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Which Children Are Most Affected by Mobility?

Common sense suggests that mobility is more likely to be a problem for children who move for negative reasons, such as family disruption or economic stress, than for those whose families are seeking better schools or employment. Families experience a broad range of difficulties that may result in residential or school instability. Researchers and policy makers have sought a greater understanding of which children are most negatively affected and the sequence of circumstances that lead to academic and other problems. Tracking mobility and its effects is difficult because it requires collecting accurate longitudinal data on families and children. Relatively few studies have provided such data, but workshop presenters described a variety of ways to examine the role that mobility plays in the lives of particular groups of children.

RESEARCH OVERVIEW

Arthur Reynolds provided a synthesis of the research on the effects of mobility on educational progress. He began with data from the National Assessment of Educational Progress (NAEP), shown in Figure 2-1, to illustrate the importance of the number of moves children make.

He also described two studies of students in Baltimore and Chicago, respectively, in which the researchers controlled for other risk factors in an effort to isolate the effects of mobility itself (Alexander, Entwisle, and Dauber, 1996; Temple and Reynolds, 1999). Both showed a reduction in achievement test scores of approximately one-tenth of a standard devia-



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2 Which Children Are Most Affected by Mobility? Common sense suggests that mobility is more likely to be a problem for children who move for negative reasons, such as family disruption or economic stress, than for those whose families are seeking better schools or employment. Families experience a broad range of difficulties that may result in residential or school instability. Researchers and policy makers have sought a greater understanding of which children are most nega- tively affected and the sequence of circumstances that lead to academic and other problems. Tracking mobility and its effects is difficult because it requires collecting accurate longitudinal data on families and children. Relatively few studies have provided such data, but workshop presenters described a variety of ways to examine the role that mobility plays in the lives of particular groups of children. RESEARCH OVERVIEW Arthur Reynolds provided a synthesis of the research on the effects of mobility on educational progress. He began with data from the National Assessment of Educational Progress (NAEP), shown in Figure 2-1, to illustrate the importance of the number of moves children make. He also described two studies of students in Baltimore and Chicago, respectively, in which the researchers controlled for other risk factors in an effort to isolate the effects of mobility itself (Alexander, Entwisle, and Dauber, 1996; Temple and Reynolds, 1999). Both showed a reduction in achievement test scores of approximately one-tenth of a standard devia - 

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0 STUDENT MOBILITY 100 90 Percentage of Sample 80 66 70 53 60 46 50 36 40 30 20 10 0 No Moves 1 Move 2 Moves 3+ Moves Number of Moves FIGURE 2-1 Mobility and fourth grade achievement at basic or above on the NAEP reading test, 2000. Fig 2-1 SOURCE: Reynolds, Chen, and Herbers (2009a). tion for each move a child makes, after other factors are accounted for. In other words, the effect of mobility is consistently negative and increases with the frequency of moves, although it is smaller than the effect of other factors, such as the family’s socioeconomic status or home environment. To explore the question further, Reynolds conducted a meta-analysis of research conducted since 1990 that examined the effects of school or residential mobility on achievement or dropout rates (Reynolds, Chen, and Herbers, 2009b). His goal was to consider impacts that are evident in the early school years as well as those that linger through high school, especially dropout rates. The 16 studies Reynolds identified measured nonstructural school moves across grades K-12. Each had measured premobility achieve- ment levels and also included a full set of control variables, and each provided measures of reading and mathematics achievement as well as school dropout. Nevertheless, the studies that met Reynolds’s criteria still varied in many ways, using different covariates and measures of achievement, for example, and investigating different sorts of moves, made at different points in children’s lives. Only five of the studies examined outcomes for students more than three years after their school

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 WHICH CHILDREN ARE MOST AFFECTED BY MOBILITY? moves, so longitudinal conclusions are limited. Five of the studies used national probability samples; Reynolds and his colleagues identified 9 of the 16 studies as methodologically strong (see Reynolds, Chen, and Herbers, 2009b, for methodological details). Compiling the data from all of the studies included in the meta- analysis, Reynolds and his colleagues found a significant relationship between mobility and both lower school achievement and dropping out. The data available on student achievement are stronger overall than the data for dropout rates, but the impact on dropout rates was the largest. The effects increase with number of moves—as shown in Figure 2-2, the effects are significantly more pronounced for students who make three or more moves. Looking at just the impact on dropout rates, Reynolds found that the effects of mobility varied somewhat with its timing: both early mobility and mobility during high school had the greatest impact. The studies varied in the magnitude of the impact they found. Because they differed in methodology, it was difficult for Reynolds to calculate a mean effect, but in some cases the increase in dropout rate associated with mobility was as large as 30 percent. 0.7 0.6 Impact on Dropout Rates 0.5 0.4 0.30 0.3 0.23 0.21 0.2 0.10 0.08 0.07 0.1 0 Low Reading Low Math Dropout Each move 3+ moves FIGURE 2-2 Effects of mobility on school achievement and dropping out (adjusted mean effect sizes, standard deviation units). SOURCE: Reynolds, Chen, and Herbers (2009a). Figure 2-2

