Mapping students’ photos and qualitative observations
The block that I studied was similar to the blocks that other people in my census tract observed, in that our census tract, while nice in some parts, is mostly run-down and has considerable crime, poverty, and unemployment. We all observed a large proportion of African Americans, and a lack of children on the streets. Our census tract appears to be comprised of lower middle-class people and has a mixture of small/neglected homes and nicer, well-cared for neighborhoods. However, it seems that the areas that my peers studied were more dangerous–they mentioned graffiti, damaged streets, exposed electrical wiring, a fallen sign, barred windows, loud music, and blaring police sirens. I either didn’t notice these or there wasn’t enough evidence for it to make a significant impact in my observations. Overall, however, their reports seem to reflect my impression of my block and the surrounding areas.
By reading through individual observations for each of the census tracts, it became clear to me that there is a large difference between the northern and southern parts of the #18 AC Transit Bus route. The northern census tracts tended to be safe, well-maintained, quiet, and clean. There was generally no litter on the ground, no graffiti, and newly paved streets. Many of the houses were having renovations or people were gardening to maintain their lawns, which indicates leisure time. Many kids were playing outside and socializing, and in several of the tracts, there were community boards or parks where people were talking that indicated that the community was tight-knit. Additionally, several students observed that the people in these areas were friendly, polite and willing to help, as well as overwhelmingly white. There were not many politically-charged messages or signs, not many signs of liberal views. None of the reports mentioned grating or bars over windows, and people seemed comfortable with the security in the neighborhood. Many of the areas reportedly had trees and other vegetation lining the streets. All of these characteristics appear to coincide with high populations of white people, high incomes, low rates of poverty and unemployment, and high rates of education (likely a bachelor’s degree or higher).
In stark contrast to this, the southern census tracts exhibited very different characteristics. Many of the communities featured littered streets, graffiti, rowdy adults/teenagers, politically-charged messages, run-down homes and streets, and unappealing neighborhoods overall. Many of these areas had some sort of crime in varying degrees, and due to this, many homes/buildings had bars on the windows and signs advertising security protection. Additionally, there were not many people walking around on the streets, and if there were they were adults, suggesting that people are not comfortable being on the streets alone due to safety concerns. Also, there were several reports of abandoned or old cars. These characteristics seem to be present in communities with high percentages of blacks, hispanics, poor people, people with lower levels of education, and unemployed people. This is the case because many of these people do not have the resources (due to lack of education, employment, and racial barriers) to move out of poor/run-down neighborhoods.
After reading other students’ descriptions about their census tracts, I now have a comprehensive understanding of the various neighborhoods on the #18 bus route that span the distance from Albany to Lake Merritt. If one were to start at the northernmost point of the bus route and move south, he/she would at first observe beautiful, well-kept, suburban neighborhoods that seem appropriate places to raise a family. The streets and sidewalks are well-maintained and clean, and the neighbors are interacting with each other, while children play outside. There isn’t any sign of crime, loitering, or graffiti, and the region is fairly quiet. The cars are relatively new. Moving south, through downtown Berkeley, the environment changes a little, so that now the streets are a little more crowded, there is more traffic, and there is a mix of people who appear to be both comfortable and healthy, as well as homeless or poor people. There are more students walking around, since the observer is close to the UC Berkeley campus. There is more activity, but there is also litter on the ground: cups, wrappers, and other garbage. There are unsanitary corners on the sidewalk where the homeless typically camp out for the night, and there are smells of urine and other unclean things. Moving yet further south, the observer encounters more residential areas, but the neighborhoods look quite different from those described in Albany further north. Now the neighborhoods are dilapidated and old, and there is graffiti dotting many buildings. Most windows are barred and the few people who are walking around are shoddily-dressed, poor, suspicious-looking adults. There are no children. The area is quiet, but still there is a sense of unrest in the air. At the very end of the bus route, the observer ends up in Oakland, where the social climate is far different from that which he/she started out seeing.
The Social Disorder and Amenities Scale
The reason we measured social disorder and amenities is because these two factors relate to a number of other characteristics about neighborhoods. According to Sampson and Raudenbush, disorder is the result of structural factors such as concentrated poverty and lack of social resources, as well as residential instability. Knowing the level of disorder will provide insight into what level of poverty is present in a census tract, which in turn will provide a general understanding factors like income, education, and demographic breakdown. It also suggests the level of crime and the number of immigrants in a community, since both of these characteristics have an effect on the amount of disorder within a community.
