Category Archives: 2013 – Technical presentations
by Lauren Taylor TP_LaurenTaylor
 Background Information:
The process of migration is largely affected by spatial differences in economic opportunities. In the typical model explaining the movement of migrants,
key factors include wage levels, unemployment rates, distance, origin, destination, quality of life, housing cost disparities, and personal characteristics
of migrants such as age and education. It is also important to recognize the effects of racial and ethnic background on migration in the United States
today. One such model by William H. Frey and Kao-Lee Liaw analyzes the role that race-ethnicity plays in migration within the United States.
[1.1] Focus of Frey and Liaw’s study:
Frey and Liaw’s study focuses on analyzing interstate migration. In their model, Frey and Liaw assess the role of cultural constraints in migrants’
departure and destination choices. Cultural constraints have an impact on migrants’ choices because of migrants’ needs for social support networks, kinship
ties, and access to informal employment opportunities that arise from within cultural communities. Frey and Liaw additionally analyze the impact of
low-skilled immigration on domestic out-migration from urbanized, high-immigrant states.
[1.2] Initial conclusions:
In their analysis of interstate migration of young adults between 1985 and 1990, Frey and Liaw conclude that the presence of communities of same-race
residents reduces out-migration and attracts Hispanics, Asians, and blacks, particularly those who are foreign-born, who are choosing a destination to move
to. This cultural constraints argument implies that states with high same-race concentrations should be major destinations for migrants. Furthermore, in
considering the recent dispersion of minorities, Frey and Liaw suggest that the theory of spatial assimilation may also be at play in addition to the
cultural constraints theory.
[1.3] Evidence of cultural constraints and spatial assimilation from Frey and Liaw’s previous studies:
Immigrant minorities typically follow well-worn migration patterns that are shaped by racial and ethnic attachments and migration networks. These
traditional patterns are often motivated by employment opportunities and social support provided by networks of individuals of similar backgrounds.
Interestingly, research also shows that the attractiveness of same-race neighbors is decreasing among more well-educated and well-off blacks. Instead,
these individuals choose to relocate to more prosperous cities instead of traditional areas with large black concentrations. In such instances, spatial
assimilation may be occurring. Spatial assimilation involves individuals choosing to move to a new location that is more economically advantaged and has a
better quality of life instead of an area with large residential concentrations of those individuals’ same ethnic-race group. It is also interesting to
note that more assimilated, long-term residents show lower segregation in their housing decisions. Frey and Liaw state that they anticipate the effects of
cultural constraints on migration to be reduced somewhat by spatial assimilation considerations. In particular, the presence of large, same race-ethnic
residential areas in a state will reduce residents’ tendencies to leave the area; however, these patterns will be weaker for more highly educated and
native born, rather than foreign-born, members of these groups.
[1.4] Other factors in migration:
In previous studies, Frey and Liaw find that the influx of low-skilled immigrants into a state causes an outflow of low-skilled domestic migrants to
neighboring states. Migrants’ decisions to leave a state when there is an influx of immigrants is more common for low-skilled and poor whites than for any
another race and ethnic group. Increasing housing prices in high-immigration states also causes out-migration and middle-class flight. Figure 1 shows that
states that gain the most foreign immigrants are also the states that lose the most domestic migrants. Conversely, states that gain the most domestic
migrants are not among the states that gain the most immigrants. Additionally, Hispanic and Asian populations are much more concentrated in the United
States than those of whites and blacks. 70% and 60% of the nation’s population of Hispanics and Asians, respectively, reside in five states, while only
about 30% of whites’ and blacks’ total populations live in five states.
[1.5] Education as an indicator of migration:
College graduates are more likely to migrate than individuals who are less educated. Additionally, college graduates choose to move to different locations
than those without college degrees. For example, California is a top destination for college graduates while neighboring Arizona and Nevada are top gainers
of individuals with high-school educations or lower. Frey and Liaw also noted that college graduates have more focused destinations while people with
lesser educations are far more dispersed across the United States. However, members of both groups generally tend to choose southeastern destinations.
 Modeling Interstate Migration:
To conduct their research, Frey and Liaw fit a two-level nested logit model to a highly disaggregated table generated from the 5 percent Public Use
Microsamples of the 2000 U.S. Census. Using a two-level model enabled them to separately determine how origin area and personal characteristics affect
departure decisions and how destination area and personal characteristics affect migrants’ destination choices. When looking at area attributes of a given
location, Frey and Liaw put particular emphasis on observing the impacts of cultural constraints, spatial assimilation, and middle-class flight. The
overall goal of their analysis is to determine the role that race-ethnicity plays in out-migration and destination selection by observing interactions, or
lack of interactions, between race-ethnic categories and different area attributes.
