based on a set of individual variables that differ from study to study. It may be possible to combine parts of these individual regressions to create a more comprehensive study. The econometric models that have been created serve as effective templates for further research on this topic. Specifically, in looking at the relationship between the economy of the Raleigh-Durham region and the expansion of the RDU airport, it is possible to apply the specific economic and airport development indicators to create a distinct regression for this specified urban economy. Hypotheses for this specific research may include the following:
- RDUiscurrentlyundergoingexpansionbecauseoftheincreasedeconomicdevelopmentin the triangle region. Using airport activity as the dependent variable and various economic indicators as independent, or explanatory, variables can prove this prediction.
- RDU is currently undergoing expansion and this increase in airport activity will cause economic development in the triangle region in the future. One can support this prediction by running a regression with various indicators of airport activity (such as the ones used in the previous three studies) as the independent variables and an economic indicator such as GDP or employment levels as the dependent variable.
While generalizations can be developed regarding the economic impact of airports in general, individual urban economies are unique. A study of RDU and Raleigh-Durham specifically will bring new findings to complement pre-existing research. The study of an airport’s relationship to an economy is important because this relationship has the ability to have large implications for the future growth of a city. In addition, because airports connect cities throughout a country, airport development can even transform national economies, such as the example in China that predicts airport growth in less-developed regions can alleviate the income disparity across different regions in the country. Regression analysis is an important tool in determining these relationships and revealing the importance of airports in local economies.
Benell, Dave W; Prentice, Barry E, 1993. A regression model for predicting the economic impacts of Canadian airports. Logistics and Transportation Review; Jun 1993; 29, 2; ProQuest pg. 139
Green, Richard K. 2007, “Airports and Economic Development, Real Estate Economics; Spring 2007; 35, 1; ProQuest pg. 91
Yao, Shujie and Yang, Xiuyan. 2008, “Airport development and regional economic growth in China,” Nottingham, UK: University of Nottingham research paper 2008/07.
By Emily Jorgens The association between urban sprawl and obesity- is it a two-way street?
I. Research Question
Plantinga and Burnell’s 2005 paper draft proposes a model that addresses how obesity and urban sprawl are related. This question arose due to the recent rise in obesity in the United States. There is ample research in the public health and urban planning realms on this topic. However, Plantinga and Bernell challenge the existing literature’s conventional assumption that sprawl causes obesity.
Urban economics is perhaps a surprising avenue by which to analyze the problem of obesity. However, the analytical model put forth in this paper finds meaningful results that could have profound public health and policy implications. Urban planning specialists have drawn links between urban sprawl patterns and demographic and lifestyle characteristics.
Specifically, urban sprawl and obesity are related in three main ways. First, poor street networks and low density lead to longer travel distances. Longer travel distances mean people are forced to travel by car rather than bike or foot. Second, low density means that public transportation systems are less effective and less likely to exist. Therefore, people are traveling by car and have longer commute times and thus less time for physical activity. Lastly, sprawling areas often have poor or unsafe public parks, which discourages exercise.
The existing research holds that poor infrastructure and land use, as outline above, ultimately cause weight issues. As a result, many cities are investing in projects to encourage healthy living. For example, the Atlanta Regional Commission recently invested $1.1 billion in bike and pedestrian infrastructure. Plantinga and Bernell’s model questions whether this will be effective. They assert that overweight people self-select for sprawling residential environments, and thus improving land use in these areas is futile.
Previous research has treated urban form as an exogenous variable. In other words, researchers have assumed that one’s Body Mass Index (BMI) has no influence on residential location choice. This study, however, poses that BMI indicates lifestyle choices that influence residential location choice. This distinguishes whether sprawl causes BMI to rise or whether high BMI individuals choose to live in sprawling locations. Treating BMI and location preference both as endogenous variables answers this question.
II. Theoretical Background
Plantinga and Bernell use the National Longitudinal Survey of Youth from 1979 (NLSY79) together with the sprawl index produced by McCann and Ewing (2003). The resulting dataset includes variables such as BMI, income, education, county of residence, and degree of sprawl. This paper builds on the conventional model for regressing BMI on locational attributes and a composite good.
The conventional function holds that utility is maximized by considering weight, attributes of the residential location (such as walkability), and a composite good. Weight (W) is given by:
Where W0 is initial weight of a person, N is a vector of locational attributes, and C is a composite good. Utility is maximized using the follow equation:
Given that p is a vector of prices for locational attributes and I is income. Using standard constrained optimization techniques, the following equations give the locational attributes (N*I ) and weight (W*I) that result on the greatest utility for an individual.
However, this paper argues that there would be codependence between the weight and locational attribute variables. Also, the researchers hold that a complete model would distinguish between people who recently moved versus have been in the same county for four years or more. This is because if land use does have an impact on weight, it would take some years to manifest. Therefore, the researchers propose a simultaneous equation model that would treat BMI and locational attributes as endogenous. They also create two different models that look at movers and non-movers separately.
III. Empirical Model
The BMI model used in this paper is as follows:
given that i (i=1,…,N) indicates specific individuals, B0 is the intercept term, Bj (j=1,…,14) are the variable coefficients, and ɛi is the error term. The explanation of variables is given in Table 1.