Amenities provide similar information, but in the reverse direction. It is useful to study amenities because they give a picture of how privileged people living in a particular census tract are, and their overall quality of living.
Additionally, knowing the amount of both disorder and amenities is a way to determine the neighborhood effects on children growing up in that community. This is true because, according to Sharkey and Faber, the influences of a residential environment accumulate over time and have a lasting impact on residents’ lives and trajectories. Moreover, continuously living in a neighborhood is what propagates neighborhood inequality. Thus, measuring the levels of social disorder and amenities can suggest the life trajectories for community residents.
I feel that the items that were included in the two scales for disorder and amenities were mostly comprehensive and painted a mostly complete picture of the physical disorder and amenities within a census tract. However, by adding some additional attributes to measure, we can gain a clearer understanding of the disorder/amenities of a community.
In addition to all the aspects that we already studied, I would add a few other things to measure, such as the number of needles and syringes (something that was mentioned in the Sampson and Raudenbush study,) the number of people on the street, the age of the residents, and the generally visible racial makeup of the residents. High social disorder would be present if we observed large numbers of needles/syringes, a low number of people on the street, only adults walking around, and the presence of blacks and latinos. Additionally, it would be useful to observe the number of homeless people on a certain block and any visible or obvious evidence of urine/feces on sidewalks, as the presence of these is also a sign of high social disorder. On the other hand, lots of greenery, well-maintained gardens, children playing outside, and people walking their dogs/other pets are all signs of amenities.
According to the scale, the three census tracts with the highest level of social disorder are 4014, 4005, and 4031, and those with the lowest level are 4204, 4213, and 4214. On the other hand, the tracts with the highest level of amenities are 4030, 4204, and 4229, and those with the lowest are 4014, 4007, and 4206. It is interesting that census tract 4014 ranked in the top three for having both the highest social disorder and the lowest amenities.
Some socioeconomic characteristics that usually accompany social disorder are lower income, less education, higher unemployment, more families under the poverty line, more families with public assistance income, more black and latino people, more foreign-born residents, more single-mother families, and younger adults that don’t have families. Conversely, regions with amenities usually also host more white people, more people with higher education (bachelor’s degrees and above), more older people with children/families, lower unemployment rates, and higher median house values.
Census Data and Maps
According to William Julius Wilson in his paper Being Poor, Black, and American, certain characteristics like being black, living in the inner city, being poor, a high unemployment rate, and high rate of imprisonment are all factors that are closely related. The introduction of new industries in the field of technology has led to a decline in domestic manufacturing and instead an increase in the demand for human capital. The exportation of manufacturing jobs to other companies has resulted in joblessness for low-skilled workers, many of whom are poor, live in the inner-city, and are black. Due to geographical segregation, it is hard for these workers to travel to places with work, which are usually the suburbs, so they become siphoned off from work, and get caught in a cycle of poverty. For example, Wilson states a statistic that “in 2008, black males age 18 and older accounted for 5 percent of the college population, but 36 percent of the prison population,” which demonstrates the prevalence of racial inequality and bias in the legal system. Furthermore, other characteristics that tie in with the traits listed earlier are high levels of concentrated poverty, poor physical conditions, the lack of basic services and amenities, substandard schools, and the lack of basic facilities like banks, grocery stores, parks, and public transit.
According to Elizabeth Kneebone and Natalie Holmes, poor neighborhoods usually come with a host of problems like high crime rates, poor physical and mental health outcomes, high dropout rates, and weak job-seeking networks. In addition, concentrated poverty is the highest among blacks and hispanics.
The census data does match up with my predictions. The data shows that certain sets of characteristics “go together” in different census tracts. For example, if social disorder is high, that means that other factors like low education, low median income, the percentage of families under the poverty line, and single-mother families, among much more will also be present. On the other hand, if amenities are high, the opposite of those factors just listed will also be high. In general, being white seems to be a strong determinant of other factors. For example, in census tract #4213, where the population of white people is 78.4%, the statistics are the following: unemployment (4.1%), household median income ($135,556), bachelor’s degree or higher (81.2%), families with income under the poverty line (0.7%), families with public assistance income (0.4%), median house value ($836,000), percent black (3.2%), percent hispanic/latino (3.3%), social disorder (0.05), and amenities (0.31). From looking at this data, we can see that the unemployment, number of poor families and families with public assistance income, percent black, percent hispanic/latino, and social disorder are extremely low, while all the other, more “positive” characteristics, are very high. All in all, the census data seems to simply confirm the predictions I made in the last question.