[2.1] Area attributes considered include:
· low-skilled, foreign-born immigration rate
· total employment growth rate
· service employment growth rate
· unemployment rate
· racial similarity
· median housing value
· distance to next state
· contiguity to next state
· population size of origin and destination location
[2.2] Personal characteristics considered include:
- poverty status
- place of birth
· state of residence in 1995
· state of residence in 2000
 The model:
A two-level nested logit model was used in Frey and Liaw’s analysis. In this model, a given potential migrant has personal attributes, s, and lives in
state, i. Migration behavior depends on departure probability, P(s, i), in the departure submodel and a set of destination choice probabilities, P(j|s, i)
for all j ≠ i, in the destination choice submodel. These probabilities become functions of observable explanatory variables in the two submodels
the inclusive variable defined in (3) reflects the attractiveness of the rest of the system from the perspective of the potential migrant with personal
attributes, s, and living in state, i, in 1995.
[3.1] Model details:
In this model, unknown coefficients in equations (1) and (2) are estimated by the maximum quasi-likelihood method. The dummy variables that represent
personal characteristics are entered into each submodel as interactions with the variables that represent area attributes. The farther away from zero,
either negatively or positively, the resulting coefficient is, the more responsive the personal characteristic is to the area attribute in question.
Furthermore, these interactions are used to determine if a given variable has a significant effect on each of the racial categories of migrants. The two
submodels described above indicate the probability of departure and destination selection given certain personal and area characteristics. Of equal, if not
greater, importance in this study are the Rho-square calculations. These Rho-squares are used to determine the goodness of fit of a given specification of
the submodel and the equation as follows:
Rho-squares are particularly useful in determining the importance of a given subset of explanatory variables against another subset. To do this, each
subset is deleted in turn from the specification and the resulting decreases in Rho-squares are then compared. A greater decrease in Rho-square indicates
that the deleted subset of variables, for example racial similarity, is of greater importance.
 Importance of findings and implications:
This model is important because it helps develop an understanding of the effect of race-ethnicity on individuals’ movement choices across labor markets.
Immigration and its effects on the U.S. economy are important to study now more than ever because of the increased immigration of individuals to the United
States in the past several decades. Furthermore, individuals across different races and ethnicities have different preferences and thus may act differently
than the population as a whole. Thus, traditional labor market migration models no longer adequately explain migration across labor markets. With the
United States becoming even more racially and ethnically diverse, a model such as Frey and Liaw’s that incorporates race-ethnicity in its analysis of
domestic migration patterns is necessary.
Frey and Liaw’s model found that cultural constraints reduce out-migration from places with large same-race concentrations, but contrary to Frey and Liaw’s
hypothesis, this result is not affected by the education level of migrants. Therefore, the departure submodel does not follow the
expected spatial assimilation model. Additionally, it was determined from the data that both high low-skilled immigration rates and median housing values
promoted out-migration for all race-ethnic groups but particularly affected those who had less than a college-level education. Furthermore, individuals who
are older, less-educated, non-poor, and born in the state they live in are less likely to move. Lastly, the Rho-squares indicate that migrant departure can
be explained more by the racial similarity of a given area than by the labor market variables or housing value there.
Frey and Liaw found that both the cultural constraints model and spatial assimilation affect migrants’ destination selection. In other words, migrants of a
particular race-ethnic group are more attracted to areas with large concentrations of others in their ethnic group, and especially attracted if these
migrants’ have only a high-school education or less. Furthermore, high low-skilled immigration rates and housing values act to dissuade worse-educated
migrants when selecting destinations. Additionally, areas with increasing total employment growth rates and income per capita attract migrants. The
Rho-square calculations indicate that areas of racially similar composition (Rho-square of 0.0059) play a more important role than the roles of climate or
housing prices. However, together immigration and labor market variables have a greater impact than the effect of racial similarity (the joint Rho-square
value is 0.0101). Thus, when determining a location to move to, migrants largely value not only racial composition but also immigration rates and other
labor market variables in their decision.