Due to the fact that migration is a separate decision and difficult to factor into the model, the researchers decided to define the decision to move to a county as whether it is high or low density, income, education, marital status, and more. Therefore, their model for adjusted BMI on all the other variables as a follows:
Where y*I is the latent variable describing choice of a low or high density county, that i (i=1,…,N) indicates specific individuals, y1 and y2 are parameters on ADJBMIi and SPRAWLi, X1i and X2i are vectors of the exogenous variables, B1 and B2 are conformable parameter vectors (like race, sex, smoking, age, education, and regional dummies), and ɛ1i and ɛ2i are the error terms.
Using least squares and a probit maximum likelihood model, they created a set of covariant matrices of expected values for the endogenous variables. These estimates were made using data from the year 2000 in the NLSY79 and the sprawl index. To separate out movers from non-movers, the model was run twice, each time with only individuals who had lived in their counties for 4 years or more versus less.
IV. Results and Conclusions
The results of the simultaneous equation model suggest that BMI does, in fact, have a negative effect on whether an individual moves into a dense county. This holds true for both movers (coefficient -.789) and non-movers (coefficient -1.182). The researchers also accounted for the fact that their arguably arbitrary cutoff for density or their year cutoff for being a mover versus non-mover may have skewed the results. However, even with more conservative and liberal estimations of these cutoffs, their results generally held true. The implications of these results are that current policies about land use and public health may be misguided. Increasing infrastructure that encourages an active lifestyle in sprawling areas could just result on obese-prone people moving elsewhere.
The fact that Plantinga and Bernell challenged the assumption that sprawl causes obesity could have profound policy implications. With this discovery, money will be saved on fruitless or inefficient policies. Also, researchers are one step closer to discovering the root of obesity problems. Their research helps society edge closer to the true causes and possible solutions for obesity. This investigative research model could also be applied to other public issues related to urban sprawl. For example, one could research whether violent people move to sprawling or dense areas. Does density versus sprawl cause the violence or is it a result of the type of person who chooses to live there?
Andrew Plantinga and Stephanie Bernell, 2005, “The association between urban sprawl and obesity: is it a two-way street?”. Draft. americandreamcoalition.org *
Andrew Plantinga and Stephanie Bernell, 2007, “The association between urban sprawl and obesity: is it a two-way street?” Journal of Regional Science 47(5): 857-879.
*I used the 2005 draft because it explained which equations were used, while the 2007 version did not. The remainder of the article and analysis was largely the same.
By Emily Jorgens Energy and Urban Economics
Due to rising energy prices and recent attention surround energy consumption, an increasingly relevant area of academic interest is how urban systems are adapting. Historically, urban areas developed without constraints due to energy availability. The low price of energy led to highly dispersed urban landscapes. The value of clean air, larger properties, and the suburban lifestyle outweighed transportation and other energy costs. Today, in the face of energy constraints and rising energy prices, established cities have to adapt. This creates an opportunity to study the dynamics of urban development with respect to resource constraints. Research into this field can have many benefits, like shedding light on possible socioeconomic inequalities, as well as providing an efficient framework for developing cities to emulate.
Urban Form and Energy Consumption
There are two main points of view on how urban landscapes will respond to rising energy prices. Some authors argue that urban planning models indicate that the changing energy landscape will lead to a monocentric city. Other authors hold that semi-independent suburban centers of economic activity will result. The difference of opinion arises largely due to variation in the assumed cost of moving people versus goods. If goods are more expensive to move, a monocentric model will arise, and visa versa. Others argue that the reality seems to be something in the middle (Sharpe 1982).
Monocentric City Theory
A pertinent issue in the discussion of how energy and urban economics are related is whether different socioeconomic groups are impacted differently. Sharpe (1982) found that inefficiencies in urban planning do increase to socioeconomic inequalities. Specifically, he found that outer urban residents are affected most by rising energy costs. This is due to rising transportation costs and loss of value in property. Therefore, this model supports the shift to a monocentric city model due to rising energy prices.
Sharpe (1982) found that initially, rapid suburbanization, efficient public transport, rising wealth, and low energy prices led to sprawling development. Subsequently, as oil prices started increasing in the 1980s, evidence of socioeconomic discrepancies arose. In particular, low-income groups in outer areas who have low accessibility to public transportation carry most of the burden. This is because higher energy prices make it more attractive to live in urban areas, due largely to transportation costs. Low-income people are especially impacted by rising transportation costs because transportation takes up a larger percentage of their expenditures. Furthermore, low-income people are more likely to be burdened by changing land values across urban areas. Therefore, supporting the monocentric city theory, these socioeconomic groups are forced to move towards the city center for low-priced housing and short commute time (Sharpe 1982).
Semi-Independent Suburban Center Theory
In support of the semi-independent suburban center theory, one model holds that because energy reduction is easy, there is little incentive to relocate. A household’s energy costs can vary greatly based on commute distance, size of home, number of cars, age of household members, and number of people in a family (Small 1980). However, depending on the elasticity of demand for energy-using goods and services, consumers can adjust their consumption behaviors. Several studies demonstrate that “in those sectors most strongly affected by potential scarcity”, there are a variety of easy avenues to reduce energy consumption (Small 1980, 101). For example, a Resources for the Future study showed that simple home alterations can reduce heating costs by 20% (Schurr et al. 1979). Therefore, if energy prices rise, households can take simple steps to reduce their costs, rather than relocating to reduce costs. This supports the semi-independent suburban center theory because there is little incentive to move to a city center.