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 STUDENT MOBILITY Reynolds made a few observations about this body of work. Ques- tions about mobility received increasing attention over the time frame he studied, and the overall quality of the studies increased. He noticed a certain fragmentation, however, with researchers identifying themselves with different fields, such as sociology, child development, or education; in many cases they reviewed only the existing literature in their own tradition. While the studies as a group supported the general finding of impact, Reynolds found that the precision of the measures varied considerably and that many possible differences among students (such as student and family characteristics) were not adequately examined. Other important questions deserve further exploration, he suggested, including threshold effects, long-term effects, and interactions among effects. To characterize the overall findings, Reynolds used a comparison with public health studies of the effects of smoking. Compared with the very strong conclusions researchers have drawn about the relationship between health and smoking, the mobility research is “middling,” he suggested. The number of studies is low, and although they are fairly con- sistent in finding effects and in the magnitude of the effects, the mecha- nisms are not fully described, and they do not provide a coherent picture of how mobility affects outcomes for children in the long term. However, several of the studies overcontrolled for differences between mobile and nonmobile groups, and Reynolds suggested that the findings are likely to be conservative—that is, that negative effects of mobility are actually more pronounced than the studies show. One participant observed that it might be possible to use the litera- ture on the predictors of dropping out—such as disruptive or aggressive behavior or problems with self-regulation early in development—as a starting point for researching the mechanisms that connect mobility to dropping out. Reynolds concurred, noting that other research suggests interactions among mobility, involvement with the juvenile justice system, and lower academic achievement, but these processes and relationships are not well understood. Another participant pursued the question of a cumulative effect of multiple moves, asking whether it could be the case that “kids who are destined to have frequent moves are also destined to experience a variety of other problems in their family situations or in their personal situations, and that these other changes [over time] are confounded with [the effects of multiple moves].” Reynolds agreed that although his review controlled for selected factors that are likely to confuse the results in this way, other unreported factors, such as mental health and problems in the home environment, may be present in a consistent way. He and his colleagues conducted some additional analysis, looking at measures of development

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 WHICH CHILDREN ARE MOST AFFECTED BY MOBILITY? and academic achievement prior to the first move, and concluded that there was still a likely effect of mobility. This issue is discussed in greater detail in Chapter 4. NATIONAL PICTURE Valerie Lee, David Burkam, and Julie Dwyer used national longitudi- nal data to search for patterns in mobility and its effects. They examined evidence from the Early Childhood Longitudinal Study—Kindergarten (ECLS-K) Cohort to explore the experiences of children in kindergarten through third grade (Burkam, Lee, and Dwyer, 2009).1 This data set, a project of the National Center for Education Statistics, includes data col - lected from parents, teachers, and school personnel on a nationally repre - sentative sample of the 1998-1999 kindergarten class. The children were also tested in reading and literacy skills and mathematics. Lee and Burkam, who presented this analysis, pointed out that there are important differences in school changes that take place between school years and those that take place during the academic year. The ECLS-K data allowed them to examine children’s status at four points—at the beginning and end of the kindergarten year, the end of first grade, and the end of third grade—so they could search for the impact of moves at some of the possible school change points.2 Given this array of information, the team explored four questions: 1. Who changes schools and who does not (national snapshot)? 2. What is the broad nature of school moves (during the school year, between school years, structural reasons, family reasons)? 3. What is the impact of changing schools on children’s reading and mathematics learning? 4. Is that cognitive impact conditioned by other characteristics of the child or family, such as gender, race/ethnicity, or social class? Lee and Burkam assumed that the impact of mobility would vary with the characteristics of children and families and with the reasons for the move, so they used a regression model to isolate different factors, including outcomes (reading and mathematics scores), type and num - ber of moves, and covariates (family characteristics, prior achievement, 1 Burkam, Lee, and Dwyer (2009) include more detailed analyses than those addressed in the workshop presentation. 2 The ECLS-K includes data on parent-assessed, teacher-assessed, and rater-assessed social and school behaviors, but Lee and Burkam did not find these data to be complete and did not include them in their analysis.