In question 3 I traveled down the #18 bus route explaining the visual characteristics of the surrounding regions, but now I can describe the tracts in terms of census data as well. Starting at the northernmost point of the bus route, you find yourself in an area that has a high population of white residents, almost all of whom have bachelor’s degrees or higher, well-paying jobs, settled families, expensive houses, not many community problems, and a high number of amenities. There are very few poor families, single-mother families, and minorities. Moving further south, you encounter a mixture of well-settled, white families and lower-income, minority families. As you near the college campus, there is increased diversity in the kind of people you see walking around on the streets. Finally, as you approach the last stops of the bus route, you start to observe more minorities walking around on the streets, particularly blacks and latinos, evidence of poverty, homelessness, and people who have low incomes, low education, and low house values. Overall, the community has high social disorder and low amenities.
Histograms, Correlations, and Regressions
The two variables I chose to discuss are “Household Median Income” and “Unemployment %.” By looking at the histogram for household median income, I noticed that there are only a few census tracts (about 6) that have median incomes higher than $80,000. Moreover, the highest cluster of incomes is in the lowest income region on the histogram, which is $20,000-$40,000. This income is below the poverty line for households in the San Francisco Bay area, which means that the majority of households in the Berkeley and Oakland areas are getting by with relatively meager incomes. Additionally, the highest value the x-axis goes to for income is $140,000, which falls in the upper middle class range for the bay area, but still does not fall into the category of upper class. This means that the range of incomes indicates that the tracts being studied only range from poor to upper middle class.
In terms of unemployment, there is a wide range of levels across the different census tracts. The most common percentage of unemployment is about 9%, which is much higher than the average unemployment rate throughout America (which is only about 4.4% as of August 2017). This aligns with my observation in the previous paragraph about household median income being quite low among these census tracts. It seems that there is a similarly low level of unemployment. To add onto this, three of the communities have 16% unemployment, which is alarmingly high. However, there are more communities that have lower numbers of unemployment than those that have higher numbers, so while the mode of the histogram is 9%, the mean tends to be a little lower, at around 6%, which is more reasonable in light of national standards.
When two variables are positively correlated, it means that as one variable increases, the other variable increases as well. The higher the R-squared-coefficient, the higher the slope of the relationship is. In other words, the more the two increase with regards to each other. When two variables are negatively correlated, in means that as one variable increases, the other decreases, and their R-squared-coefficient is correspondingly negative.
The first set of variables I investigated was the percent of white people in a community and household median income. I found that the R squared value is 0.69, which indicates a strong positive correlation. This means that as the percentage of white people increases, the median income tends to increase as well. In other words, white people tend to have higher incomes. The next set of variables are the percent of families with income under the poverty line and social disorder. The R squared value here was 0.35, which indicates a moderately strong positive correlation, suggesting that people who live under the poverty line also tend to experience more social disorder in their neighborhoods. Finally, the last set of variables I explored was the percentage of people with Bachelor’s degrees or higher and the percentage of single mother families in a community. I found that there is a moderately strong negative correlation with an R squared value of -0.30, indicating that as the percentage of people with bachelor’s degrees or higher increases in a community, the percentage of single mother families decreases. This shows that people with higher education tend not to end up as single mothers later in life, or vice versa–that single mothers tend not to have higher education.
The correlation matrix is a very useful tool for exploring the relationship between any two variables, and especially to be able to check where every census tract lies in the distribution of each variable. Some observations I made was that there tends to be a positive correlation between the following set of variables: unemployment, percent of families with income below the poverty line, percent of families with public assistance income, % black, % single mother family, and social disorder. Another set of variables that also seem to have positive correlations with each other are: household median income, percentage of people with bachelor’s degree or higher, median house value, and % white. Moreover, there appears to be a negative correlation between any set of variables from both sets. For example, the correlation between unemployment and median house value is negative.
However, there are also variables that don’t seem to fall into either category, such as % asian, % hispanic/latino, % foreign born, and amenities. Percent asian seems to have strong positive and negative correlations with several other variables, regardless of what category they fall into, and the other three variables simply have very weak correlations (close to 0) with most other variables.