Rather than relying on theoretical frameworks, some economists assert that no conclusions can be made about location response to energy prices without quantitative evidence. They favor an empirical approach using data on energy consumption patterns together with cost data for city-suburban migration. This type of analysis more concretely sheds light on how energy scarcity might impact city form (Small 1980).
The 1980 Small paper empirically analyzes how energy use for urban versus suburban dwellers vary in terms of work travel, nonwork travel, and home heating/cooling. In order to quantify incentives for relocation based on cost differentials for work travel, Small (1980) used data from the 1975 Travel-to-Work Supplement of the Annual Housing Survey. The data allows for comparison of whether city or suburb residences are employed in the city or suburbs. The increased cost of the commute was calculated as 5 cents extra per mile for 240 round-trips per year. The data revealed that absolute increases are quite small for all four categories of commuters. Specifically, “city locations are less attractive by $33 per year per worker compared to the average suburb” (Small 1980, 108). Therefore, the data does provide weak evidence that more centralized suburban locations are more popular due to rising energy prices.
Small acquired non-work travel data from anther author who used household survey data to estimate car ownership and use. The cost differential was estimated using variables like single family versus multifamily home, owner-occupied versus rented, and suburban versus central city location. The cost difference for an urban versus city resident was estimated as $113 per car.
Finally, heating and cooling cost differences in suburban versus city centers were found using engineering studies on housing unit types in the four regions of the U.S. The calculation used weighted averages for energy consumption based on region and home type. Small (1980) found that, with a 75 cent per gallon increase, the cost differential ranges from $63 to $136 per year amongst the different groups.
One shortcoming that is acknowledged in Small’s 1980 paper is that this study does not completely quantify the net incentives for relocations. In addition to household modifications and buying more fuel-efficient cars, consumers can opt to carpool, etc. Quantifying all of these options would be arduous. Instead, Small chooses to assume that these alterations would impact different locations proportionally. Therefore, excluding these factors from the analysis gives the upper bound on relocation incentive.
Qualitatively these cost differentials total about $256 annually for households. It is important to remember that this study excludes any considerations of energy-saving behavior, like carpooling, that might result from increased energy prices. Small (1980) extrapolates on these findings by adapting a model that predicts household migration response to taxation disparities in cities versus suburbs. The multiple regression equation is not given in this paper. However, Small says that the model shows that if households reacted to energy price differentials in the same way as taxes, a $256 increase would cause a net outmigration of .56% from cities. Small (1980) concludes that location shifts within suburbs and cities may occur but that overall density and form will not change as a result of energy prices.
Urban Energy Footprint Model
Continued research in the field of urban and energy economics has lead to more robust models. For example, one of the most recent and comprehensive methods, developed by Larson et al. (2012), is the Urban Energy Footprint Model (UEFM). It shows how land use and transportation policy affect housing markets and transportation. Unlike most models in the field, Larson et al. (2012) includes income groups and traffic congestion as factors.
Congestion is found by v(k) = 1/(a-bV(k)c, where v(k) is the commuting speed, V(k) is the traffic volume through location k, and a, b, and c are parameters that reflect the severity of the congestion function. In order to determine the spatial structure of the housing market, the authors drew variables from the American Housing Survey, 2000 Census of Population database, and the Internal Revenue Service. In order to add the energy consumption variables to the spatial housing market data, this model drew from the Housing Assistance Supply Experiment (HASE) and estimates from household energy consumption equations, which are based on the 2005 Residential Energy Consumption Surveys (RECS) (Larson et al. 2012).
The paper concludes that the relationship between energy prices and urban form are significant. Specifically, Larson et al. (2012) hold that with rising fuel prices, the city becomes more compact in terms of reduced area and increased density. Also, low-income households are more impacted than higher income households due to a difference in income elasticity of demand. Interestingly, the increased fuel prices save more energy due to the shifts in the housing market than the reduction in driving. This bold conclusion differs from other research, but is perhaps more valid because of the inclusion of more variables (Larson et al 2012).
Conclusions and Further Research
Inspired by the findings in the vast literature surrounding energy and urban economics, there is room for further research into the socioeconomic differences in urban form response to energy. A combination of models that exists in the field today seems like the most viable way to answer the question of whether low income groups are more effected by rising energy prices and how that influences their movement in an urban sprawl.
Some limitations of the existing literature include that some studies focus mostly on oil prices rather than coal and natural gas because historically oil has been more scarce than the other two (Sharpe 1982). Another major shortcoming of in this field is that most of the research doesn’t factor in the differences in energy use across cities. Energy use can vary substantially based on the quality of public transportation, state gas taxes, environmental sympathy of the population, technology, and land use policies. However, the current models rely heavily on national averages for energy consumption. This leads to broad conclusions, rather than city-specific results.
Furthermore, the fact that the global energy landscape is so fast-paced must be taken into account. Many studies were conducted in the 1980s, which was a time of great pessimism towards energy resources. Today, we have improved extraction technologies for conventional resources, like oil and gas, as well as development of alternative and renewable energy sources (Small 1980).
It is important to understand the dynamics between urban and energy economics because, for example, energy policies may have unintended impacts on urban form and visa versa. The UEFM model lacks inclusion of technological improvements. Technological improvements can expect to impact the energy industry by reducing the need to travel, creating more energy efficient products, and by producing alternative forms of energy. Therefore, I think the model should include a variable that captures the increasing likelihood that these advancements will happen in the future. A proxy could be made for the state of technological advancement by comparing two similar cities in a developed versus developing nation.