 Possible extensions:
The racial composition of the United States has changed quite drastically in the past several decades. Thus, it is important to adjust traditional economic
models, such as the one describing migration patterns, to incorporate the effect that race-ethnicity plays in these models. Furthermore, the immigration
debate has emerged in the news and in public policy as an important issue in the past few years. Frey and Liaw’s study of the impact of race-ethnicity, in
addition to other factors, on migration patterns in the United States provides important additions to traditional models of migration. However, because
their study used data from 1995-2000, it is important to conduct further studies which incorporate data from more recent years, particularly because annual
immigration rates have increased since 2000 (Migration Policy Institute, 2007). It is also important to analyze the impact of race-migration patterns on
the economies and social structures of both the communities that migrants leave and move to. Studies analyzing the effects of race-ethnicity on local
economies and social structures will be useful in this discussion of migration within the United States.
Frey, William H. and Kao-Lee Liaw. “Migration within the United States: Role of Race-Ethnicity”. Brookings-Wharton Papers on Urban Affairs, 2005,
Migration Policy Institute. “Annual Immigration to the United States: The Real Numbers”. 2007.
by Michael Rebuck
Prior to the 2008 Beijing Olympics, the Beijing metropolitan government strategically placed enormous investments to increase green space and to improve public transit in southern Beijing. Siqi Zheng and Matthew E. Kahn, The authors of the paper entitled “Does Government Investment in Local Public Goods Spur Gentrification? Evidence from Beijing?” define gentrification as, “taking place when a geographical area undergoes an increase in its quality of private-sector economic activity as shown by rising local home prices, new housing construction and new restaurant openings”. They find that the investments made by the Beijing municipal government do indeed cause local gentrification. In these areas, richer people have moved in and have continued to spur the gentrification process. Public investment combines with private investment to synergistically transform the local area.
Their paper simultaneously studies the effects of new public transit (and its access to a central business) and new investments in green space. Several papers have previously examined the effects of these two investments, and the work of Zheng and Kahn contributes to these findings. To test the gentrification claims, Zheng and Kahn utilized the results of three pieces of data to give a more complete analysis of the gentrification. The first piece of evidence comes from real estate prices. Secondly, they study the geographical patterns of new residential projects and restaurant openings. The third piece of evidence focuses on demographic changes by zone. Equation 1 is the following:
This equation uses hedonic regression to examine whether the local infrastructure improvements are capitalized in land price and residential property price. Hedonic regression splits the researched item into its constituent components and uses estimates of their contributory value. The basic premise is that the price of something is related to its characteristics, and each of those characteristics has some measurable value. In this particular example the unit of analysis is a residential property project j located in zone z in quarter t. (αz, Φt) are used to account for zone and quarter fixed effects and Xjz is used to account for project-specific attributes that are time-invariant. The subscript sb symbolizes the different types of subways (old subway lines, new subway lines, unbuilt subway lines). To account for the suburbanization effect the CBD price gradient is allowed to vary over time. In this case, t counts the number of quarters since 2006Q1 and there is a linear time trend (a x t). Distance to Subway varies over time for a given location. For example, when a new subway line is built, this value will shrink. Distance to Olympics is time-invariant but increases in value as the construction of the Olympic Park nears completion.
The authors hypothesize that the CBD price gradient will be negative, meaning that the further an area is from access to the CBD, the cheaper the land will be. The CBD price gradient will be smaller when the proximity to a nearby old subway stop is included, and perhaps will even become insignificant. The price gradient is also expected to be negative with regard to distance to the Olympics. These are but a few observations that can be intuitively made through an examination of this model. However, there are several extensions of this equation that can be performed. One example, with respect to new subway construction, is that one could determine whether a shift in price happens when construction starts or when construction ends (or both). Equation 2 is the following:
In this example, count regression models are used to study the spatial distribution of new housing supply and new restaurant openings. This is used to determine what areas are attractive to real estate developers. The unit of analysis in this particular case is zone/ quarter for residential projects and zone/ year for restaurants. An increase in density in either is seen as a sign of gentrification. Both real estate developers and restaurants have a strong incentive to locate houses/ stores in areas where there are customers. Negative binomial regressions are used because there are only two possible outcomes, more or less housing/stores.
