Rapid Response Research Projects

In the wake of the 2015 Freddie Gray-related Baltimore Uprising, many wondered if the systemic challenges that contributed to the event could be prevented in the future. Johns Hopkins University students affiliated with JHU Poverty and Inequality Research Lab conducted Rapid Response Research projects in the summer of 2015 to understand the many factors that led to events which followed Freddie Gray’s death. Topics for the Rapid Response Research projects ranged from mapping divestment to upward mobility opportunities. This research was supported by 21CC with seed funding from the Annie E. Casey Foundation. Project descriptions and updates are detailed below.

Examining the Geography of Opportunity

Project leads: Anna Rhodes and Allison Young

The April 2015 unrest in Baltimore following the death of Freddie Gray shone a national spotlight on the inequality present in Baltimore’s neighborhoods. The history of discrimination and segregation, and the actors and institutions affected, once again became the focus of many ongoing conversations. Arguably, one of the most important resources for families across the U.S., and in Baltimore, which has been deeply affected by this history is housing.

Recent research, released nearly a year after the national conversation turned its attention back to Baltimore, examines a housing voucher program that was established in 2003 as part of the legal remedy from the Thompson et al v. HUD fair housing court case. In his ruling, Judge Garbis held that the U.S. Department of Housing and Urban Development unfairly concentrated African-American public housing residents in poor and segregated areas of Baltimore City, a violation of the Fair Housing Act of 1968. Judge Garbis ruled that HUD must implement an effective strategy for promoting housing opportunities across the Baltimore region for African American residents of public housing.1

The Baltimore Housing Mobility Program (BHMP) was designed as a partial remedy to this fair housing violation. Through this program low income families move from many of Baltimore’s poorest and most racially segregated neighborhoods to more advantaged and diverse communities across the metropolitan region. The BHMP provides low income families with a housing subsidy, and extensive counseling and support, to assist them with residential moves to more affluent and more racially integrated neighborhoods. Through these neighborhood changes, low-income children moving with BHMP also gain access to dramatically different school contexts that are more racially and economically diverse, and higher-performing. In a recent report released by the Abell Foundation in March, the study team examined the effect of new school contexts on children’s academic achievement after receiving a BHMP voucher.

Researchers found that after moving with the program, children attended more racially and economically diverse schools that were significantly higher performing. Students did face an initial adjustment period in their new school, reflected in a dip in students’ math scores in the first year after moving with the BHMP. However, their scores rebounded by their second year, and students showed steady learning gains over time as they remained in the program. Within five years of voucher receipt students participating in this housing program began performing significantly better on the state standardized assessment tests than they would have in the absence of the program. Although test scores are but one measure of student academic success, the improvements in students’ scores likely reflect additional positive gains in other academic, social, and non-cognitive domains, which support school achievement.

To more broadly examine how the change in school and neighborhood contexts experienced by families moving with the BHMP affected both students and parents, a qualitative in-depth interview study was designed to complement the quantitative analysis of children’s academic outcomes. Through interviews with parents and youth, we examine the processes through which moving with the BHMP affects low income families. For example, what is it about the new neighborhood and school that leads to the positive test score changes identified in the Abell Foundation report?

This qualitative work began in 2012 when the study team conducted in-depth interviews with 110 families. Although these interviews begin to tell the narrative of how parents and children navigate neighborhood and school changes after moving with the BHMP, capturing their experiences at a single point in time cannot fully illuminate the numerous ways in which this program affects families. To further an understanding of how parents and youth are impacted by the BHMP, we must capture their experiences over time.

In the summer of 2015, three years after our initial interviews, the study team conducted 83 follow-up interviews, with the assistance of two 21CC interns, Mollie Cueva-Dabkoski and Olivia Long. Through interviews with 42 parents, and 41 youth who moved with the BHMP while they were in elementary school or high school, our team began the process of understanding change over time. Youth participants were asked to describe their schools, classmates, and teachers. The interns also asked youth to tell us how they navigate their academic and social life at school, the activities they participate in, the classes they take, and how this has changed over time. We asked about their friends and how they form new friendships after changing schools. Adult interviews also asked parents to describe their neighborhoods and their children’s schools. Parents were asked about their interactions with the school and their children’s teachers, and their opinions about the support and programming available to students and families. The team also talked to parents about their neighborhoods, the amenities available to families, and how their children engage with their new neighbors. The parents were asked to compare their experiences in the suburbs with their former city neighborhoods and how things have changed for their family as they spend more time in new neighborhoods.