William Larson, Feng Liu, Anthony Yezer. Energy footprint of the city: Effects of urban land use and transportation policies. Journal of Urban Economics. Volume 72. Issues 2–3. September–November 2012. Pages 147-159. (http://www.sciencedirect.com/science/article/pii/S0094119012000332)
Kenneth A. Small. Energy Scarcity and Urban Development Patterns. International Regional Science Review. Volume 5. Issue 2. August 1980. Pages 97-117. (http://irx.sagepub.com/content/5/2/97.refs?patientinform-links=yes&legid=spirx;5/2/97)
R. Sharpe. Energy efficiency and equity of various urban land use patterns. Urban Ecology. Volume 7. Issue 1. September 1982. Pages 1-18.
References within articles:
A. Davies and G. Glazebrook. Transport energy and equity: winners and losers. Australian Transport Research Forum Papers. 6th Meeting. October 1980. Brisbane. Brisbane Metropolitan Transit Authority. Page 227-247.
S. H. Shurr, J. Darmstadter, H. Perry, W. Ramsey, and M. Russell. Energy in America’s Future: the choices before us. 1979. A study by the staff of the Resources for the Future National Energy Strategies Project. Baltimore: the Johns Hopkins University Press.
By Jack Willoughby The Effect of Public Transportation on Crime
In response to population growth, scarce natural resources, and the growing atmospheric problems caused by greenhouse gasses, many cities have sought to reduce the number of drivers on the road to limit both traffic and fossil fuel consumption. One theoretical means of reducing the number of cars on the road is to expand the use of public transportation. Such an expansion can have unintended negative effects, however, if it results in an increase in the prevalence of crime near the new transit stations or bus stops. The goals of this paper are to present a model of how public transportation affects crime, to demonstrate how theory has been confirmed or challenged by previous studies, and to use data related to Durham’s Bull City Connector to test the model. Analysis of available crime data indicates that the Bull City Connector has had no noticeable effect on the total amount or distribution of crime near its route.
II. Theoretical Model
As first presented by Ihlanfeldt (2003), the effect of public transportation on crime can be spatially modeled and decomposed. The expected value of a crime (π) to the person who is committing the crime is a function of the expected payoff of the crime (w), the cost of committing the crime (c), and the expected punishment of being caught, which is equal to the product of the probability of being caught (p) and the expected penalty conditional on being caught (f). The expected value of committing a crime in neighborhood H is modeled in (1):
Furthermore, (c) can be decomposed into the accounting costs of committing a crime, (b), the transportation costs to the crime (tc), and the opportunity costs of committing a crime. Accounting costs of committing a crime include the cost of buying a weapon or tool needed for the crime, the cost of buying disposable black clothes or a mask, the cost of paying someone for help, etc. The opportunity costs of committing a crime are the forgone welfare that a criminal could have earned through legitimate activities plus the value of improved mental wellbeing from not being a criminal (g). These forgone earnings are equal to the income from legal employment (e) minus the transportation costs of traveling to a job (tj). The costs of committing a crime are modeled in (2):
Aggregating (1) and (2), the net expected value of committing a crime is (3):
Transportation costs can also be broken down into accounting and opportunity costs. The accounting costs are the sun of the monetary costs of transportation, (m), which include the gas needed to drive, depreciation of a motor vehicle, and public transportation fare. The opportunity costs are the time it takes to make the journey, which can be decomposed into the value of time (v) times the amount of time traveled, which equals distance (d) divided by speed (s). This relationship is modeled in (4):
Subscript (i) indicates the trip being taken. As noted above, subscript (j) will indicate the trip to a job, and subscript (c) will indicate the trip to commit a crime. Incorporating this into (3) yields (5):
To determine the effect of public transportation on crime, we need to take the partial derivative of (5) with respect to transportation (T) and drop all terms that are not affected by a change in public transportation. The result of this marginalization is modeled in (6):
Next, we can decompose crimes into two types: crimes committed in outside neighborhoods (O) and crimes committed in the neighborhood in which the criminal is a resident (R). Adding these geographical distinctions as superscripts, we can revise (6) into (7):
An expansion in public transportation that results in its increased use implies that the marginal effect of the expansion is to decrease the total transportation costs of its riders, but it does not indicate if the mechanism through which it decreases total costs is through monetary or time costs. With one decision (whether or not to use public transportation) and two decision factors (monetary costs and time costs), the relative contributions of effects of public transportation on speed and money are unidentifiable. Theoretically, however, it is conceivable that improvements public transportation decrease both time and monetary costs. Public transportation is often highly subsidized and therefore cheap, and in cities where traffic and parking are time consuming, it is likely also faster than alternative methods of transportation. Given that the addition of new public transportation is both faster and cheaper than previous methods of existing transportation, its marginal effect on crime would be threefold. The addition of public transportation would:
(A) Decrease the transportation costs of committing crimes in distant neighborhoods that contain public transportation stops;
(B) Increase the relative transportation cost of committing crimes in criminals’ own neighborhoods relative to remote neighborhoods; and
(C) Decrease the transportation costs associated with having a legitimate job.
Therefore, assuming that the new public transportation is cheaper and faster than previous modes of transportation, its three theoretical marginal effects should be to decrease crime in areas in which criminals live, increase crime near public transportation stops in neighborhoods not previously populated with criminals, and decrease total crime levels as legitimate employment becomes a more attractive substitute to crime. The latter two effects operate in competing directions, so while crime should certainly decrease in areas currently populated by criminals, it theoretically will change ambiguously in areas not currently populated by criminals, depending on the relative contributions of the effects on crime due to (A) and (C).
