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Student Mobility Exploring the Impacts of Frequent Moves on Achievement: Summary of a Workshop (2010) / Chapter Skim
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3 Methodological Issues
Pages 33-40

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From page 33...
... ISOLATING THE EFFECTS OF MOBILITY ON INDIVIDUALS AND SCHOOLS Hanushek began with the point that had emerged so clearly from the data already discussed -- that although national residential mobility rates may have declined, they are consistently highest among low-income families. The challenge is to distinguish between positive moves, made in search of better schools and neighborhoods, and moves that are made because of some sort of disruption and that cause harm.
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From page 34...
... To do this, they compared student achievement among, for example, fourth graders in consecutive years, and used the differences in mobility rates "to see whether mobility shows up in differences in achievement, other things being equal." They found that "higher student mobility in a school during the school year really hurts everybody, and it hurts people in a fairly dramatic way." Hanushek emphasized that for people in high-mobility schools these effects persist throughout their school careers. Moreover, African Ameri can students in the Texas sample had much higher mobility rates than other children, and they also tended to go to schools with much higher mobility.
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From page 35...
... But, by isolating individual fixed effects, they were able to show that while the effects of mobility on individuals are fairly small, the effect on schools is quite large. SEEKING CAUSALITY Jens Ludwig described the methodological challenges associated with estimates of the effects of residential mobility on outcomes for children.
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From page 36...
... "The answer that LaLonde got," Ludwig explained, was that there were big differences between the two sets of results, a finding that "was shockingly grim and has had a profound influence on the field of empirical economics and applied statistics." 2 This approach was initially developed for research on employment, but it has been used to examine education questions as well. Although the results of nonexperimental estimates vary in practice, depending on the context and the quality of the available data, Ludwig has found that, on average, they do not look particularly impressive.
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From page 37...
... To develop a nonexperimental estimate for comparison with the empirical results of the randomized experiments, researchers can use dif ferent methods, such as standard regression analysis and its close cousin, propensity score matching, in which certain variables are held constant and others are varied with the goal of isolating a particular effect. The ability of these sorts of nonexperimental or observational approaches to reproduce the experimental answer probably depends on the qual ity of the data that are available, so it is important that a fairly rich set of background characteristics are available for the families and children in the MTO study, including demographics (e.g., age, household size)
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From page 38...
... Another suggested that, given the practical and ethical difficulties of randomly controlled studies, the solution is to seek "overwhelming data of the 3 Nonexperimental research designs that might be used when randomized controlled stud ies are not feasible include more qualitative approaches, such as case studies or ethnographic surveys; longitudinal studies; correlational studies; and statistical analyses, such as regres sion discontinuity, use of instrumental variables, or propensity score matching.
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From page 39...
... For his part, Ludwig challenged researchers and policy makers not to take shortcuts on causal questions. Strong design considerations -- either randomized experiments, regression discontinuity studies in which the selection process is fully known, or natural experi ments in which there is a clear instrumental variable -- are needed to support causal interpretations.
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Key Terms

  • natural experiments
  • mobility rates
  • methodological issues
  • nonexperimental results
  • school quality

  • jens ludwig
  • nonexperimental methods
  • individual students
  • residential mobility
  • policy intervention

  • propensity score
  • causal questions
  • ludwig explained
  • randomized experiments
  • nonexperimental estimates

  • housing vouchers
  • experimental results
  • estimated results
  • ludwig suggested
  • score matching

  • gray bars
  • bad situation
  • various outcomes
  • observational approaches
  • using nonexperimental

  • ticular interventions
  • constant school
  • compared student
  • empirical evidence
  • including demographics


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