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 STUDENT MOBILITY academic status). Their analysis produced a portrait of mobility at the national level, beginning with the frequency of different sorts of moves, as shown in Table 2-1. The primary distinction they focused on was between structural moves, which occur when a child must change schools when the next higher grade is not available, and nonstructural moves, which occur for a wide variety of reasons. The data did not allow Lee and Burkam to distinguish types of non- structural moves, except by inference. However, they were able to look at how mobility rates vary by gender (little difference), race, and socio- economic status (SES). Black children had the highest mobility rates, with only 45 percent enrolled for third grade in the same school they attended during kindergarten, compared with 54 percent for Hispanic children and nearly 60 percent for white and Asian third graders. Children from low- SES homes were also more likely to move than their more affluent peers, especially during the first two years of schooling. In terms of the impact of mobility, the researchers found that children who change schools during kindergarten (though relatively few in num - ber) ended up behind their peers in literacy skills, even when their prior achievement levels are taken into account; this effect is strongest for low- SES children. While there is no overall negative impact for mathematics achievement, there is a negative effect for low-SES children that lingers TABLE 2-1 School Mobility at the National Level Frequency of School Change Percentage During kindergarten (n = 17,745) Remain in same school 93.0 Change schools (family reasons) 7.0 End of kindergarten to end of first grade (n = 14,943) Remain in same school 77.1 Change schools (structural reason) 5.2 Change schools (family reasons) 17.7 End of first grade to end of third grade (n = 11,975) Remain in same school 72.5 Change schools (structural reason) 3.1 Change schools (family reasons) 24.4 Beginning of kindergarten to end of third grade Remain in same school 55.7 Change schools once 35.9 Change schools twice 8.1 Change schools three times 0.3 SOURCES: Burkam, Lee, and Dwyer (2009); Lee, Burkam, and Dwyer (2009).

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 WHICH CHILDREN ARE MOST AFFECTED BY MOBILITY? at least through the primary grades. In addition, these children were at greater risk of being retained in grade—overall there is a small nega- tive effect on achievement for them. Moves made at any point between kindergarten and third grade similarly had greater impact for children receiving special education services, children whose first language is not English, and children from low-SES families. Looking at just the number of moves children made during this time period, Lee and Burkam found that while a single move had no impact, two or more moves were associ- ated with somewhat lower achievement in third grade—and again the effects were stronger for some children, such as those receiving special education services. Reflecting on what they had found, Lee observed that the effects of mobility seem to be small, but the available data do not yet provide a complete picture of school moves in the early primary grades. Longitudi - nal data, they suggest, provide the most useful tool, but do not currently allow a close look at different types of moves (e.g., structural or family reasons) or at noncognitive impacts. The problem is exacerbated because the ECLS-K data are often least complete for mobile students, in part because only a subsample of children who changed schools was followed in the data collection effort, and perhaps also in part because teachers have less information about those students. Moreover, teacher assessment data on mobile children are limited because of the difficulty of tracking these students. Yet the circumstances of the move and its noncognitive impacts, Lee and Burkam suggest, may be more important factors than background family characteristics in outcomes for children who move. What these data show, Burkam explained, is that the impact of school mobility appears benign when one looks at the overall effects for the entire population. A more complex picture emerges when one looks at conditional effects and the ways in which the impact is different for dif- ferent children. Participants concurred, noting that some data sets may lose children because of attrition, and that they may be most likely to lose, for example, children who make multiple moves during the first few years of school—such disproportionate attrition can make it difficult to draw accurate conclusions from the data. Moreover, others observed, longitudinal data collection efforts may miss information because they sample only at intervals (perhaps chosen with other goals in mind) that do not allow them to capture all student moves. Participants also pointed out that weighting procedures may allow researchers to compensate for some missing information, but that it is still difficult to capture the most disadvantaged populations.