As part of the fieldwork portion of the neighborhood project, I studied a region between Oakland and Berkeley labeled census tract #4007, and recorded my observations about it. After the entire class conducted their observations, we combined our results to form a comprehensive summary of information about each census tract along the #18 bus route. In this essay I will outline the characteristics of the area I studied and what kind of effects it would have on a child growing up there, as well as additional information I would need to support my prediction.
I believe that a child growing up in my census tract would have lower life chances due to a number of factors. My overall impression of the area was negative, due to visual information I gathered from my time there, that was later backed up by other students’ data. According to the census data from the Jupyter notebook, the majority of people in the community are not highly educated. A meager 40.9% of people have a bachelor’s degree, meaning that most people are likely not working in highly-skilled labor and did not pursue an education for very long. This may lead children to become comfortable with the idea of not receiving an education. Additionally, the level of unemployment is high, at an alarming 10.5%, meaning many people do not have jobs and are simply loitering in the streets. This could result in increased drug trafficking and gang activity, both of which are very dangerous for impressionable children. Furthermore, there is a high population of single mothers (40.0%). Thus, the child in question could be growing up in a single-mother household, and if the mother is working long hours to support her family, as is usually the case, she will not have time to spend talking to her kids, providing moral support, or helping out with their homework. In the long run, this is not good for a child’s development, both social and cognitive. In addition, the level of social disorder is high (~0.3), which means that children will be exposed early on to violence, crime, drug trafficking, gang activity, prostitution, and public drinking. To make matters worse, there are few amenities as well, meaning that there are not many areas for recreation or community support to help limit negative influences.
To top it all off, the area is segregated (as it has a black population of 40.3% and a latino population of 13%), making it an undesirable place for white people to live and thus propagating existing residential inequality. Due to a high minority population that has a low median income, there will be little tax money to go towards creating amenities for the community, and according to William Julius Wilson in Being Poor, Black, and American, the area will likely only have lower-paying jobs in the service and retail sectors, since most industries exist in suburbs, where the white population is higher. This dearth of jobs and lack of amenities will hurt parents who are trying to earn a living to support themselves and their children, which in turn will create an unstable financial environment for kids. In addition, according to Sampson and Raudenbush in Disorder in Urban Neighborhoods: Does it Lead to Crime?, the percentage of minorities in a community is related to the extent of gentrification that occurs. According to the paper, there is a negative association between blacks and latinos and gentrification, and once the population of blacks hits 40%, gentrification begins to slow. The implications of this are that since my census tract has a large population of blacks and latinos, there is little scope for gentrification, meaning that children growing up under bad conditions and influences will likely have to remain in their situation, resulting in a host of negative influences.
In order to confirm my prediction that my census tract will have negative influences on children, I would like to gather additional information such as police records and information from community interviews. Gathering information from police records about homicides, robberies, and burglaries is useful because it will provide insight into the level of crime in the community, which will negatively influence the upbringing of a child. In addition to police records, conducting community surveys and interviews would be helpful in evaluating neighborhood cohesion, “collective efficacy,” and informal social control, as described by Sampson and Raudenbush. Neighborhood cohesion is essentially how tight-knit a community is. “Collective efficacy” is a link of trust and a mutual understanding of situations in which community members should intervene. Informal social control mechanisms describe the process by which community members maintain social order and safety while limiting crime and disorder. It would be useful to conduct interviews to measure these three characteristics, since SSO data cannot capture enough information through visual cues. It would be valuable to be able to assess the levels of each of these, since high levels of all result in greater community support and thus a healthier social upbringing for children. Additionally, high levels of collective efficacy mean that adults in the community can work together to prevent disorder, even if the law or other external forces cannot.
All in all, I believe that a child growing up in census tract #4007 would not have very high life chances, and he or she would be exposed to a host of negative influences early on in life. In order to be fully sure of this conclusion, I would have to gather additional data from police records and community surveys.
Sampson and Stephen W. Raudenbush discuss disorder and crime in their paper Disorder in Urban Neighborhoods–Does it Lead to Crime?, and Jackelyn Hwang and Robert J. Sampson discuss gentrification and racial inequality in their paper Divergent Pathways of Gentrification: Racial Inequality and the Social Order of Renewal in Chicago Neighborhoods. They both make use of SSO (Systematic Social Observation) data, but for different purposes: Sampson and Raudenbush use it to measure disorder, while Hwang and Sampson use it in their study of gentrification.
