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By Carmen Augustine LR_AUGUSTINECARMEN
A life-long Durham resident, I seek to learn more about the cultural and historical roots of my home. Durham has a unique reputation as an up-and-coming city worthy of New York Times appraisal and the curiosity of hipsters nationwide. My Duke friends tease that they wouldn’t venture off campus for fear of Durhamite encounter, crime or lackluster dining. I find Durham to be quite the contrary: vibrant, growing and delicious. A self-proclaimed gourmet, I have become more and more infatuated with the Durham dining scene through college. Ethnic restaurants are more prominent than I recall from my childhood, and the quality of dining has unambiguously improved. Downtown Durham more closely resembles Carrboro, with restaurants supporting local farmers and vegan diets, than the grungy urban sprawl it once was. My curiosity is endless. How does a shop like Scratch exist in the same city that scares pastry-loving Duke students? Who created the concept of Watt’s Grocery? Why did Magnolia Grill close? Who moved here to open these eateries, and who are the people keeping them in business today?
My obsession with food aside, the question can be initially expanded to national demographic trends—what factors cause Durham to be an urban hub in the South? Historically speaking, the South is not a particularly attractive destination for business. Since before the civil rights movement, the South has struggled to maintain a constant influx of business. Wright (1987) posits a model of Southern out-migration explained by rocky assimilation with Northern industrial production. A dramatic decline in low-income farm laborers forced a majority of the ethnic population out of the South.
However, Frey and Liaw’s model of racial migration trends suggests the possibility of an established minority network in the South. Their model of out-migration and destination selection suggests that minority groups tend to stay in areas that have large populations of their minority group and move toward areas with similarly established minority networks. Movement into the South has been striking across all minority groups studied, a testament to the growing economy and employment opportunities in the South. Combining Wright’s theory with Frey and Liaw’s model, I see the potential for Durham’s economic success to be explained by a flight to economic growth combined with a return of black laborers to what could be considered an informal ethnic network, a historical home base.
Zhao’s model predicting discrimination in real estate suggests that brokers of the same racial-ethnic status as their client tend to discriminate less, a testament to the cyclical potential of movement into the South. As immigration continues, the ethnic network grows and racial similarity becomes a larger factor in attracting and retaining racial minorities.
These three surveys open a discussion of the causes of Southern economic improvement but do not fully explain my question of why there are so many boutique eateries in Durham. In further investigations, I hope to answer the following additional questions: What is the ethnic makeup of migrants to Durham? Are minorities a majority in Durham? What is the demographic profile of Durham small business owners? Does the desire for racial similarity drive migration and economic growth, or is it the economic growth that fosters migration? I hypothesize that the economic growth in the South combined with its attractiveness to immigrants and minority groups has created an environment that fosters small business and boutique ethnic eateries.
Wright outlines a model more qualitative than the econometric models of Zhao and Frey and Liaw. Wright poses the question: why can’t macroeconomic factors explain the homogenization of Northern and Southern US economies, and why did the Southern economy finally become assimilated with the North? Historically, he notes that labor flows have in general occurred in an East to West direction rather than North to South, a trend “…rooted in certain geo-agricultural continuities, such as familiarity with seeds, crops, livestock, and climate.” (Wright, 164) As the North continued to receive international immigrants and technological improvements, the South was geographically isolated from this influx of productivity. The dynamic was compounded by the fact that Northern wage rates far exceeded those in the South, making economic assimilation more of a daunting task. As the North grew, Northern producers had little incentive to expand into the South because it was “…much cheaper to utilize existing channels or expand them incrementally than to lay out the large fixed cost that would have been required to redirect the established lines of the market.” (Wright, 164) The South became stagnant, despite the potential for American unity provided by the booming machine tool industry.