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 STUDENT MOBILITY FOCUSING ON PLACE Data on housing stress and mobility in general suggest, as Chapter 1 discusses, that circumstances may vary significantly by region, county, or city or town. Local circumstances may influence the causes and the effects of mobility, and communities may respond to these stresses very differ- ently. Jane Hannaway, Lavan Dukes, and Amy Ellen Schwartz explored patterns of student mobility in three places: North Carolina, Florida, and New York City. North Carolina Student mobility rates are higher in North Carolina than in the nation as a whole (17 compared with 14 percent), Hannaway reported.3 She suggested two factors that offer at least a partial explanation: a dramatic increase in the immigrant population (274 percent between 1990 and 2000) and an increase in options for public schooling, such as charter schools and school choice programs.4 Using state administrative data that included information on free and reduced-price lunch status, ethnicity, sex, English language proficiency, special education status, and achieve - ment, Hannaway developed a picture of mobility among elementary and middle school students in the state and its effects on their academic achievement. She cautioned that she was unable to determine the percentage of the mobility that involved both a school and a residential move or to char- acterize the differences in the character of the neighborhoods students moved to and from. Moreover, it is likely that the data underestimate the number of moves because they do not capture all of those made during the school year. She had no information on reasons for moves, nor was she able to distinguish among those made during or between school years. The data offered only a once-per-year look at students’ moves. They cov- ered public school mobility within the state and did not include mobility out of the state or among private schools. However, the data cover large numbers of students over a long period (1997 to 2005) and made it pos- sible to control for student fixed effects (i.e., to conduct the analysis hold - ing certain variables, or attributes of the students, constant so that these 3 Hannaway based this comparison on census data that show a national average annual mobility rate of 13.9 percent in 2005 (and 11.9 percent in 2008; see http://www.census. gov/population/socdemo/migration/tab-a-1.xls) and a calculation using American Com - munity Survey data showing that the annual mobility rate in North Carolina has remained above 17 percent. 4 Hannaway credited colleagues Zeyu xu and Stephanie D’Souza, who collaborated with her in conducting this research.

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 WHICH CHILDREN ARE MOST AFFECTED BY MOBILITY? variations will not disguise any differences that may exist among students with different mobility rates). The data available included yearly cross-sections of all third through eighth graders in the state, as well as cohorts of third graders who were followed for six consecutive years. During the time covered by the study, overall enrollment increased by 15 percent, the percentage of the student population who were Hispanic tripled to reach 8 percent, and the percent- age of students who were English language learners doubled, reaching 4 percent. The percentage of students eligible for free and reduced-price lunch increased from 38 percent in 1999 to 47 percent in 2005. At the same time, North Carolina schools saw increased numbers of students changing schools (turnover rates). Overall, the turnover rate increased between 3 and 4 percent, but in urban schools it increased by 33 percent and in rural schools by 16 percent. Charter schools saw an increase of more than 30 percent, and, in general, the schools with greater percentages of minority students and students eligible for free and reduced-price lunch saw higher turnover rates. Mobility rates varied significantly for North Carolina students in dif - ferent subgroups, as shown in Table 2-2. Among all North Carolina students in grades 3 through 8 who had moved at all, between 36 and 38 percent had changed schools twice or more. At the same time, students whose parents had higher incomes and levels of education—and white students—were the most likely to move to a school of higher quality, as measured by test scores. TABLE 2-2 School Mobility in North Carolina, 1997-2005 Percentage Percentage Who Moved, Who Moved, Student Characteristics 1997 Cohort 2000 Cohort Eligible for FRPL 44 46 Not eligible for FRPL 25 24 Difference between FRPL and not-FRPL (gap increasing)   Black 46 50 White 28 29 Difference between white and black (gap increasing)   Hispanic 43 39 White 28 29 Difference between white and Hispanic (gap decreasing)  0 NOTES: Percentage of third grade students in North Carolina public schools who have ever made a nonpromotional move over a six-year period, by race/ethnicity, cohort, and FRPL eligibility. FRPL = free and reduced-price lunch. SOURCE: xu, Hannaway, and D’Souza (2009).

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 STUDENT MOBILITY What are the effects of North Carolina students’ mobility? The admin- istrative data allowed Hannaway to examine the end-of-grade assessment results in reading and mathematics for the students who moved twice or more. She also compared results for students who moved within a district (these moves are associated with more negative reasons, such as family disruption or job loss) with those who changed districts (cross-district moves associated with positive reasons, such as job opportunity). In general, the North Carolina data seem to reinforce the conclusions Lee and Burkam described about differing impacts for different groups of children. Hannaway reported that these data show fairly consistent nega - tive effects of moving within a district on mathematics test performance, but little effect for moving across districts. For reading there is little effect from moving within a district (and in some cases a benefit to moving across districts). (This finding differs from that of Lee and Burkam, who found a smaller effect for mathematics than for reading.) Hannaway sug- gested that mathematics learning may be more school-dependent than the development of reading. Hannaway and her colleagues also investigated the effects of the number of moves on math achievement (see Figure 2-3). She pointed out that a significant number of black students in North Carolina are making multiple school moves, and that once students make two or more moves, the negative effects on their mathematics test perfor- mance escalate sharply. Florida Florida is another very mobile state and a diverse one, Lavan Dukes reported. Looking just at Florida children in kindergarten through third grade in the 2007-2008 school year (about 900,000 children), the population is 43.3 percent white, 23.3 percent black, 26.2 percent Hispanic, 2.4 percent Asian, 0.2 percent American Indian, and 4.6 percent multiracial. Using administrative data, Dukes was able to capture information about all the school moves children enrolled in public schools in the 2007-2008 year had made since the start of kindergarten, regardless of the time of year of the move (he excluded structural moves).5 He was able to link this infor- mation to student background information and to scores on the Florida Comprehensive Assessment Test (FCAT). The data did not allow him to examine mobility outside the state. Mobility rates for these students vary by subgroup. The differences are apparent in kindergarten, as shown in Table 2-3. Not only have nearly half of all Florida students made at least one nonstructural move between 5 Dukes noted that the mobility of students who leave the state and return (during the time they are absent) is not captured.