III. Previous Research
The theoretical predictions of the effect of public transportation on crime have been tested empirically with mixed results. Plano (1993) found no significant relationship between proximity to rail transit stations and crime using data from the opening of stations in the Baltimore Metro system. Block and Block (2000) found a significant positive relationship between proximity to subway stations and street robberies in both the Bronx borough of New York City and the Northeast Side of Chicago. Liggett et al. (2003) found that the Green Line light rail transit system in Los Angeles had no effect on the overall levels of crime and the spatial distribution of crime in Los Angeles. Ihlandfeldt (2003) analyzed Atlanta’s MARTA system to find that the addition of rail transit stations resulted in the increase of crime in the center city, but did not affect crime levels in the outer limits of the city. Denver Regional Transportation District (2006) analyzed crime patterns near stations of the Central Corridor light rail transit line in Denver and found no evidence of an increase in crime as a result of its inception. SANDAG (2009) studied the expansion of the Green Line transit system in San Diego and found that both crime rates and distribution of crime were unaffected by the expansion of the public transportation system, as well as that residents did not feel more or less safe as a result of the expansion. Taken together, these studies indicate variation in the effects of public transportation, suggesting that specific cities respond differently to its expansion. Additionally, the rare statistical significance suggests that the magnitude of the effect of public transportation on crime is likely small.
IV. Case Study: Durham’s Bull City Connector
On August 16, 2010, the Bull City Connector (henceforth BCC) began operation in the city
of Durham, NC. In conjunction with Duke University, Durham launched the BCC to more seamlessly connect Duke and downtown Durham. During operating hours, the bus runs every 15-20 minutes, depending on the time of day and day of the week, and it is completely free to the public. It stops 34 times on its loop bounded by Duke’s Central Campus on one end and Durham’s Golden Belt District on the other, passing through Brightleaf Square and downtown Durham along the way. The full route is shown in Appendix 1. Using available data, the effect on crime of this exogenous improvement in public transportation will hereafter be investigated.
B. Data and Empirical Method
First, regions of interest were identified, the boundary maps of which are contained in
Appendix 2. The theoretical model establishes a differential effect on crime of public transportation in neighborhoods in which criminals live versus neighborhoods not inhabited by criminals, so neighborhoods of different home values were studied. The first area studied was “Trinity South,” which is comprised of the neighborhood bordered by N Buchanan Blvd on the west, Urban Ave on the north, N Duke St on the east, and W Main St on the south. This region is directly to the north of the BCC route. Using estimates from Zillow.com, most houses in the region are valued between $300-450K. A control for this region, which is comprised of houses of similar value but is not immediately adjacent to the BCC’s route, is the “Trinity North” neighborhood. This region is to the north of Trinity South, and is bordered by N Buchanan Blvd to the west, W Club Blvd to the north, Ruffin St to the east, and Green St to the south. Next, low wealth neighborhoods were identified, both near and removed from the BCC route. Adjacent to the BCC’s route is the “Holloway” Neighborhood, which is comprised the residential areas surrounding Holloway St bordered by N Roxboro St to the east and N Alston St to the west. Using Zillow.com estimates, a large share of these houses are valued between 50-150K. Of similar value are the houses immediately north of North Carolina Central University, in the “North NCCU” neighborhood. This neighborhood is isolated from the BCC by Rt. 147, and is comprised of the residential areas bordered by Fayetteville St to the west, Linwood Ave and Simmons St to the north, S Alston St to the east, and Dupree St to the south.
Crime data on these four regions from the Durham Police Department Crime Mapper were used in analysis. The Crime Mapper displays spatially identified crimes across Durham for every month from January 2010 to the present. For this analysis, the total number of crimes in each month from January to July in both 2010 and 2011 was recorded for each of the four areas described above. The data from 2010 illustrate crime before the inception of the BCC, while the data from 2011 describe crime immediately after its inception. The analysis was confined to this limited dataset because no information on crimes before 2010 is available on the Crime Mapper.
A difference-in-difference analysis was used to identify the effect of the BCC on crime. The first stage of the difference-in-difference was to subtract the number of crimes in a region in a given month in 2010 from its corresponding month in 2011. Months were compared to corresponding months throughout the analysis to control for any seasonal variation in the amount of crime in Durham. This first difference yields the change in crime from before to after the launch of the BCC in each of the four regions. Next, a second difference is needed to control for any city-wide changes in crime that were occurring in Durham independent of the inception of the BCC. To control for these exogenous changes, changes in crime in areas geographically removed from the BCC were subtracted from the areas of interest adjacent to the BCC route. Furthermore, to attempt to balance the city-wide changes in crime as well as possible, control areas contained houses of similar value to the areas of interest. In keeping neighborhood quality constant, the relationship between holistic changes in crime in Durham and quality of neighborhood will be roughly accounted for.