The largest constraint to assimilation was the large gap between Northern and Southern wage rates, and as national wage policies were implemented the gap closed. This was overall productive for the Southern economy—in-migration of educated Northerners increased, out-migration of farm laborers increased and integration began. However, this was harder on low-income workers and “…the majority of the departing farm population had few options other than leaving the South.” (Wright, 172) Particularly hard hit was the black population due to the combined reduction in agriculture and tobacco manufacture—the black share of labor force more than halved in the former Confederacy from 1930 to 1960. On the flip side, Northern migration created opportunity for education, “…yet the same migration channeled other blacks into the high-unemployment ghettos which if anything have worsened with the passage of time.” (Wright, 175)
Frey and Liaw (2005) paint a contrary picture 20 years later. Their extensive study of the effect that racial/ethnic background has on migration patterns concluded that minority migration is occurring in a general Southward direction, particularly within the black population. Frey and Liaw seek to understand the effect of two separate theories of migration. The cultural constraints theory suggests that migration networks are shaped by racial and ethnic attachment. Spatial assimilation, on the other hand, suggests that education and socioeconomic status become larger determinants of migration in the upper-middle class, even within minority populations.
Frey and Liaw consider two components of migration separately—decision to migrate out of a location (“out-migration”), and the choice of destination location. They formulate a two-level nested logit model to show the effect of observable explanatory variables on the probability of out-migration and destination choice at the state level. Study conclusions were focused predominantly on migration patterns into and out of California, but will be omitted in the context of this survey.
Overall US out-migration was found to be reduced by the cultural constraints hypothesis, with education level playing a minimal role—“…these [racial similarity] constraints do not play a stronger role for less educated than more educated members of these [coethnic] groups.” (Frey and Liaw, 236) Destination selection is also affected by racial similarity, with “…positive effects on migrant destination selections for each race-ethnic group” (Frey and Liaw, 241). However, distance from previous location, contiguity and population size along with employment growth rate affect destination selection more strongly than racial similarity.
Two of their findings provide a possible explanation for minority (and black in particular) migration back to the south and are thus most relevant with respect to my topic: first, the finding that “both persons born in a different state and the foreign-born are more likely to out-migrate than persons born in the same state” (Frey and Liaw, 240) and second, the fact that destination selection is positively affected by racial similarity. Aside from cultural constraints and spatial assimilation, the human capital investment theory of migration is supported in the finding that less out-migration occurs as employment growth rate and per capita income increase. This may explain the success of the Southern economy, as it is able to attract and retain laborers.
Zhao (2005) investigates the question of whether number of homes showed by a broker varies with the homeseeker’s race, a proxy for racial discrimination in the housing market. He conducted a paired experiment in which two auditors of similar gender and age but different minority status (one white and one minority) were assigned similar income levels, marital status and parental status and sent to the same real estate agency to be shown available houses. Zhao built off of Page’s 1995 Poisson model with fixed effects, which established a relationship between auditor characteristics and discrimination. This study did not predict discrimination using a more complex model that incorporated auditor and agent characteristics and interaction terms. Zhao expands Page’s basic model to include visiting order, agent characteristics, actual auditor socioeconomic characteristics, as well as interaction terms between race and auditor characteristics, agent characteristics, house value, neighborhood characteristics, month of visit and site of home. He interprets each variable’s coefficient as the effect of the variable on the number of houses shown to the auditor, testing each coefficient for significance in the complete model (including all variables and interaction terms above).
Zhao additionally hypothesizes three potential causes of discrimination. First, broker’s prejudice, summarized as “distaste for minorities.” (Zhao, 135) White customer’s prejudice is defined as discrimination by brokers based on perceived desires of whites in the local community. For instance, if a broker has a large base of white customers he or she might act to satisfy their needs rather than the minority client. Finally, statistical discrimination involves prediction of minority behavior based on the probability of a transaction occurring. If the broker believes a minority buyer would not be interested in a residential area with a large white population or would not be able to pay for a home, he or she may be less likely to show the house.
Focusing on the black/white paired samples, Zhao finds that the customer prejudice hypothesis is true to the extent that discrimination decreases once the share of black residents increases. Additionally, discrimination increases with percent of owner-occupied homes and with value of house. Black homeseekers are shown overall 30% fewer homes than white homeseekers, which reflects an increase in discrimination of 12% since 1989 (Zhao, 144). Though the South is quickly re-establishing itself as an economic hub, there may be evidence of racial discrimination that limits economic development in the real estate market.