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Number of Moves 1 2 3 4 5 Cross- 0.00 District Movers All Movers –0.05 –0.10 Within- District Movers –0.15 Cumulative Effect FRPL Eligible –0.20 Black –0.25 Hispanic –0.30 FIGURE 2-3 The effect of multiple moves on mathematics achievement in North Carolina. NOTE: FRPL = free and reduced-price lunch.  SOURCE: xu, Hannaway, and D’Souza (2009) Figure 2-3 Broadside

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 STUDENT MOBILITY than nonpoor students to move during the school year and to move multiple times. • here was little difference between rates of change for foreign-born T and native-born students. • ignificant numbers of children moved during the school year. The S rates range from a low of 3.2 percent for Asian third graders to a high of 7.9 percent for black fifth graders. And 56 percent of all first graders moved during that school year, with minority students most likely to move. Schwartz used a variety of regression models to calculate the effect of mobility on children’s performance in schools, using data from test- ing conducted in the third and fifth grades. Overall, she found that “for every move a kid makes across school years between first and third grade, their performance declines [in English language arts] by 0.08 of a standard deviation in the third grade.” For mathematics, the decline was 0.11 standard deviation. Taking the results up to fifth grade, she found that the detrimental effect continued in a “monotonic way.” That is, each additional move had a cumulative impact. Moreover, not only were black, Hispanic, and poor children more likely to move, the moves these children made had a greater negative impact on their academic progress. Schwartz acknowledged, in response to a participant’s comment, that it is difficult to discern whether the multiple moves were progressively harm - ful in themselves, or whether they are simply an indicator that students are experiencing high levels of adversity outside school. Schwartz and her colleagues, using test score data to assess the qual - ity of the schools to which students go when they move, found (looking just at third graders) that 66 percent of black children who moved went to a lower quality school and 33 percent to a better school. Conversely, 60 percent of white students who moved went to a better school. Another participant pointed out that high mobility rates within a school tend to foster more mobility, because “the higher the mobility in the school, the more likely it is that there will be a seat open at any given time for an incoming student.” Thus a district may be more likely to place an incom- ing mobile student who arrives in the middle of the year in a school with more frequent openings than in a stable school. For Schwartz, these results highlight the importance of using district policy both to minimize the number of structural moves children make (by, for example, structuring schools to cover grades K through 8) and also to direct academic supports to the groups most likely to move repeat- edly and to suffer for it academically. She also noted that housing policy (addressed in Chapter 6) offers opportunities to limit children’s school mobility. Her work focuses on the circumstances and needs of students in

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 WHICH CHILDREN ARE MOST AFFECTED BY MOBILITY? the New York City schools, and, because of its size, that school district is in some ways unique, Schwartz observed. Nevertheless, she said, “poor kids in New York look pretty much like poor kids in other cities.” PATTERNS OF RESIDENTIAL MOBILITY Another way to explore children’s mobility is through the family cir- cumstances that lead to frequent moves. Residential mobility may influ- ence families, children, and neighborhoods in many ways. Several partici- pants explored the ways residential mobility relates to school mobility and the groups who are at highest risk for disruption. Again, the presenters used data from particular places, but the focus of the conversation shifted from general demographic characteristics to analyses of selected groups, such as poor and homeless children, who are found in many places. Urban Homelessness Families who are homeless face clear challenges in providing continu- ity in their children’s education, and John Fantuzzo pointed out that there are many reasons why research on this group is difficult. The difficulty of sampling this highly mobile group and the risk families may perceive in reporting events, such as domestic violence, are just two of the research challenges. He described a unique partnership in Philadelphia that has created an integrated data set known as the Kids Integrated Data System (KIDS). This system was designed to support research that identifies risk and protective factors among cohorts of children from birth to age 21 using administrative data from multiple public agencies, including the School District of Philadelphia, the Department of Child Health and Welfare, and homeless shelters. The data housed in this system allow researchers to study the relationships among homelessness, school mobil- ity, and educational well-being in this large urban setting. Fantuzzo presented information about a third grade cohort for whom state proficiency data were available. The cohort included chil - dren who were born in Philadelphia, entered the school system, and remained in the county through the end of third grade—a group of about 12,000 children who were predominantly from minority and low-income families. To put this cohort into a national context, Fantuzzo noted that Philadelphia is the poorest of the 10 largest cities in the United States, with 24.5 percent of the households living in poverty. Among the cohort studied, however, the poverty rate was 70 percent—just under three times the rate for the municipality as a whole. Only 42 percent of this third grade cohort met state standards for reading proficiency and 59 percent met mathematics standards. During the third grade year,