The interviews will continue in the summer of 2016 as the team heads out again to conduct additional follow-up interviews with parents, and youth who moved with the BHMP during middle school. Again we will focus on their experiences over the past four years in school and in their neighborhoods, understanding how their lives have changed as they engaged with new schools, classmates, teachers, and neighbors after moving with the BHMP.

This has been generously supported by the Abell Foundation, the National Science Foundation, and the Spencer Foundation.

1See http://www.naacpldf.org/case-issue/thompson-v-hud for more information.
Figures 1-3 show examples of the types of neighborhoods in the Baltimore metropolitan area families move to with the BHMP (note: for confidentiality reasons, photos are purely exemplary and do not contain homes of program participants).

Divestment & Reinvestment in East Baltimore: A Project of the Johns Hopkins Poverty and Inequality Research Lab

Project leads: Phil Garboden and Christine Jang

A Project of the Johns Hopkins Poverty and Inequality Research Lab. Funded by the Annie E. Casey Foundation and the 21st Century Cities Initiative.
Photo: The Reinvestment Fund’s Renovations on Preston Street

Mapping Neighborhood Reinvestment

While the scattered incidents of civil unrest that accompanied the Baltimore Uprising made for dramatic media images, it was the background, not the foreground, of those photographs that truly told the story. The Sandtown neighborhood is a landscape of concentrated abandonment; its blocks upon blocks of vacant and abandoned housing have become a ubiquitous reminder of the national failure to support urban neighborhoods and Baltimore’s legacy of discriminatory housing policy. These forces combined to lock African American communities across the city into a decades-long cycle of divestment and abandonment.

For much of the last two decades, urban decline was taken as given. Nothing, it was thought, could be done to stem the exodus of middle-income families from the urban core. But in the past few decades, American cities have seen an unexpected reversal of fortune. Instead of collapsing, as was widely predicted, many former industrial cities are increasing in population for the first time since World War II. Almost overnight, the policy discussion shifted from large-scale demolition, “right-sizing,” “moth-balling,” and metropolitan annexation to neighborhood revitalization projects with the potential to reverse decades of neglect.

But this trend generated concerns as well. If market forces are driving neighborhood revitalization, how can policy be structured to ensure that legacy residents – those who could not or would not leave the neighborhood – benefit from the renewal?  Will the revitalization process follow the well-documented pathways of gentrification and displacement, or is a third way possible – a virtuous cycle of renewal that recreates diverse mixed-income neighborhoods?

In 2015, researchers from the Poverty and Inequality Research Lab set out to address these issues with an in-depth case study of East Baltimore. The researchers focused on three questions: 1) What does the process of neighborhood revitalization look like? 2) What factors drive supply-side actors (landlords and developers) to invest in a neighborhood? 3) What effect are the changes having on community demographics – who’s moving in, who’s staying, and who’s leaving?

This document presents some initial findings from a small portion of this research, focusing on the spatial distribution of investment.

Guide to the Maps

Over the summer of 2015, our research team conducted a systematic social observation of each block and parcel in five East Baltimore neighborhoods (Oliver, Broadway East, Milton-Montford, Biddle Street, and Gay Street), noting signs of disorder and property abandonment. We observed 370 blocks and 8,189 parcels, creating a rich database of the housing landscape in the area.

Map 1: East Baltimore Study Area

Our study focused on the five neighborhoods immediately adjacent to the East Baltimore Development Inc.

Map 2: Patterns of Abandonment and Reinvestment

At the moment, East Baltimore is a tale of two neighborhoods. In Broadway East, the legacy of divestment is very much evident in the hundreds of abandoned properties. By contrast, Oliver is showing unprecedented levels of reinvestment and property rehabilitation.

Map 3: Trash On Street Heat Map

As new residents move into Oliver, there is noticeably less trash in the street. New residents mean more people invested in the neighborhood, and neighborhood cleanups are frequent in the area.

Map 4: Street Quality Heat Map

Private reinvestment is often supported by public infrastructure improvements. Streets in Oliver are in noticeably better condition than in Broadway East.

Map 5: Cigarettes On Street Heat Map

Because they are hard to clean up and correlated with population, there is much less difference in the number of cigarette butts in the gutters than in trash overall.

These maps are just a few examples of our work in East Baltimore, which includes interviews with developers, new residents, legacy residents, and public officials. This work would not have been possible without our three 21CC Summer Interns: Mollie Cueva-Dabkoski, Brianna Bueltmann, and Ben Schwartz.