Bo Zhao, 2005. “Does the number of houses a broker shows depend on a homeseeker’s race?” Journal of Urban Economics 57(1): 128-147.
Gavin Wright. 1987. “The economic revolution in the American south.” Journal of Economic Perspectives 1(1): 161-178.
William H. Frey and Kao-Lee Liaw, 2005. “Migration within the United States: role of race-ethnicity,” BWPUA 2005: 207-248.
This includes race, immigrant status, age, immigration rate, employment growth rate, population size and housing value, along with a number of other variables and interaction terms
by Carmen Augustine TP_AUGUSTINECARMEN
The question of whether black renters and homeowners face a different set of housing prices than their white equivalents is addressed in several studies through the period of integration and beyond. Past studies have drawn conflicting conclusions, some pointing toward a discount for black housing and others suggesting there is a premium paid by blacks. The present study is a response to Follain and Malpezzi’s 1981 review of racial price differentials in Chicago from 1974-1976, which reported significant discounts to black renters and owners in a majority of regions in Chicago. The survey omitted racial composition of each region as well as neighborhood control variables, which Chambers believes may have a significant effect on price differentials. For example, it may be the case that black renters and owners more often live in older or otherwise less desirable neighborhoods, which are factors outside of race that would cause the price of housing to be reduced.
Chambers additionally distinguishes between three types of racial housing differentials. A household differential exists “if black households pay more than white households for the same housing in the same market.” (Chambers, 216) Another cause of differential may be exclusion of black families from certain neighborhoods, raising demand for housing (and with it prices) in neighborhoods supporting a larger black population. The third cause is white households that prefer areas of more homogenous racial composition, causing housing demand to go up in less integrated areas and with it prices.
In order to more thoroughly assess the racial price differential, Chambers uses data from the 1975 Annual Housing Survey for Chicago as well as linked data from the 1979 survey to look at the effect of housing structure, neighborhood characteristics, renter/owner racial characteristics and neighborhood racial characteristics on the price of housing. This is an extension of the 1981 Follain and Malpezzi model that excluded both racial and non-racial neighborhood characteristics.
Presentation of the Model
Chambers first presents the following model to describe changes in housing price, Ei:
Ei = f(Hi, Zj, BLACKi, RCBj)
Where H represents housing structure characteristics, Z represents non-racial neighborhood characteristics, BLACK represents race of the renter or owner (a dummy variable equal to 1 if head of the household is black) and RCB is the percentage of housing units occupied by a black household. The subscript i represents one housing unit and the subscript j represents one neighborhood. H is a vector that includes many variables describing housing—number of rooms, presence of amenities such as air-conditioning and heating, parking and age (for a complete list, see Chambers 228-230). Z is a vector that includes average aggregate variables for a neighborhood, including household income, education level of the household head, neighborhood quality, property tax rate and others (for a complete list, see Chambers 231).
The model is estimated as a linear semi-log equation:
(1) ln(Ei) = a + bHi + cZj + dBLACKi + eRCBj + u1
Where each of the variables b, c, d and e are a coefficient on one of the independent variables and u1 is an error term. In relation to the comparable Follain and Malpezzi equation:
ln(Ei) = a + bHi + dBLACKi + u2
It can easily be seen that omitting the variables Z and RCB would cause the error term, u2, to be much higher than u1 if Z and RCB are significant. This is because if those two variables do in fact affect housing price, their effect will turn up in u2. Additionally, if either Z or RCB is correlated with H or BLACK we can state that the independent variables are endogenous in the Follain and Malpezzi model, which violates an assumption of OLS estimation and implies that our estimated coefficients (b and d) are biased. Chambers further speculates that the effect of omitting Z and RCB is a downward bias on the coefficient d. Z and BLACK are negatively correlated, and we anticipate the coefficient on Z to be positive (as neighborhood quality improves, price increases) yielding a negative bias. Similarly, RCB and BLACK are positively correlated but we anticipate the coefficient on RCB to be negative (as percentage black increases price of housing decreases), causing an additional negative bias. Thus, we expect Follain and Malpezzi to have lower estimates of the effect of race on housing price than the complete model.