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 STUDENT MOBILITY 50 48 .2 Percentage of Students 4 6.0 4 4 .5 40 4 3 .3 4 2.0 30 30 .8 3 0 .0 20 10 0 s NF o te ys NF k irl tin ac hi Bo G TA TA La W Bl No School Mobility FIGURE 2-4 Intradistrict school mobility* in Philadelphia. *Mobility was defined as at least one move any time between kindergarten and the end of third grade. NOTE: TANF = Temporary Assistance Figure 2-4 for Needy Families. SOURCE: Fantuzzo, Rouse, and LeBoeuf (2009). 34 percent of the cohort was classified by the district as truant and 1 in 10 children was suspended. Fantuzzo and his colleagues used KIDS data, including administra - tive records from multiple city agencies together with school outcome data, to study the prevalence and impact of publicly monitored risks. They found that the rate of homelessness among these children, at 9.2 percent, was three times higher than the national average for elementary- age students. Figure 2-4 shows the rates of intradistrict school mobility for this cohort, by gender, participation in the Temporary Assistance for Needy Families (TANF) Program (a federal program that provides sup- port to low-income families), and race. Mobility was defined as at least one move from kindergarten through third grade. Fantuzzo and his colleagues also used multiple regression models to discern the increased odds of students in this cohort having poor academic and behavioral outcomes as a result of being in one of the following cat - egories: only homeless, only school mobile, or both homeless and school mobile. They found that, compared with children who had experienced neither homelessness nor a school move, those who had experienced one or both had a significantly higher risk of poor academic and behavioral

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 WHICH CHILDREN ARE MOST AFFECTED BY MOBILITY? outcomes. Homelessness was associated with greater odds of poor aca- demic achievement and classroom engagement, whereas school mobility was associated with increased risk for truancy and suspension. Across all outcomes, the greatest impact was found for students who experienced both homelessness and school mobility. The next step, Fantuzzo explained, was to look in greater detail at the experiences of the mobile and homeless children in the cohort, particularly the co-occurrence of other publicly monitored risk factors. Table 2-5 shows the levels of risk for four categories among the third grade cohort (school mobile only, homeless only, both, or neither) compared with national rates. This table shows that homeless children, including those with and without school mobile experiences, were also most likely to experience every other risk in the analysis. Fantuzzo and his colleagues have explored questions about the timing of adverse events, such as becoming homeless, as well as patterns across schools and neighborhoods that could identify contributing factors and opportunities for interventions. They found, for example, that among the homeless children, 95 percent first experienced homelessness before they entered first grade. Homelessness also tends to be highly concentrated in particular neighborhoods and schools: the average across schools is 9 percent, but the range is from 0 to 32 percent. TABLE 2-5 Co-Occurrence of Mobility and Homelessness with Multiple Risks in Philadelphia Philadelphia Both Not Homeless Homeless Only Only and National or Mobile Mobile Homeless Mobile Inadequate 4 30 36 53 52 prenatal care Preterm/low 3 20 21 30 23 birth weight High lead 4 17 24 31 38 exposure Teen mother 12 21 28 31 31 Low maternal 12 24 27 36 38 education Child 1 7 11 34 37 maltreatment NOTE: Numbers in this table represent percentages of the third grade cohort. SOURCE: Fantuzzo, Rouse, and LeBoeuf (2009).