In the above regression equation, time-fixed effects are accounted for with Φt. The gradient with respect to the distance to the CBD varies by quadrant and changes over time (again the suburbanization effect). b2t is a linear function of t and therefore b2t=b2×t. Xz is used to account for zone-specific attributes that are time-invariant. The anticipated results should be similar to problem 1. The difference is that we are now measuring for housing units/ restaurants offered rather than measuring for the price of land/ housing. A negative price gradient with regard to distance from the subway is expected. A negative price gradient with regards to distance to Olympics is also expected. Equation 3 is the following:
The third equation focuses on demographic changes by zone. In the Beijing example it was difficult to obtain micro household data with geographic identifiers. These data constraints deny the authors the opportunity to perform micro-level regressions and they are also unable to control for some household demographic attributes. The evidence therefore should only been viewed as “suggestive.” However, the authors were able to acquire zone-level average annual household income (incomez) and the household head’s years of schooling (eduz). By using a geographic unit (zones), it is possible to undergo parsimonious regressions (simple regressions) to test whether the zones close to the Olympic Park and/ or the new subway stops have shifted towards higher-income residents with higher levels of education.
In the above equation, the dependent variable Yzt is the zone-level average of annual household income (incomez) and household head’s years of schooling (eduz). Quadrant-fixed effects are accounted for by . Y2010 is 1 for the year 2010 and c1 is the average income/schooling growth rate. In this example, it is calculated from years 2007 and 2010 (the years the data is collected). Zheng and Kahn test whether the spatial gradients with respect to the distance to CBD, distance to the closest old subway stop, and distance to the Olympics have changed by including the interaction terms of the distance variables and Y2010 (ex: c3 x Y2010 x Distance to CBDz). In a different example, any two times (for which data has been collected) can be used in order to examine a change over the difference in duration. No interaction term is included this time for Distance to New Subwayzt because it is time-variant. The effect becomes smaller when new subway stop is opened nearby).
The authors obtained their data from a variety of sources. When studying the spatial distribution of new housing construction the micro transaction data they used was obtained through a private relationship with the Beijing Municipal Housing Authority. The time period used is only from the first quarter in 2006 to the fourth quarter in 2008, but this could no be avoided because transaction data prior to 2006 could not be converted into electronic form. Additionally, the authors had to identify whether the housing developed was a state-owned enterprise (SOE) or an auction type, because SOE developers may be able to obtain inside information on urban planning details. Slightly more than 30% of real-estate developers are SOE and they have been shown to buy land leaseholds at slightly higher prices. For data on restaurant cuisine and patronage, the authors constructed their own indicators by using the most famous food guide and review website www.dianping.com. The authors identified 33 chain restaurants (like McDonald’s and Starbucks) that target rich Chinese urban customers. The authors then noted the restaurants’ location and opening date information.
The authors’ research indicates gentrification was caused by the Beijing government’s investments in local public goods. Local home prices increased, developers increased their construction and more restaurants of higher quality opened nearby. Demographic data suggests that high-income and more highly educated households are attracted to areas where government investment in local public goods has been made. These three pieces of evidence support the assertion that government investment and private sector investment serve as complements that can gentrify previously underdeveloped areas.
In conclusion, government infrastructure projects have dramatic effects on local markets. Decisions about where to place these projects have enormous consequences on the surrounding communities. The models introduced in this paper do not have to be used exclusively for subway projects or Olympic facilities. Any number of projects can be substituted with some minor adjustments, and their effects do not always have to have a negative price gradient. For example, the construction of a dump or of a nuclear facility could be reasonably expected to have a very high price gradient. I especially enjoyed this paper because it tested its hypothesis in a variety of ways. It used price data, count data, and demographic data. Through the use of regression analysis the model gives an in-depth look into the issue of gentrification.
The full document can be found here
Zheng, Siqi, and Matthew E. Kahn. “Does Government Investment in Local Public Goods Spur Gentrification? Evidence from Beijing.” Real Estate Economics 41.1 (2013): 1-28. Web.
by Stephanie Xu TP_XuStephanie
Fosgerau, Mogens and Andre de Palma. “Congestion in a city with a central bottleneck,” Journal of
71 (2012), pp. 269-277.
Road congestion is a classic and every day example of the tragedy of the commons, where a public good is provided at such a negligible cost that consumers
have no incentive to avoid overconsumption. In this case of public highways, public goods problems create traffic congestion, which in turn has effects
upon time, productivity, and utility losses on both an individual and societal level. Well-known solutions to this problem include road pricing through
tolls and privatization of roads.
In this model, Fosgerau and de Palma construct the following scenario, and ultimately offer insights regarding potential solutions to the problem of
congestion. There exists a downtown bottleneck through which N number of individuals must pass to reach their destination.