The main argument that Sampson and Raudenbush seek to answer is whether or not manifestations of social and physical disorder lead to more serious crime. Most similar studies use subjective perceptions of social disorder from residents, but this study was different because it only used visual cues, independent of individual opinions. In order to keep their data objective, they used SSO data, and simply observed the “natural social phenomena,” or events and their consequences as they occurred in the neighborhood. They drove through the streets and videotaped their surroundings to measure both physical and social disorder. Physical disorder can manifest itself through “garbage, litter, graffiti, abandoned cars, needles and syringes.” Social disorder is evident through “loitering, public consumption of alcohol, public intoxication, presumed drug sales, and the presence of groups of young people manifesting signs of gang membership” (Sampson and Raudenbush). In order to obtain additional information about the neighborhoods, the researchers conducted interviews to obtain more information about cohesion and collective efficacy. From their observations, they reached a number of conclusions. One thing they found was that crime and disorder both stem from the same thing: concentrated poverty. This was different from the popular perception that crime is a product of social disorder, a theory which has been propagated over time and which has influenced the “zero tolerance” policy of the New York police department, as they crack down on the most minor of offenses. They also found that disorder and homicide are not related, and that the only crime that is directly a result of social disorder is robbery. Furthermore, they found that high collective efficacy usually correlates to low violence and low levels of disorder, and that the only way to lessen disorder is to increase collective efficacy.
On the other hand, Hwang and Sampson focused their research on gentrification and racial inequality. Their main research question pertained to how neighborhoods gentrify, and how this relates to continued racial and class inequality in neighborhoods. The researchers used Google Street View to collect SSO data on gentrification, which was a very novel and creative method for observing neighborhoods. Through their study, they found that residential selection and which neighborhoods gentrify is strongly determined by racial factors. They also explained that gentrification is negatively associated with populations of blacks and latinos, and that once the black population in a neighborhood reaches 40%, the process of gentrification slows. Additionally, they observed that neighborhoods next to gentrified or high-income neighborhoods can feel the side-effects of nearby gentrification. The study concluded that in places with higher proportions of minorities, which also usually have higher perceptions of disorder, there is a neighborhood stigma that stems gentrification. This slowed gentrification leads to the reproduction of neighborhood racial inequality, which answers the original question of how gentrification ties into the reproduction of racial inequality in neighborhoods.
Sampson and Raudenbush use their SSO data to measure the level of disorder in the various neighborhoods. The data they find is also used to determine the relationship between disorder and crime, as well as the link between disorder and poverty. Their method has its limitations. For one thing, the SSO data, by definition, is strictly surface level. The idea of SSO data itself is that all the information collected is based on visual cues. This means that the only data they gathered was from the video they took while driving. Therefore, the researches did not have the chance to get a deeper perspective into the neighborhood dynamics because they could not see what people are doing behind closed doors. For example, in the privacy of their homes, people could be doing dangerous drugs that they do not want to take out onto the streets, engaging in prostitution or human trafficking, engaging in underage drinking, and engaging in domestic abuse. All of these things are signs of social disorder, but none of them, or many other factors, would show up in a simple video recording of a street. Moreover, by taking the video while driving, much information could be missed, and the car could easily pass up something noteworthy that could have been documented if it were going a little slower. This introduces a great deal of variation and uncertainty into the data collected; the most foolproof way to collect accurate data would be for the researchers to not only investigate more under-the-surface features of the community, by conducting interviews, but also to take a video on foot or spend a certain specified amount of time on each portion of the block. These measures would ensure that the data collected is comprehensive.
In the meantime, Hwang and Sampson use their SSO data to measure gentrification. They do so by “integrating census data, police records, prior street-level observations, community surveys, proximity to amenities, and city budget data on capital investments.” (Hwang and Sampson). One way in which the researchers could improve their method is if they were to measure the inflow or outflow of white people in a community over the course of a few years to determine the actual rate of gentrification. This is particularly helpful, since white people are a good indicator of where gentrification is occurring, since they can afford (both for financial and social reasons) to move to places with a higher quality of living.
In conclusion, Sampson and Raudenbush and Hwang and Sampson make use of SSO data in their observations to come to separate conclusions about different topics (disorder and gentrification), but they both face issues in their methods that should be remedied in order to reach more sound conclusions.