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 STUDENT MOBILITY Fantuzzo described several goals for data collection that could improve this portrait of mobility and homelessness, including analysis of more frequent data points to measure school mobility (e.g., between report card periods) and longitudinal analyses of students’ academic trajectories across the first through third grades. Partnerships among shel- ters and city agencies might expand opportunities to collect information about hard-to-reach children and families and their residential changes. Fantuzzo stressed that other cities (Cleveland, for example) are making similar efforts. Anne Masten also took a close look at homeless families, noting that, even among families facing significant disruption and possessing few resources and protections, there is “a striking variability of risk” in chil - dren. She presented data on risk factors among children ages 8 to 10 living in shelters, which show that the higher numbers of risk factors in a child’s life are associated with higher rates of behavior problems (see Obradovic et al., 2009). And, on average, homeless, highly mobile children perform less well on academic achievement tests than their peers. Yet, Masten pointed out, there is surprising variation even in these groups—with some children with multiple risk factors faring very well, and some highly mobile children scoring very high on achievement tests. Rural Poverty Fantuzzo focused on homelessness in an urban setting, but disadvan- taged children in rural or small-town settings may have somewhat differ- ent experiences. Kai Schafft synthesized a range of empirical studies and other work to shed light on residential mobility and student transiency in nonurban contexts (Schafft, 2005, 2006, 2009; Schafft and Prins, 2009; Schafft, Killeen, and Morrissey, 2010). His focus was on the community contexts in which transience occurs because it is his view that much of the scholarship on the issue is “analytically circumscribed by individual out - comes or the walls of the classroom or school.” His research often relies on qualitative or mixed-method approaches, owing to the difficulty of examining community characteristics using the available large data sets. Specifically, he has explored such questions as why people in rural set- tings move, when and where they move, what guides their decision mak- ing, and how schools and other community institutions support, or fail to support, mobile families and children. He used a profile of a woman who grew up in homelessness to highlight the interconnected nature of the problems mobile families face. Based on empirical data he collected on student transience in approxi- mately 300 upstate New York rural districts, Schafft found that mobility tends to be greatest in the poorest districts and that many families are

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 WHICH CHILDREN ARE MOST AFFECTED BY MOBILITY? highly mobile within a fairly small area. He used the term “rural mobil- ity sheds” to suggest a comparison with the way various environmental forces and local topographical features interact to affect the quantity and flow of water in the region surrounding a body of water. Similarly, he suggested, social and institutional features of communities may shape the flow of low-income movers. The key reasons families move are social and economic insecurity, exacerbated by the lack of safe, adequate, and affordable housing. Schafft observed a self-reinforcing cycle of poverty, residential mobility, and com- munity disadvantage, illustrated in Figure 2-5. This cycle tends to contain the mobility within communities with high unemployment, high per- centages of rental housing stock, and high poverty. Long-term economic decline is followed by out-migration, particularly of younger and more educated residents. Housing is devalued, and single-family homes are converted into multiple rental units. Low-income families remain, in cir- cumstances of increasing economic insecurity. Looking at a particular district in which this cycle had developed, Schafft described a student population of which 46 percent were eligible for free and reduced-price lunch, and in which an average of 1.6 middle school students per day entered or left their schools, for a turnover rate of 29 percent. Approximately half the moves took place within the district, and the median distance between students’ new and old schools was ↑Varying degree of out- migration of residents, Long-term especially younger and economic ↓ Population more educated decline in rural community Devalued housing Low-income Economic and ↑ Conversion of families remain housing insecurity single-family under circumstances leads to frequent homes into of increased short-distance multiple rental economic insecurity residential moves units FIGURE 2-5 The cycle of poverty, residential mobility, and community disadvantage. SOURCE: Schafft (2009). Figure 2-5

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 STUDENT MOBILITY 11 miles. In order to gain insight into why and where these families were moving, Schafft conducted interviews with 22 parents of children who had made unscheduled moves within the district and developed detailed histories of the five years that preceded the move for each family. He found that these 22 households had made 109 residential moves during the five-year period, 91 percent of which occurred in upstate New York. Thirty-one percent were within the same municipality, and 31 per- cent did not require a school change for the children. Most of the families lived in 3 to 8 different places, although some lived in as many as 10 to 13 places in that five-year period. Families reported moving because of a combination of social, economic, and housing problems, and in nearly every instance Schafft was able to identify a main precipitating reason or proximate factor for each move. The overwhelming majority of moves resulted from push factors, rather than pulls. That is, the families were impelled to leave their place of residence as opposed to seeking a better situation somewhere else. More- over, he noted, “while human capital theories of migration and mobility might lead us to believe that most mobility is primarily economically motivated,” just 3 of the 109 moves were made as a result of a job oppor- tunity. The residential push factors included eviction, condemnation of a property, and overcrowding. Schafft stressed not only that transience and chronic mobility are problems in rural as well as urban areas, but also that the consequences may be different in these settings. Improved understanding of the pat- terns that are common in urban and rural settings, for example, can help school administrators and others develop strategies to support mobile students. The administrator of the district Schafft had profiled used the maps of mobility patterns he had developed to plan ways to coordinate services with neighboring districts, because the maps made it clear that they were sharing many students back and forth. This sort of effort is particularly important, Schafft suggested, because transient students are not identified with migrant or homeless children and they are thus a large population of students at risk who are “flying under the radar.” Schafft closed with two points. First, although most research on stu - dent transience focuses on urban settings, the problem occurs widely across economically disadvantaged areas in both urban and rural areas. Student transience in rural areas has often been overlooked by both researchers and policy makers. Second, Schafft has found that transience is not simply an academic issue, but is closely linked to broader questions about family and community disadvantage. Thus he advocates multi- disciplinary and multimethod research, applied in the pursuit of ques- tions that look beyond the school, as the best analytic approach to the problems faced by highly mobile families.