Fosgerau and de Palma assert that congestion occurs when travelers have similar scheduling and timing preferences, and when travelers’ trip origins are
within similar distances from the bottleneck. Finally, they compare the impacts of both a laissez-faire, or no-toll, system of management and a social
optimum form of road pricing.
First, we make the following assumptions. The city has N individuals, spatially distributed around a bottleneck site. Each
individual has preferences such that their utility is a concave function that decreases as their overall commute time increases. Their trip duration is the
sum of the distance (in time) it takes to get to the bottleneck and the time spent in the bottleneck. The social welfare function used in this model does
not integrate the effects of toll revenue and the utility such revenue may bring to toll-payers.
In a laissez-faire model, individuals arrive at the bottleneck during some interval [a0, a1], where a is the arrival time. Cumulative arrivals are calculated such that R(a) is the integral of the time
varying rate. As an example, suppose there is a queue at the bottleneck from time a0 to a.
An individual who arrives at the entrance of the bottleneck at time a will arrive at his destination at time R(a) Y + a0, where Y is the bottleneck’s maximum capacity of people per time unit. Thus it takes
R(a) travelers in front of this traveler time to pass through the bottleneck.
Fosgerau and de Palma prove the theorem that equilibrium can only exist in a laissez-faire situation. Individuals located a distance of c from the bottleneck will arrive at the bottleneck at time a(c), where:
F represents the cumulative distribution of travel distances; f is the density. The utility functions represent the scheduling
utilities as functions of arrival time at the bottleneck and travel distance to the bottleneck. Intuitively, a commuter’s utility depends upon the time
that he must depart from his origin and the amount of time he spends in the bottleneck.
Fosgerau and de Palma then investigate the impacts of a socially optimal toll. They prove that an optimal toll satisfies the following equation:
where t is the toll payment. An optimal toll would remove the queue, though the sequence of arrivals at the destination would remain constant. That is,
those closest to the bottleneck reach their destinations first. However, the overall arrival times at destinations are earlier under this toll system than
under laissez-faire. Thus, by removing the queue, commuters’ distance traveled to the bottleneck remains constant, but the time spent in the bottleneck has
been eliminated. The optimal toll allows commuters to pay for the extra time that they gain from arriving at their destination sooner.
Conclusions and Implications
Fosgerau and de Palma make three significant assertions in this study. First, congestion occurs only when travelers have similar scheduling preferences and have trip origins with similar distances from the bottleneck. Secondly, they contend that tolling improves social welfare by removing queues
completely. The toll, while a transfer from travelers to the state or toll administrator, results in earlier arrivals across the board. Finally, Fosgerau
and de Palma show that under optimal tolling, those travelers with the farthest trip origins from the bottleneck benefit from the tolling system, while
those closest lose. Intuitively, those closest cannot afford to depart later under an optimal toll than they would under a laissez-faire system. However,
they do reach their destination earlier than necessary under the toll, which diminishes their utility. Those located farthest from the bottleneck are the
last to arrive to the bottleneck every day, and consequently have the most to gain under an optimal toll. Whereas in a laissez-faire system, these
commuters would be subject to the accumulation of all of the congestion throughout the morning prior to their arrival at the bottleneck, an optimal toll
means that these commuters will not encounter any congestion upon their arrival at the bottleneck location.
In laymen’s terms, this study shows that socially optimal tolling systems bring about an increase in social welfare at the cost of a transfer from
travelers. Because there are winners and losers in this solution, it is a form of wealth redistribution and thus carries significant equity questions.
However, a key aspect of this kind of bottleneck congestion that this study does not analyze is the impact and use of the toll revenue. As is, those
travelers located closest to the bottleneck are the losers if the road is tolled; if a study investigated the uses of the toll revenue that would likely
create the most benefit for these same travelers because they are closest in proximity to the actual congestion site, they may not lose as much as Fosgerau
and de Palma suggest. In line with a Kaldor-Hicks calculation, the losers may be able to be compensated through additional public works or public goods
funded by the toll revenue, and the situation may transform into a Pareto efficient improvement from the laissez-faire model.
Another area for further research might factor in the distance (in time) between the bottleneck and the final destination. Fosgerau and de Palma do not
acknowledge the possibility that travelers may have significant and varying commutes after they go through the bottleneck. Intuitively, this additional
travel time would factor into each individual’s decision regarding what time to depart from their trip origin and what time they wish to arrive at the