To learn more, please email Philip ME Garboden at pgarbod1@jhu.edu or Christine Jang at cjang4@jhu.edu

Sampling the Extreme Poor: Notes from a Systematic and Venue-Based Study

Project leads: Robert Francis and Elizabeth Talbert

The central hypothesis of this study is that a growing number of American families have incomes so low that the difficulties of their living situations are masked by studies that treat the poor as a homogeneous group. A recent study by Edin and Shaefer (2013) suggests that about one-fifth of all poor households with children—and about one-fifth of all poor children—live at the extreme income threshold of only two dollars of cash income per person per day. They refer to this income level as “extreme poverty.” For the purposes of this study, we define extreme poverty as a cash income of no more than 25% of the federal poverty line, which is currently $20,090 for a family of three. Table 1 provides additional detail about measurements of poverty and extreme poverty.

Table 1. Measures of Poverty and Extreme Poverty by Household Size

The growth in extreme poverty is the result of several factors, including changes to the Temporary Assistance for Needy Families (TANF) program as part of welfare reform in the 1990s (Edin and Shaefer 2015). The virtual demise of TANF, combined with the expansion of the Earned Income Tax Credit (EITC) and other policy changes, has ushered in an era of an employment-based safety net. As economist Robert Moffitt (2015) has demonstrated, while social welfare expenditures have increased in recent years, the proportion of that spending going to those below 50 percent of the poverty line has decreased. This project set out to demonstrate that a systematic sampling of this population in extreme poverty was possible. Being able to find and talk with these families is necessary to understand the diverse dynamics of poverty in the United States.

Methodology

This project was conducted in two field sites in Maryland: Baltimore City and Somerset County. These sites were chosen to provide one urban and one rural site with high poverty rates. The research team consisted of two supervising professors (Drs. Andrew Cherlin and Kathryn Edin), two doctoral students (Talbert and Francis), and six research assistants, four of whom were undergraduates (Lauren Abrahams, Marni Epstein, Eliza Schultz, Juliana Wittmann) and two of whom were recent graduates (Kaitlin Edin-Nelson and Geena St. Andrew).

The goal of the study was to test two different sampling methods—random sampling and venue-based sampling—in hopes of reaching the target population: black, white, and Hispanic families with children between the ages of 3 and 15 who have cash incomes of less than 25 percent of the federal poverty line. For the random sample, we identified one majority black, one majority white, and one plurality Hispanic high-poverty Census block group in the urban site, and one majority black and one majority white high-poverty Census block group in the rural site. (There were no Census block groups in the urban site with a majority of Latinos and no rural block group with a plurality of Latinos.) We purchased a list of random addresses in these block groups and then randomly subsampled from these lists. In total, the subsample consisted of 403 addresses: 300 from the three urban block groups (100 each) and 103 from the rural block groups (52 from one, 51 from the other). The team visited all 403 addresses, seeking to screen each household for study eligibility. Tables 2 and 3 list selected demographics for the urban and rural sites.

Table 2: Urban Sites and Characteristics, Random Sample

Table 3: Rural Sites and Characteristics, Random Sample

For the venue-based method of sampling, we identified venues that served the Census block groups chosen for the random sample. Eventually, we broadened our search for venues, particularly in the urban site, to include venues outside of the initial Census block groups. This was done once we realized that the Census block groups chosen for the random sample had limited venues that would work for the venue-based sample. In some venues, we did time interval screening, in which researchers screened every person to visit the venue in a given time interval. In other cases, the venue-based screening was more fluid and involved talking to people as there was opportunity. In a few cases, researchers at a venue made an announcement about the study to those present and invited those who thought they fit the criteria to speak with the researchers further.

The screening questionnaire used for both the random sample and the venue-based sample consisted of eight questions designed to determine eligibility for the study. The first two questions established if the person was the head of the household and whether or not children in the desired age range (3-15) were present. If the answer was negative to either of these questions, the researcher told the person the household was not eligible and thanked him or her for their time. If the person was the head of the household (or an adult in the household with knowledge of the household’s situation) and kids of the specified age were present, the researcher proceeded with six additional questions designed to be a proxy for extreme poverty. Questions asked about recent homelessness, health problems, the receipt of food assistance, if the household had trouble making ends meet, and so on.

Researchers began the study by requiring three “preferred” answers to the hardship questions to be eligible for the study, but we quickly realized that this was too selective and changed the protocol to just one “preferred” response. Thus, a household screened into the interview portion of the study if there was an adult in the household who could speak to the situation there; there were any children between ages 3 and 15; and the respondent indicated in any way that the household was struggling financially. Respondents who participated in the in-depth interview were given $25 in cash or a gift card. To read the full report click here.