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 WHICH CHILDREN ARE MOST AFFECTED BY MOBILITY? Neighborhood Contexts Robert Sampson also used the social context as a focus for under- standing student mobility, highlighting the urban neighborhood rather than the rural county. He described a collaborative study in Chicago called the Project on Human Development in Chicago Neighborhoods (PHDCN), which began in 1995.7 The study’s purpose was to investigate the influence of the structural and neighborhood context on children’s development through multiple research methods that included commu- nity surveys, video records, talks with neighborhood leaders, and local archives. At the same time, the researchers studied a cohort of children from birth, collecting data at three-year intervals. They used a stratified random sample of neighborhood clusters to make sure they captured the economic and social diversity of Chicago’s neighborhoods in the popula - tion they studied. Sampson and his colleagues hoped to explore the nature of mobility, what predicts it and influences it, and how it influences the environment in which it takes place. They found a great deal of mobility in Chicago neighborhoods, and its nature and impact varied significantly by group. Figure 2-6 shows the changes in median income for different groups of Chicago residents who moved within Chicago or out of the city or who stayed in place. The data show both whites and minorities becoming more prosperous as they move away from the city, although, as Sampson noted, much of the change is “in essence, leading to a new kind in inequality,” in that income gaps among the groups remain even as the overall income levels rise. In general, Sampson found, white residents and those who are more educated, wealthier, and in stable relationships, as well as those who own their homes, are more likely to move out of Chicago. As Schafft had found in rural New York, families in poverty in Chicago tend to move frequently but do not move far. Many Chicago neighborhoods are highly segregated, and white and Latino residents seem to be more influenced by moves in their own neighborhoods—particularly changes in the racial composition of the neighborhood—to make a move out of the city. At the same time, rates of both upward and downward mobility differ across population subgroups. DISCUSSION Looking across the data about student mobility in different places and circumstances, participants had various detailed questions about the col - 7 Details about the study can be found at http://www.icpsr.umich.edu/PHDCN/.

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0 75 White - stayers 70 White - moved w/in Chicago 65 White - moved outside Chicago 60 Black - stayers 55 Black - moved w/in Chicago 50 Black - moved outside Chicago Median Income 45 (in thousands of dollars) Latino - stayers 40 Latino - moved w/in Chicago 35 Latino - moved outside Chicago 30 1995 1997/1998 2002 Survey Wave FIGURE 2-6 Unadjusted trajectories of neighborhood median income among stayers, movers within the city, and movers out of Chicago, by race and ethnicity. SOURCE: Sampson et al. (2009). Reprinted with permission from Sampson and Sharkey (2008). Copyright 2008 by the Population Association of America. Figure 2-6

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 WHICH CHILDREN ARE MOST AFFECTED BY MOBILITY? lection and analysis of mobility data, and particularly about the interac - tions among the many factors that influence families’ circumstances and their decisions. Nevertheless, some clear patterns were apparent in the available evidence, as Stephen Raudenbush pointed out: • tudies of the average effect of mobility in the population of all S students suggest that mobility has a negligible effect. • nalyses of disadvantaged groups—particularly low-income A children and minorities—suggest that they move more often and appear to experience more negative consequences from moving than do other children. • he contexts in which children move seem to provide a useful pre- T dictor of whether or not the outcome will be detrimental, one that comports with common sense. That is, children in families who are economically stressed and downwardly mobile, whether in urban or rural settings, are at the greatest risk of both high rates of, and negative effects from mobility. • omeless children appear to be at particularly high risk, not only H because they are highly mobile but also because they have numerous other risk factors, such as family disruption, prolonged economic distress, and lack of social, community, and other resources. • ore negative effects may be associated with moves within a M school district than moves between districts, and with moves that take place during the school year, compared with those that occur between school years. • ultiple moves appear to be cumulatively detrimental, particu - M larly after a threshold of three to four moves or more. • vidence suggests that when schools experience high rates of E mobility, particularly during the school year, achievement levels diminish. Thus, high school–level mobility rates negatively affect the achievement of levels of nonmobile children. With those points in mind, the group’s attention turned to questions about the ways in which mobility harms children and what can be done to minimize the harm.

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