The theoretical model developed above predicts that crime should certainly decrease in areas
currently populated by criminals, and it theoretically will change ambiguously in areas not currently populated by criminals, depending on the relative effects on transportation costs to a job vs. to commit a crime. The BCC meets the assumptions of the model since it is free, so the monetary costs of travel will certainly decrease, and it travels to areas in which parking is often time-consuming, so it plausibly increases the speed with which people can travel. Assuming that criminals are more likely to live in lower value homes, more criminals per capita will reside in the poorer areas than the wealthier areas. Therefore, the theoretical model predicts that crime will increase in the wealthier area adjacent to the BCC, as compared to the control wealthy area. It predicts ambiguous results in the poorer area. The difference-in-difference results are displayed in table 1:
As displayed in the table, there is no noticeable relationship between crime and the introduction of the BCC. In wealthier residential areas, crime in close proximity to BCC increased more after the launch of the BCC than in areas farther from the BCC in 3 out of 7 months, and in poorer areas it increased in 4 out of 7 months. Furthermore, neither result is statistically significant from zero (p- values = 0.9282 and 0.2378, respectively). Therefore, the theoretical hypotheses of the inception of the BCC are not confirmed by analysis of the available data.
The lack of observed relationship between the introduction of the BCC and crime rates in
nearby neighborhoods could be due to the small sample of data used in analysis or the reality that the BCC actually does not affect crime in Durham. First, data were only used for 4 regions over 14 total months. The unavailability of data from before January 2010 confined the amount of data accessible for use in the study, but if more data were available, a potentially more significant result could be deduced. More likely, however, is that the BCC does not actually affect crime in Durham. The bus system shuts off at 10pm on Monday-Thursday, and at midnight every other day of the week, and is therefore not operational in the hours of the night when crime may conceivably take place. Also, from end to end, the BCC route is only about 3.5 miles long, so it may not provide criminals access to areas that were previously inaccessible. Duke University and Medical Center are at one end of the route, and I would suspect that people considering working at these employers would find a decrease in transportation costs, which might decrease overall crime as criminals turn to legitimate employment. An unlikely but possible explanation is that both of the regions studied were not home to criminals, and as theory would predict the shift in the distribution of crime toward these areas caused by the BCC was washed out by the decrease in the total number of criminals as former-criminals shifted to legitimate employment. Finally, as Durham’s once-impoverished areas improve, there may not be enough of a discrepancy between the number of criminals residing in the wealthier and poorer areas to establish any differential effect of the addition of the BCC.
V. Conclusion and Extensions
Many public officials believe that expanding public transportation is a solution to the existing problems of air pollution and excessive automobile traffic that current transportation methods have precipitated. Residents of currently peaceful neighborhoods counter this sentiment by arguing that the expansion of public transportation will provide criminals access to their homes. Theoretically, expansion of public transportation that results in a decrease in transportation costs should result in the shift of crime from neighborhoods in which criminals live to other neighborhoods, and also a shift from crime to legitimate employment as the travel costs associated with having a job decrease. Empirically, this relationship has rarely been found in the past, either due to a flaw in its theoretical development or a lack of available data. To test the application to Durham, data from the introduction of the Bull City Connector were collected and analyzed, but no relationship was found between the proximity to the Bull City Connector and change in crime rates after its inception.
The consideration of how changes in a city affect crime is a subject that has not been extensively researched, largely because isolating spatial effects on crime requires unique natural experiments and geo-coded data. As a result, there are many extensions for future research, even in Durham. For example, how did the construction of the Durham Bulls Park affect surrounding crime? How will the construction of Durham’s first skyscraper affect crime? How has the gentrification of Durham affected the spatial distribution of crime? Before policy decisions are made in the future, government officials should consider how their actions might change the incentives that prompt criminals to commit crimes. Only after assessing the externalities on crime, among other factors, can one truly value the effect of a change in Durham.
Appendix 1: Bull City Connector Route (photo taken from Google Maps)
Appendix 2: Areas of Data Collection (photos taken from Google Maps)
Appendix 3: Data
Data are on the number of crimes committed in the corresponding area and time period. Crimes reported are arson, assault, burglary, homicide, larceny, motor vehicle theft, robbery, and rape. Regions are defined in Appendix 2.
Block, R., & Block, C. “The Bronx and Chicago: Street robbery in the environs of rapid transit stations.” In V.
Goldsmith, P. McGuire, J. Mollenkopf, & T. Ross (Eds.), Analyzing crime patterns: Frontiers of practice. Thousand
Oaks, CA: SAGE Publications, Inc. (2000): 137-153.
Denver Regional Transportation District. “Technical Memorandum: Neighborhood vs. Station Crime Myths and Facts.”
Durham Police Department. “Crime Mapper Online Software.” Durham, 2010-2011. http://gisweb.durhamnc.gov/
GoTriangle. “Bull City Connector: First Year Report, August 2010-2011.” http://www.gotriangle.org/images/uploads/
Ihlanfeldt, Keith R. “Rail Transit and Neighborhood Crime: The Case of Atlanta, Georgia.” Southern Economic Journal 70.2
Liggett, Robin, Anastasia Loukaitou-Sideris, and Hiroyuki Iseki. “Journeys to crime: assessing the effects of a light rail
line on crime in the neighborhoods.” (2003).
Plano, Stephen L. “Transit-Generated Crime: Perception Versus Reality–A Sociogeographic Study Of Neighborhoods
Adjacent To Section B Of Baltimore Metro.” Transportation Research Record 1402 (1993).
SANDAG. “Understanding Transit’s Impact on Public Safety.” (2009).