Neighborhoods make a significant difference in the lives the people living in them. The sights, experiences, and other influences all around make a lasting difference on not only the personality and behavior of the residents, but also their long-term outcomes. Therefore, the question of whether neighborhoods are important is irrelevant: we can already recognize that neighborhoods have a monumental impact on people. This viewpoint is reflected in Patrick Sharkey and Jacob W. Faber’s paper Where, When, Why, and For Whom Do Residential Contexts Matter? Moving Away from the Dichotomous Understanding of Neighborhood Effects. These two researchers further argue that a more relevant question to ask is “where, when, why, and for whom do residential contexts matter?” Their goal is to move away from a dichotomous understanding of neighborhoods, and instead investigate these specific questions to gain deeper and more complex insight into neighborhood effects.
Sharkey and Faber argue that future research on how neighborhoods influence outcomes should focus on a few specific issues. One of these issues is the importance of timing in residential contexts, and another is the heterogeneity of experiences across different groups of youth. Sharkey and Faber argue that the duration of exposure to good and bad environments over time has a profound effect on youth. The longer a child spends in one community, the more that community will come to bear upon that child’s development in multiple ways. For example, a child who spends his or her entire childhood in the same place will become absorbed into that neighborhood’s institutions, academics, and peers, such that the child’s very development and academic achievement will be influenced. However, the impact of the neighborhood is not the same for all children–depending on the duration of stay and other factors, the community has a different impact on all its residents.Due to this, our perception of the neighborhood changes from a static entity to a dynamic being that plays a different role in every person’s life.
Another topic that Sharkey and Faber mentioned there should be further research on was the heterogeneity of experiences in residential contexts. In other words, we should investigate how different groups of people respond to neighborhood effects. The researchers propose that we look at how responses vary based on individual susceptibility, genetic background, and social cognitive responses to the environment. Overall, it would be valuable to observe what effect unique individual circumstances related to family background and personal characteristics have on individuals in combination with neighborhood effects.
If I had the resources to conduct a study, I would research how individual outcomes influence residential effects; basically, I would look into heterogeneous responses to neighborhood settings. In order to formulate conclusions, I would need to collect data that allows me to go deeper than simple surface-level observations. Thus, I would need to use some SSO data along with a great deal of other data collected from community surveys and/or interviews of a few thousand people in the neighborhood. During these interviews, I would ask a number of questions related to the individual’s family, such as if he or she knows of any family illness, where the family has lived over the last few generations, where the family is ethnically from, the racial makeup of the family members, and how many generations of the family have lived in the U.S. Asking about family health is important, since medical problems can take a major toll on a person’s life, and this can affect how a child grows up. Additionally, knowing details about family background would help me categorize patterns in growing up using similar characteristics, like race, ethnicity, time spent in the U.S., etc. In addition, I would ask about less personal aspects of this person’s family’s life: whether they are experiencing problems of domestic abuse, drug addictions, problems with the law, and if the family is currently experiencing any legal, financial or relationship problems. This kind of information is good for determining what kind of trauma a child may be going through, since exposure to abuse and drugs can radically change (and damage) a kid’s childhood. Finally, I think it would be valuable to ask a few questions about cultural practices within the family and the parents’ health. These are two particularly important factors. For example, it is worth looking into whether parents spend a great deal of time with their children helping them learn and cognitively develop, or whether they simply leave their children out of the picture and let them fend for themselves. Secondly, the parents’ health (both emotional and physical) can have either a positive or negative impact on children. If one parent is overburdened with work and stress, he or she will likely not have much time for children, whereas a parent who takes good care of themselves will be of good use to their children.
Alongside information gathered through interviews, I still think it would be useful to gather some SSO data. Some characteristics it would be useful to capture would be the kind of houses in a neighborhood and their specific traits: i.e. the size, the external appearance, the size of the front and back yards, etc., as well as the number, age, and type of cars that people have, and other various physical attributes, such as the extent to which the yard or garden is taken care of. This data, in combination with more specific, family-related data would help me create sound conclusions about how different individuals experience the same neighborhood over time.
In closing, it is important to note the topics Sharkey and Faber stressed that future research on how neighborhoods influence outcomes should focus on as areas that need to be addressed in order to develop a complete understanding of neighborhoods and their effects. Until then, we have a good understanding of the fact that neighborhoods matter, and of where, when, why, and for whom they matter.