Results and Conclusions

The random sample of 403 addresses yielded 38 interviews, 26 from the urban site and 12 from the rural site. The venue-based recruitment added another 5 interviews from the urban site and one interview from the rural site, although it turned out that one of those 5 interviews from the urban site was with someone without qualifying children. In total, this project—between the two different sampling methods—resulted in in-depth interviews with 44 people who the research team believed might qualify as extremely poor. However, upon completing the interviews, only 3 of the interviewed families had cash incomes below 25 percent of the poverty line. The team discusses implications of these findings in the conclusions section found in the corresponding PDF.

Table 4 shows the yield rates from the random sample. It was possible to approach 389 of the 403 randomly selected addresses. Of the 14 addresses not approached, some did not physically exist; some were university-owned student housing; and some were unable to be approached by the researchers. Of the 389 addresses, there were 75 where the researchers were unable to make any contact with someone at the residence, thus the eligibility of these households for the study is unknown. People at another 18 addresses refused participation, so the screener could not be administered. This left a total of 296 addresses where contact was made by the research team and the screening questionnaire was administered. Of these 296 addresses, 257 of them, or 86.8%, were ineligible for the study, mostly because the household did not contain children in the desired age range (3-15). Based on this alone, it is clear that the yield rate even for households potentially eligible was quite low. Indeed, the 38 interviews conducted represents just 9.8% of the total addresses (403), or just 12.9% of the addresses where contact was made and the screening questionnaire was administered (296).

Table 4: Interview Yields from the Five Sites, Random Sampling Only

* Less than 100 because 3 doors were not knocked.
** 52 addresses were selected, but 7 addresses were student housing and another 3 addresses that did not physically exist, hence we knocked on 42 doors.
*** 51 addresses were selected, but 1 addresses did not physically exist, hence the possibility of knocking on 51 doors.

The results are somewhat more varied when each neighborhood is examined individually.  Urban neighborhood #1, for example, had a contact rate of 78%, an interview yield rate for all addresses of 14%, and an interview yield rate for those addresses where the screener was administered of 17.9%. Similarly, rural neighborhood #1 had a contact rate of 95%, an interview yield rate for all addresses of 21.4%, and an interview yield rate for those addresses where the screener was administered of 22.5%. Notably, these two neighborhoods are the two African American neighborhoods in the sample. The contact and yield rates for the white and Hispanic neighborhoods were lower and much lower in the case of the urban Hispanic neighborhood. Table 4 also shows that we were able to identify a total of three families in extreme poverty, which is a yield that is next to zero (0.8%) as a percentage of the overall sample. However, while this is low as a percentage of the overall addresses, these three extremely poor families represent 7.9% of the 38 households interviewed.

Several conclusions were drawn from this pilot study. First, finding the extreme poor, even in areas of concentrated poverty, is challenging. This could be the case for several reasons. Even though the survey data indicates the population of the extreme poor is quite large in the aggregate, this population is still a small minority of the poor and a much smaller percentage of the overall population. Second, researchers recommend a life-history approach when interviewing the extreme poor. Based upon the interviews conducted for this pilot, the team encountered a number of individuals who, while not extremely poor at the moment of the interview, had experienced extreme poverty in their lifetimes. This finding underscores the fact that extreme poverty can be episodic for some families and highlights the value of using what has been called “narrative interviewing,” in which interviewers are trained to put a premium on eliciting narratives from the respondent (see DeLuca, Clampet-Lundquist, and Edin 2016).

Third, venue-based sampling holds promise for finding these families in the future. Although venue-based sampling was not fully tested in this pilot, our limited experience with this method, combined with the challenges encountered using the random sample, leads us to conclude that a venue-based approach is preferred. However, such sampling should be as systematic as possible, and venues should be varied to try and capture different types of extremely poor families. Venue-based sampling is probably the most cost- and time-effective way of sampling the population of Americans living in extreme poverty. Finally, we suspect that the presence of TANF likely affects the prevalence of extreme poverty, and this effect is differential across geography. While TANF does not even lift a family above 50% of the federal poverty line, it does lift them out of extreme poverty. Thus, the presence of TANF, even if much smaller than in past decades, indeed makes a dent in extreme poverty. Figure 1 shows the percent of families receiving TANF in Baltimore by neighborhood.