Using data available from both 1975 and 1979, Chambers estimates the restricted Follain and Malpezzi model as well as his revised model. He finds that omitted variables bias the coefficients on BLACK and RCB downward. In the complete specification, the effect of race on rental prices almost disappears (drops to 0.3% premium in 1975 and 0.2% in 1979) and the effect of race on housing prices for owners drops significantly in both years, where it was strongly negative in the incomplete model. The effect of RCB on rental and housing price is negative in all cases except that for renters in 1975, suggesting that as the percentage of units occupied by blacks increases cost of housing decreases.
The results suggest that the observed black household discount is likely a result of racial composition and other non-racial amenities rather than race of renter or owner.
Addition of Racial Submarkets
Chambers breaks down housing price further by racial submarkets, speculating that each region may have an independent average price of housing and thus regions should not be aggregated across the entire Chicago area. Submarkets are divided into black ghetto with an average of 81% black occupied housing, black border with an average 27% black occupancy, Spanish submarket which is predominately Spanish but has 3% black occupancy and white interior submarket with 2% average black occupancy.
The updated model includes housing unit i, neighborhood j and submarket k as follows:
(2) ln(Ei) = a + bHik + cZj + dBLACKik + eRCBj + fRCBSQj + gSUBk + u3
The terms added in this iteration of the model include RCBSQ, which is a quadratic term reflecting racial composition, and SUB which is a dummy variable for each submarket. The coefficient g on SUB displays price differentials between submarkets.
The 1975 results suggest that rental prices are higher for black renters than white renters in the ghetto and Spanish submarkets, and lower for black renters in the black border and white interior submarkets. This is somewhat aligned with the theory that increased demand in areas supporting a larger black population cause prices to rise. In 1979 there is no black renter premium in the ghetto submarket, but a premium emerges in the black border submarket, which may reflect an “entrance fee” into communities bordering on racial transition.
For owners, the findings suggest that black households are given a discount in ghetto, black border, and white interior submarkets in both 1975 and 1979. This is not consistent with the “entrance fee” hypothesis, and may instead be a result of black owners purchasing homes in racially transitional neighborhoods within a submarket that are in lower demand.
The relationship between racial composition of the neighborhood and price was inconsistent across years for both owners and renters based on both the RCB and RCBSQ terms. Additionally it is not clear that a price differential exists between submarkets, as evidenced by the changing sign of coefficients on the dummy variables GHETTO and BLKBORD, representing homes in ghetto or black border submarkets.
Addition of Racial Transition Factor
To take into account the hypothesized discount due to racial transitioning within a neighborhood, Chambers adds a factor RTB to model (2) based on change in percentage of neighborhood population that is black from 1975 to 1979.
(3) ln(Ei) = a + bHik + cZj + dBLACKik + eRCBj + fRCBSQj + gSUBk hRTBj + iRTSj+ u3
The coefficient on RTB is negative and statistically significant, indicating that there is a discount on housing based on the level of racial transition within the neighborhood—neighborhoods experiencing more transition over the period had overall lower housing prices for both renters and owners. Looking at the same transition in percent occupied by Spanish residents (RTS), there is also a significant discount in housing price in neighborhoods with higher RTS.
Implications and Extensions
The study suggests that racial housing differentials are overstated in models that fail to include racial and non-racial neighborhood characteristics. The addition of a racial transition factor specifies that neighborhoods experiencing higher rates of racial transition offer an overall price discount, which may account for some of the observed price discount to black households.
The discount in racially transitioning areas implies lower demand for housing, which in turn suggests that integration of black and white households may be encountering some social barriers—whether consciously or not, households prefer to be in racially homogenous areas. One obvious extension of the present model is to look at racial price differentials in more recent years, as well as the 90s and the changes over the entire life of the study (1975-present). Additionally, it would be interesting to extend the survey beyond Chicago and look at other racially integrated urban areas and the difference between cities—for example, whether integration has been more complete in the northern or southern US and whether certain cities are more open to integration than others. I would also be interested in seeing the application of model (3) to Asian minorities in cities receiving a larger volume of Asian immigrants.
D.M. Chambers. 1992 (September). “The racial housing price differential in racially transitional neighborhoods.” Journal of Urban Economics: 214-232.