By Lisa Wang Urban Development and the Rise of China
The monocentric urban model has long generated conclusions about the correlations between urban expansion and the fundamental building blocks of economies. As the past few decades have been accompanied by the rapid evolution of developing countries, economists have relied heavily on the monocentric model to uncover trends in these urbanizing landscapes. In particular, the scale of urbanization in China is without precedent in history. Sixty years ago, merely 15% of people in China lived in cities. Today, urban settlers comprise of 45% of the overall population, with a projection of 60% by 2030. China’s economic boom has led to a drastic transformation in its urban landscape, and with a bright economic future ahead, there is no doubt that further transformations must take place in order for China to continue its journey towards becoming a developed country.
In examining the evolution of urban landscape in China, I will focus on three studies that explore the topic from different perspectives. In the first study, Land and residential property markets in a booming: New evidence from Beijing, Siqi Zheng and Matthew E. Kahn use the urban monocentric model to examine Beijing, additionally exploring how the capitalization of local public goods contributes to urban development. In the second study, Growth, Population and Industrialization and Urban Land Expansion of China, Xiangzheng Deng, Jikun Huang, Scott Rozelle and Emi Uchida, adapts the basic empirical monocentric model to urban China, determining some of the key variables driving the country’s urban expansion. Both studies introduced above conclude that the monocentric model does align with the urban development of cities throughout China. Finally, the last paper I refer to explores the rise of “megacities,” such as Beijing, and the necessary path towards polycentricity in order to achieve continued and balanced urban development. In Beijing-the Forming of a Polycentric Megacity, Dong Zhi and Kong Chen provide a thought-provoking analysis of the problems of a monocentric Beijing, and leave us with suggestions of how rapid urban development in China can be sustained without negative effects if Beijing becomes a polycentric megacity through the inclusion of its neighbors Tianjin and Tangshan.
1.1. Urban Expansion in Beijing
China’s economic boom has sparked explosive growth of new reconstruction in Beijing’s housing market. As the capital city of China, as well as the political and cultural hub of the nation, Beijing’s population grew by 40.6% between 1991 and 2005. Consequently, this steep rise in demand for housing in Beijing has sparked escalating real estate development, instigated by long-term leases from the government (urban land is owned by Chinese government), both through negotiations as well as rigorous auctions (Zheng et al., 744). Having been born in China and personally experienced what it was like before the real estate policy changes that took place in 1988, traditionally, Beijing’s urban land was assigned through a central planning system, where housing units were built in accordance to the location of workplaces, to provide subsidized housing for employees. After the reforms, old homes in Beijing were taken down to make way for more luxurious housing and commercial projects, significantly increasing the spread of the Central Business District, which represents the area surrounding the historic TianAnMen Square. In addition, it is important to note that after the implementation of the open-door policy, the structure of the Chinese economy transformed from an agriculture-based sector to a predominantly manufacture/service-based sector, which evidently affected urban land development.
Another critical factor that needs to be taken into consideration to clearly understand the role of Beijing is that it is one of four cities (known as municipalities) in China that act as provincial entities, with the autonomous right to govern all social and economic development within their jurisdictions. Furthermore, recent reforms promoting urbanization have resulted in the implementation of trial areas known as “Special Economic Zones,” where cities like Shenzhen and Tianjin receive privileges such as tax exemption, infrastructure construction and international trade. Since the 1980s, the gradual expansion of SEZs in China has created “windows” for foreign direct investment, generating foreign exchanges through exporting products and importing advanced technologies, ultimately accelerating the process of inland economic development.
1.2 Data Sets used in Beijing
Zheng and Kahn use three data sets to test the monocentric city model. The first is a housing project data set that contains a record of 920 new housing projects, with an average of 791 housing units in each, between 2004 and 2005. The second is a land parcel data set, which includes information about land parcel auctions from 2004 to 2006 (Zheng et al., 746). Both of the data sets above are used to analyze the prices of Beijing’s land and housing projects as a function of distance to the CBD. The third data set contains information on housing projects and their proximity to local public goods such as public transportation, educational institutions, crime levels and environmental sustainability, depicting how public goods capitalize housing prices. The results of the study conclude that the monocentric model is a good representation of Beijing’s urban development.
1.3 Testing the Monocentric Model in Beijing and Variable Specification
The empirical analysis is done through estimating hedonic pricing regressions. For housing projects, j represents a project at location q in year t. For land parcels, j represents a parcel at location q in year t (Zheng et al., 751). The regressions are controlled for the region of Beijing in which the land is located, partitioning the city center into four quadrants, with TianAnMen Square as the point of origin. This ensures that the differences in the various regions that result from factors aside from distance to CBD are captured. The estimation equation run for the land parcel data found that an extra kilometer of distance from TianAnMen Square decreases the price of residential and commercial land by 4.8%. When the regression was run for residential housing, the land price gradient dropped to 4.3%, indicating a higher value of land for commercial purchases. The second estimation run for housing projects predicted a 2% decrease in price per kilometer away from the CBD (Zheng et al., 751).
Furthermore, Zheng and Kahn’s inclusion of local public goods in determining housing prices is advantageous because the location of public goods is determined exogenously in China, due to the former central planning system. After running multiple regressions, it was found that the explanatory power increases when controlling for distance to local public goods, where air quality, parks, universities and schools have an impact on home prices, while transit and crime have no significant effect (Zheng et al., 754). Since this paper was published in 2005, it would be interesting to see whether the transformation of the public subway system, which has expanded to 14 lines and now ranked fourth in the world, would be significantly relevant today.
2.1 Monocentric Model of China and Measures of Urbanization
Deng et al. tested the hypotheses of the monocentric model throughout China, through a unique three-period panel data set of high-resolution satellite imagery data and socioeconomic data for entire area of coterminous China. The testing of the model utilizes four key determinants: income, population, agricultural rental, and transportation costs. Included is also a time trend variable to control for five decades worth of data to capture the time-variant unobservable factors. Methodologically, the study relied on the OLS estimator (Deng et al.,6).
The unit of measure in the study is the county, which is the third level in the administrative hierarchy in China, below province and prefecture. With over 2000 counties in China, this analytical unit can be regarded as both an administrative and economic region, which has the power to determine its own land usage (Deng et al., 8). Within the county, areas are broken up in the urban core, rural settlements and other built-up areas, where counties with more than one urban settlement make up the urban core. Further, the expansion of the urban core throughout time is defined as the built up area. Rural settlement refers to built-up areas in small villages (Deng et al., 9).
The focus of analysis is on the urban core, and to overcome the administrative shifts in county boundaries, two counties that had been subject to border shifts would be combined into a single unit. China’s four provincial municipalities would encompass all cities within its administrative region, which resulted in a total of 2,348 analytical units (Deng et al., 11). Two key control variables are the measure of distance in kilometers from a county to the provincial capital, and the measure of distance between a county and the nearest port city. Data showed that changes in urban core was significantly associated with changes in GDP, as well as the rate of growth in industry and rate of growth of the service sector, which is consistent with the monocentric model (Deng et al., 15).
2.2 Empirical Model and Variable Specification
The equation above models the differences in the spatial scale of cities across space and over time, where UrbanCore is the total area found in the ithcounty in year t. The explanatory variables include GDP, Population, AgriInvest (measure of investment allocated to agriculture is proxy for rent) and HwyDensity (proxy for commuting costs). The measure of industrialization is constructed as the value of GDP from the industrial sector divided by total GDP (GDP2_share), and the same measure
was created for the service sector (GDP3_share). Control variables include climate, elevation, terrain and distances from provincial capitals and port cities (Deng et al., 23).
The results of the multivariate analysis show that growth in GDP is highly correlated to the expansion of the urban core, with a coefficient of 0.397, representing at 3.97% expansion of the urban core for every 10% growth in GDP. Population is significant and positive with a coefficient of 0.057 while AgriInvest is negative, which is in accordance with the monocentric model. The coefficient on transportation cost is also positive, indicating that when transportation networks are well developed and commuting costs are low, the urban core should expand more. Further, when geophysical factors are included in the model, the coefficients of the variables retain their same signs (Deng et al., 26). Ultimately, the paper concludes that when the monocentric model is applied to cities in China, there is fairly high explanatory power, highlighting that if China wants to continue growing at high rates, urban expansion is essential.
3.1 Beijing’s Path to Polycentricity
In contrast to the studies above, Dong and Kong examine the transformation of Beijing into a “megacity,” an emergent concept that falls under a subgroup of a metropolitan region with over ten million people (Dong & Kong, 13). In the recent decades, the emergence of Asian megacities have surpassed the growth rate of those in developed regions, with an average population density of 8,800 persons per km2, double that of developed countries. The paper delineates the advantage of transforming Beijing from a monocentric megacity with a primary CBD, to a polycentric region comprised of Beijing, Tianjin and Tangshan (BTT) (Dong & Kong, 10). The monocentric Beijing megacity model has many flaws, including the capital city’s unique restrictions on development, the intensification of traffic jams, decreased quality of life due to high population density, and the destruction of historical monuments (Dong & Kong, 48). The polycentric transformation of Beijing is a natural advantage that will increase the quality of labor force, release traffic congestion, create access to more raw resources, halt the urban development contaminating the vicinity of the historic center, and ultimately balance urban growth through each region’s specializations. The original plan of transforming Beijing into an international exchange center aligns with the development of a polycentric city, where economic functions can be transferred to areas within the polycentric Beijing and ease the excessive functional pressure within the city (Dong & Kong, 53). The urbanization of many Chinese regions similar to the BTT will also have to adopt a polycentric model in order to achieve balanced development.
In an age of rapid growth and expansion in the developing world, it is imperative to evaluate the efficacy of smart growth conditions to ensure sustainable urban development. After two decades of rapid economic growth, urbanization in China threatens to produce damage to the environment, shortage of land resources, and social inequality. Through the investigation of the various studies presented above, I aim to uncover some of the fundamental factors that impact urban growth in Chinese cities, in accordance to classical urban economic models. To further this discourse, I have touched upon some of the concerns illuminated by Dong & Kong, regarding the sustainability of the monocentric model in megacities such as Beijing, and the benefits of moving towards a polycentric model. As large megacities such as Beijing continue to expand, polycentric urban development seems to be a natural transition, improving commuting patterns, reducing congestion, lowering development costs and increasing administrative efficiency.
1. Deng, Xiangzheng, Jikun Huang, Scott Rozelle, and Emi Uchida. “Growth, Population and Industrialization and Urban Land Expansion of China.” (2006): 1-39. Print.
2. Zheng, Siqi, and Matthew E. Kahn. “Land and Residential property markets in a booming economy: New evidence from Beijing.”Journal of Urban Economics 63 (2007): 743-57. Print.
3. Zhi, Dong, and Kong Chen. “Beijing- the Forming of a Polycentric Megacity.” (2011): 1-73. Print.